材料专业外文翻译
材料成型及控制工程专业毕业设计(论文)外文翻译
中文2500字本科毕业设计翻译学生姓名：*****班级：*****班学号：*****学院：材料科学与工程学院专业：材料成型及控制工程指导教师：***** 副教授2011年3月25日Section 4 – Die Design and Construction Guidelines for HSS Dies General Guidelines for Die Design and ConstructionDraw DiesHigher than normal binder pressure and press tonnage is necessary with H.S.S. in order to maintain process control and to minimize buckles on the binder. Dies must be designed for proper press type and size. In some cases, a double action press or hydraulic press cushion may be required toachieve the necessary binder forces and control. Air cushions or nitrogen cylinders may not provide the required force for setting of draw beads or maintaining binder closure if H.S.S. is of higher strength or thickness.Draw beads for H.S.S. should not extend around corners of the draw die. This will result in locking out the metal flow and cause splitting in corners of stamping. D raw beads should “run out” at the tangent of the corner radius to minimize metal compression in corners, as shown in figure 16 on page 47. Better grades of die material may be necessary depending on the characteristics of the HSS, the severity of the part geometry, and the production volume. A draw die surface treatment, such as chrome plating, may be recommended for outer panel applications.Form and Flange DiesPart setup in form and flange dies must allow for proper overbend on all flanges for springback compensation. Springback allowance must be increased as material strength increases; 3 degrees for mild steels, but 6 degrees or morefor HSS.Punch radii must be fairly sharp. 1t for lower strength steels. Higher strength steels may require larger radii, but keeping them as small as practical will reduce springback in the sidewalls.Flange steel die clearance must be held to no more than one metal thickness clearance to reduce springback and sidewall curl.Form and flange steels should be keyed or pocketed in the casting to avoid flexing.Flange steels should be designed to wrap over and coin the flange break in order to set the break and reduce springback. See figure 17 on page 48.Die strength must not be compromised with light-weight die construction. High strength steel will require a stiffer die to resist flexing and the resultant part distortions, especially for channel or “hat-section” parts. This type of part can also cause serious die damage if double blanks occur.Cutting DiesTo reduce press tonnage requirements and extend die life, a minimum shear of four to six times metal thickness in twelve inches of trim steel length is recommended.To reduce die maintenance, maximum trim angles should be about 5° to 10°less than those used for mild steel. Trim steels should be keyed or pocketed in casting to avoid flexing. Die clearance should be 7 to 10% of metal thickness.Drawbead TypesConventional Drawbeads Run-out Drawbeads For H.S.S.Lock Beads for Stretch-Form DieFigure 161. Providing a vertical step in the flange stiffens and straightens the flange, stopping sidewall curl as well as springback.2. The addition of stiffening darts helps maintain a 90-degree flange.3. By adding a horizontal step along the flange, the flange is stiffened, resulting in reduced springback.4. Back relief on the upper flange steel allows for extra pressure to be applied futher out on the formed radius.Section 5 – Die Tryout Guidelines for High Strength Steel DiesGeneral Guidelines for Die TryoutDraw DiesHigher draw die binder pressure and press tonnage will be necessary in order to maintain process control and draw parts without buckles. A double action press or a press with hydraulic cushion may be required in some cases to achieve the required binder forces.HSS draw die operations will require sheet steel lubricants that are formulated for extreme pressures. Mill oils will not provide sufficient lubricity for most applications. Pre-lubes or dry film lubricants may be necessary for process control.Die plan view punchline corner radii should be larger than with mild steels to avoid buckling in the corners of the binder.Stretch Form DiesLock beads may require modification to avoid cracking or tearing with higher strength grades of HSS. Opening side walls of beads and enlarging corner radii will avoid cracking of high strength sheet steel. Lock beads should be continuous around the punchline for stretch form dies.For large panels from stretch-form dies, such as a roof panel or hood outer, elastic recovery may result in a shrunken panel that does not fit well on the male die member of the trim or flange dies. This problem is corrected by adding a “plus” factor to the overall part dimensions of the draw die or stretc hform die punch. This “plus” is usually no more than 2.5 mm at the center of the sides and the front, tapering to 0.0mm at the corners of the part profile on the punch. Finish part profile is defined, and plus is removed, in the main flange die.Form and Flange DiesThe punch radius should be fairly sharp with 1 or 2t used for lower strength steel. HSS may require larger radii, but as small as practical to reduce springback of sidewalls.The flange steel radius affects sidewall curl and springback on any offset flanges. This radius should also be small to reduce springback of side flanges. Overbend for springback compensation must be increased as tensile strength increases: 3 degrees is standard for mild steels, but 6 degrees or more will be required for HSS.Flange steel die clearance should be tight, maintaining no more than one metal thickness clearance to reduce springback and sidewall curl.Cutting DiesTo reduce press tonnage requirements and extend die life, a minimum shear of four to six times metal thickness in twelve inches of trim steel length is required.Die clearance should be 7 to 10% of metal thickness for HSS.To reduce trim steel maintenance, reduce maximum trim angles by about 5° to 10° from those used for mild steel. Trim steels should be keyed or pocketed in the casting to avoid flexing.Die Tryout When Using Bake Hardenable SteelIn order to obtain the maximum benefits of BHS, tryout of the dies should be performed as follows: Circle grid analysis must be performed on a panel before any die rework is attempted. With the gridded panel as a reference, the die can be modified to provide a minimum biaxial stretch of 2.0%. Stretch-form or draw dies are best for this material.For rough or functional tryout, it is possible to use mild steel with a 6% to 8% gauge increase to perform the normal process of die preparation. This alleviates complications when the BHS strengthens between each die being tried out. The reason for this is the time lag that normally occurs between a panel being formed and its use in the next operation.When the entire line of dies is ready for approval, all dies must be set in line. Panels should be run through all the die operations consecutively. This will avoid some of the strengthening effects of time delays between stamping operations that can cause variation in panels. Dimensional approval of the panel will be most difficult if this procedure is not followed.The strengthening reaction in the BHS can cause dimensional variation in flanges since springback will vary with time as the strength increases. This is why running the panel through all die operations consecutively is crucial to a successful buyoff.Part BuyoffTo reduce the part buyoff time and eliminate many hours of tryout time, the benefits of functional build must be considered. This procedure has beenproven to save time and money by concentrating on an acceptable sub-assembly rather than making each stamping to part specifications. Those parts that are easiest to change are revised to suit the sub-assembly dimensional targets. Those parts that do not affect the sub-assembly quality are not changed, but the detail part specifications are revised. The functional build process will eliminate excessive tryout hours if used for part buyoff on HSS stampings.In addition to saving tryout time and die rework costs with functional build, lower part variation can also be realized. Two dimensional challenges faced by the die maker when first trying out dies are to reduce the dimensional variation from nominal specifications, and to reduce the short term variationfrom part to part. The typical priority is to first minimize part-to-part variation and later address nominal deviation. A strong argument for this strategy is that the deviation from nominal is not precisely known until a dimensionally consistent part can be evaluated. The results are a dimensionally consistent part even though a number of checkpoints may deviate from nominal, and perhaps even be out of tolerance. In many situations when dimensions on the die are reworked to shift them closer to nominal, they become less stable and result in higher part-to-part variation. The functional build philosophy evaluates the acceptability of the part after it becomes stable, and before minor dimensional shifts are made. Large deviant or critical dimensions may be identified for rework even with functional build. There are dimensions that can often be spared rework based on a functional build approach. In these cases, the part remains more stable and the die more robust because less rework occurs while attempting to shift dimensions.For more information on functional build, refer to the Auto/Steel Partnership publication. “Event-Based Functional Build: An Integrated Approach to Body Development”.第四节-高强度钢模具设计和制造指南对模具设计和制造的一般准则拉深模具为了控制高强度钢的成形并减少板料边缘的弯曲，高强度钢成型时的压力和吨位高于一般情况是必要的。
材料专业英语词汇
材料专业英语词汇材料专业英语词汇是材料工程专业学习的重要组成部分，掌握好材料专业英语词汇对于提高专业素养和学术水平具有重要意义。
下面将介绍一些常用的材料专业英语词汇，希望能够帮助大家更好地学习和掌握这些词汇。
首先，我们来看一些关于材料的基本词汇。
材料在英语中通常是指“material”，而材料的性能则可以用“property”来表示。
例如，强度可以用“strength”来表示，硬度可以用“hardness”来表示，韧性可以用“toughness”来表示，导热性可以用“thermal conductivity”来表示，导电性可以用“electrical conductivity”来表示，透明度可以用“transparency”来表示，等等。
其次，我们来看一些关于材料加工的词汇。
加工在英语中可以用“processing”来表示，而材料的成型可以用“forming”来表示，材料的切削可以用“cutting”来表示，材料的焊接可以用“welding”来表示，材料的涂覆可以用“coating”来表示，材料的热处理可以用“heat treatment”来表示，等等。
再次，我们来看一些关于材料测试的词汇。
测试在英语中可以用“testing”来表示，而材料的力学性能测试可以用“mechanical property testing”来表示，材料的化学成分分析可以用“chemical composition analysis”来表示，材料的表面形貌观察可以用“surface morphology observation”来表示，材料的断裂形貌分析可以用“fracture surface analysis”来表示，等等。
最后，我们来看一些关于材料应用的词汇。
应用在英语中可以用“application”来表示，而材料的应用领域可以用“application field”来表示，材料的设计可以用“material design”来表示，材料的选型可以用“material selection”来表示，材料的改性可以用“material modification”来表示，等等。
材料科学专业英语正文课文翻译
材料科学专业英语正文课文翻译
材料科学是一门研究物质的性质和组成以及它们在不同条件下的行为的学科。
它涵盖了从原子和分子到大型结构的各种材料，包括金属、陶瓷、高分子材料和半导体等。
材料科学的发展为各个领域的技术和应用提供了基础和支持。
在材料科学中，有许多不同的性质和特征需要被研究和理解。
这些包括材料的力学性能、热性能、电性能、光学性能以及化学性能等。
通过对这些性能的探究，学者们可以确定材料的适用范围、使用条件和潜在的改进方向。
材料科学的研究还涉及到材料的制备和处理方法。
这些方法包括从原材料中提取纯净物质、合成新材料以及对已有材料进行改性等。
研究人员通过不断改进这些方法，可以制备出更加优良和具有特殊功能的材料，以满足各种需求。
材料科学的应用广泛存在于各个领域中。
在汽车工业中，材料科学帮助开发更轻量化、更强度的材料，提高汽车的燃油效率和安全性能。
在能源领域，材料科学有助于研究和开发更高效的太阳能
电池和电池材料。
在医疗领域，材料科学帮助设计和开发可生物降解的医用材料，用于组织工程和医疗器械等。
总而言之，材料科学在各个方面都起着重要的作用。
通过对材料的研究和理解，我们能够不断改进现有的材料，开发出更加先进和功能性的材料，推动科技的发展和社会的进步。
材料制备机械技术论文中英文对照资料外文翻译文献
中英文对照资料外文翻译文献New Trends and Problems in Material Processing MachineDesign TheoryAbstract :Based on reviewing the historical background, prospecting for the development trend ,analyzing the complicacy and mechanism and summing up some achievements and experiences in scientific research , several new problems and the possible direction of development in material processing technology and machine are proposed ,such as ,producing new concept materials possessing some specific and extraovdinary properties by means of integrating and coalescing conelative frontier science and technology ;and thereafter a brief discussion is given.Keywords: interface; extraordinary physical field; procession machine ; function material1The Time Background of Material Processing Machine In the long span of history of human progress ,many tools ,machinesand methods were created and a variety of materials with different properties were processed. Materials and its processing have become one of pillar and driving force of mankind progress. In pace with multi - polar competition in current world and people’s striving perseveringly for happier life ,material function goes beyond unceasingly men’s knowledge and imagination ,for example ,cryptic function material ,semiconductor material ,energy material ,vibration - absorptive material , super - strength aluminum alloy accounting for 70 percent of application of aeronautics and space ,metal foil of 4～5μm ,deep drawing plate with anisotropy below 1 percent ,electronic aluminum foil with micro - orientation up to 95 percent ,heat resisting aluminum alloy with super strong specific strength used in aeronautics ,space and deep sea ,etc. Thus several important development trends with distinct time features in material processing domain are shaped up as follows :(1) Creating material processing machine with extraordinary physical field for processing material with special texture structures and functions. For example ,applications of thermal energy and mechanical energy are breaking through unceasingly technology limit ,and some non - tradition energy ,such as microwave ,chemical energy , bioenergy , etc. , are introduced into material processing procedure one after another ,so that some material processing machines with extraordinary energy circumstance are produced.(2) Breaking through traditional physical limits and integrating melting ,solidifying ,plastic deformation and heat treatment to obtain special function of material and cut down expenses[3 ] . For example ,near - net shaping material processing technology , such as fast rolling , spraying deposition ,over - plastic molding , injection molding , high energy beam ,etc ,is applied.(3) Material processing process is forged ahead in the direction of high speed ,heavy - duty and high accuracy online control ,for instance , the rolling speed goes up to 130 m·s - 1 ,the deformation pressure rises up to 300 MPa ,accuracy of dimension up to 0.1μm ,accuracy of shape up to 0. 1 I ,strength accuracy comes up to 0.1 MPa. For these reasons ,it is necessary for material processing machine design theory to integrate and coalesce ingeniously correlative frontier science and technology to create and produce some new concept material processing machine with following functions.2Due Functions of New Concept Material Processing Machine(1) To have the ability to produce and bear extraordinary physical field and transmit extraordinary energy flow with the aim of providing extraordinary physical circumstances necessary for new concept material processing. For example ,high gradient temperature field with the speed of cool - down of work interface which exceeds 104～106 K·s - 1 , line wave and pulse complex exerted in solidifying - deforming area , super - strength contact stress field of material forming interface ,turbulent flow field of molten metal with very big flakiness ratio ,low frequency magnetic field with random frequency ,microwave field for powder metal heating ,ultrasonic field for large volume solidifying ,etc. [4 ] ,are applied.(2) To have the ability to work in critical state so that high stability and ideal performance of processing machine is ensured under the circumstance of reinforced technological condition and multi - field coupling operation. For example ,chatter suppressing capability of fast ultra - thin rolling under the condition of boundary lubricating state[5 ] ,the capability of self - excited vibration suppressing under the condition of special friction state ,synergism stability and disturbance stability of flexible connecting parallel shaft with multi - driving system ,etc. [4 ] ,are ensured.(3) To have the ability to accurately control the material processing in order to obtain low loss ,high efficiency and high quality of material processing. For example , super - high accuracy on - line monitor of products form ,on –line monitor and on - line adjustment of products texture and properties ,precision coordination control of multi - procedure , on - line monitor of micro - orientation of metal plastic deformation ,etc. [4 ] ,are ensured. Some products accuracy index may be enumerated as follows : dimensional accuracy coming to 0.1μm , microstructure uniformity to crystal lattice ,strength error to 0.1 MPa ,etc.[4 ]In short ,only by new concept material processing machine with extraordinary function being designed and made , can special function material be processed.3Science Problems and Study Contents of Metal Material Processing MachineUnder the Circumstance of Extraordinary Physical FieldIn view of these facts and background mentioned above ,several new research topics can be advanced as follows.3. 1 Coupling Heat Transfer Mechanism of Multi - Phase InterfaceTemperature - Stress FieldA brand - new microstructure can be obtained through continuously large deformation and fast solidifying when melting metal is in critical state of liquid solid. At this very moment ,high density heat flow and dynamic heat resistance are present in material processing circumstance .A basic theory problem of designing this kind of machine is to study mechanism of heat transmittance and energy conversion ,and to establish mathematical model .3. 2 Friction Constraint Mechanism of Plastic Flow Interface ofMaterial Processing MachineThe coupling between operation mechanism and workpiece is very complicated because plastic flow is present in processing interface. The interface state , determined by velocity , load thermodynamic process , elasticity of operation mechanism ,plasticity of workpiece ,dynamic behaviour of interface sticking - sliding and partial hydrodynamic lubrication ,etc ,affect and form friction constraints mechanism peculiar to material processing machine ,because these constraints present strong non - linearity ;and under certain circumstances ,the constraints may be destroyed or mismatched instantaneously and thus dynamic instability is resulted in. Thus following problems can be put forward : Mechanism of “spectre chatter”arose from sticking - sliding friction and partial hydrodynamic lubrication in rolling interface , instability condition and mechanism of constraint between smooth surface and rotating body under the circumstance of high speed ,heavy - duty and boundary lubrication , Lubrication film absorption mechanism and physical chemistry behaviour of interface of unceasingly regenerative surface ,the relationship between rheological characteristic and machine operation parameters.3. 3 Multi - Body Non - Linear Contact Mechanism Under theCondition of Extra - High Pressure FieldTo build the super strength pressure field on large area is one of basic function of material processing machine , and it is also necessary toform by once large - size structure element (such as spacecraft , intercontinental vehicles ,car and large - scale aeroplane etc) . The ability to build super strong pressure field is one of important feature and the base of independent national defense. Under the circumstance of super strength pressure field ,multi - body strong nonhertz contact and non - linear friction will be produced ,thus local permanent deformation and degrading of element accuracy may be led. New theory foundation of design of machine with super strength pressure field will be furnished through study of multi - body strong non - hertz contact mechanism , multi - body non - linear friction mechanism (such as providing force - displacement mixed solving process of three - dimension multi - body) . 3. 4 Load Distribution Law in Multi - Sliding Pair With StructureBias LoadWith regard to statically indeterminate structure ,load distribution of constraint point is determined by deformation compatibility condition. However ,concerning some plane large - size statically indeterminate structure with sliding degree of freedom in third dimension ,load distribution can not be determined by deformation compatibility condition. Thus new theory basis will be provided by analyzing of contact behaviour and mechanism of sliding pair (such as creep ,force of friction ,integral deformation compatibility condition ,etc) .3. 5 Coupling Mechanism and Stability of Multi - Physical Fields inMaterial Processing SystemsIn the wake of system function becoming more and more diversified , conventional technology limits in material processing machine is being broke through unceasingly ,system structure also becomes increasingly complicated ,and system performance becomes increasingly multi - causal . For example ,any instantaneous state of roller in fast rolling mills is affected by elastic deformation ,plastic flow ,heat transfer process , hydro - dynamic lubrication process ,interface physical chemistry molecular state and so on . In addition ,electromechanical coupling in processing system have already gone beyond conventional concept ,for instance ,some singular point phenomenon (such as micro - variable can be transformed into macro - variable) ,are present ,thus roller operation instability may be led by perturbation[8 ] . Therefore ,this subject will study the interaction mechanism of multi - physical field and the influence on processing system stability and processing material quality started with analysis of micro - state of executive body.3. 6 Multi - Technology Integration and Coalescence of AccurateControlThe material processing machine ,which operate under the circumstance of extraordinary physical field ,is a complicated large - scale system ,and some parameters of the system vary on feasible field boundary ;thereby ,to keep under accurate control and adjustment of multi field circumstance ,multi - dimension coordination ,multi - energy conversion , multi - level information transfer ,interface multi - process coupling ,etc. is of much significance. Since a variety of multi - interaction exists in control model ,it is necessary to establish integration framework of coordination work according to decoupling of control model ,so as to accurate control based on the multi - technology integration and coalescence is realized.3. 7 Quasi - Reality Design and Concurrent Design Based onKnowledge Innovation SystemsDigitalization and visualization of material processing technology will promote immediately the quality of design, operation and control . Therefore optimization of material processing technology and material processing machine by means of realization of virtual simulation of processing procedure through quasi - reality design and concurrent design is one of our pressing study subjects.3. 8 Mechanical Behavior of Special Function Materials in theExtraordinary Physical FieldMany key elements and parts in material processing machine are often under the circumstance of super strong force field ,temperature field ,electronic magnetic field and flow field ,and must have the functions of constructing special physical interface. However ,it is difficult for common single - substance material such as metal ,ceramic polymer ,etc. to have both high index of single property and excellent overall quality. Therefore we need to use certain material with new functions for key position[9 ],for instance ,multi - dimension function gradient material with ultrahigh physical property ,multi - dimension function gradient material with intelligence. For these reasons ,it is necessary to study basic law and mechanism of these kind of function material mentioned above ,for instance ,stress (strain) distribution function ,failure mechanism and design criteria of material under the circumstance of extraordinary physical field ,static (dynamic) stiffness and damping ,digitalization design and visualization design of processing system made of gradient function material ,etc ,so that the generalmechanics law of element which is under the circumstance of extraordinary physical field and made of anisotropy multi - dimension gradient function material is obtained. Nowadays ,material processing science and technology is forging rapidly ahead. A forward - looking study aiming at key technology problem of material processing machine will provide theory and technology reserve for manufacturing science and industry of 21st century.材料制备机械设计理论中的新趋势和新问题摘要:在全面综述材料制备机械技术及设备发展动态的基础上,提出了研究领域的几个新问题和发展方向,如通过集成和融合现代相关前沿科学和技术，生产具有超常和特殊性能的新概念材料等,并进行了简要分析和讨论.关键词:界面;超常物理场;制备机械;功能材料1．机械材料加工的时代背景在人类进步的一段长时间范围内，许多工具、机械和方法被提出来；不同的原料用不同的工具来加工。
材料专业外文翻译----导体和半导体材料
中文3550字作者：Chad R. Snyder, Member Frederick I. Mopsik国籍：America出处：IEEE TRANSACTIONS ON INSTRUMENTATION AND EASUREMENT A Precision Capacitance Cell for Measurement of Thin Film Out-of-Plane Expansion–Part III: Conducting andSemiconducting MaterialsAbstract—This paper describes the construction, calibration, and use of a precision capacitance-based metrology for the measurement of the thermal and hygrothermal (swelling) expansion of thin films. It is demonstrated that with this version of our capacitance cell, materials ranging in electrical properties from insulators to conductors can be measured. The results of our measurements on p-type<100> -oriented single crystal silicon are compared to the recommended standard reference values from the literature and are shown to be in excellent agreement.Index Terms—Capacitance cell, coefficient of thermal expansion (CTE), guarded electrode, high sensitivity displacement, inner layer dielectrics, polymers, thin films.I. INTRODUCTIONTHE coefficient of thermal expansion (CTE) is a key design parameter in many applications. It is used for estimating dimensional tolerances and thermal stress mismatches. The latter is of great importance to the electronics industry, where thermal stresses can lead to device failure. For accurate modeling of these systems, reliable values are needed for the CTE.Traditionally, displacement gauge techniques such as thermomechanical analysis (TMA) have been utilized for determining the CTE. However, standard test methods basedm [1-2]. This ison these techniques are limited to dimensions greater than 100 mproblematic for materials which can be formed only as thin layers (such as coatings and certain inner layer dielectrics). Additionally, there is some question as to whether values obtained on larger samples (bulk material) are the same as those obtained for thin films,even when the effects of lateral constraints are included in the calculations .It has long been recognized that capacitance-based measurements, in principle, can offer the necessary resolution for these films . For a pair of plane-parallel plate capacitors, if the sample is used to set the spacing of the plates d while being outside of themeasurement path, then for a constant effective area of the plates A , the capacitance in a vacuum vac C is given by the well-known equation d A C vac 0ε=(1) where 0εis the permittivity of free space (m pF 854.80=ε).With the sampleoutside of the measurement path and only air etween the electrodes, the vacuumcapacitance is obtained rom the measured capacitance C by air vac C C ε=(2)where air ε is the dielectric constant of air.In three previous papers, the design and data reduction techniques were presented for our three-terminal capacitance-based metrology for thin polymer film measurements. The first paper (I) described the initial design based on gold-coated Zerodur. However, several problems were encountered. It was discovered that Zerodur displays ferroelectric behavior, with an apparent Curie temperature of 206 ℃as determined by fitting with a Curie –Weiss law. The rapid change in the dielectric constant of the Zerodur along with a coupling from the central contact through the guard gap to the high electrode created an apparentnegative thermal expansion . The second problem with the initial design was with the gold coating. This coating had the tendency to ―snow plow‖ when scratches formed in the surface creating raised areas which would result in shorts when measurements were performed on thin samples. The second problem with the gold was that it underwent mechanical creep under loading.To resolve these problems, a new electrode was designed from fused quartz coated with nichrome. A groove filled with conductive silver paint was added to the back side of the bottom electrode around the central contact to intercept any field lines between the central wire contact through the guard gap to the high electrode. The new design was described in the second paper (II) along with thermal expansion measurementson<0001>-oriented single crystal sapphire (32O Al ) and a 14-m μ thick inner layer dielectric material [10]. It was recognized in II that the data reduction was simple as long as the air filling the gap between the capacitor plates was dry. However, to expand the utility of the capacitance cell to hygrothermal expansion (i.e., swelling in a humid environment), the third paper (III) described the data reduction techniques necessary for use of the capacitance cell under humid conditions .Fig. 1. Schematic of the electrodes. Note that the shaded areas correspond tothe nichrome coating.The resolution of the instrument was determined in II and III. For dry, isothermal conditions, the capacitance cell can measure relative changes in thickness on the order of 710- , for a 0.5-mm thick sample; this corresponds to a resolution on the orderof m 11105-⨯. Under dry conditions in which the temperature is changed, thereproducibility of a relative thickness change (e.g., for CTE measurement) is on the order of 610- . Finally, under humid conditions, the ultimate resolution is primarily a function of temperature —the actual values of which are given in III.In II, a deficiency was recognized in the design. Neither semiconducting or conducting materials could be used as the material for testing. This was especially the case for silicon, which forms a Schottky barrier with nichrome and acts as a voltage rectifier. Additionally, because of the nature of the interface, the 1 kHz measurement frequency generates ultrasound which results in the epoxy contacts being shaken loose. We mentioned brieflyin II that if the top electrode had a guard ring added, the sample could be held at zero potential and this would no longer be a problem. To demonstrate this, we constructed such a capacitance cell—the design and testing of which are described in this paper.II. CAPACITANCE CELL DESIGNA.Electrode DesignBecause the construction of the electrodes was thoroughly described in II, a less detailed description will be given with emphasis on the changes in the design. The electrodes were constructed, as before, in the following manner (see Fig. 1).cm210⨯cylindrical blanks of fused quartz were ground and polished to optical flatness.cmSmall holes were drilled through the center of each blank so that 16 gauge wire could be inserted into them. The wires were then cemented with a conducting epoxy (resistivity of 10⨯-44at 25℃). A second hole and wire were then added to each blank Ωcmapproximately 0.75 cm from the edge of the blanks. A coating of nichrome was then added such that it covered all surfaces except for a small area around the back of the blanks. A guard gap was scribed on both the top and bottom electrodes such that no material was raised which could cause a short. On the bottom electrode, the guard gap was scribed on a 3 cm diameter, and on the top electrode it was scribed on a 6 cm diameter. In the bottom electrode, a 1 cm diameter well was cut into the back of the blank which extended to within 5 mm of the front surface. This well was then filled with a thin conductive silver paint. The paint connected the outer guard ring’s metallization to the edge of the well.Fig. 2. Schematic of the assembled capacitance cell.B. Cell Assembly and Capacitance MeasurementsThe holder described in II was employed for the modified cell. In this version of the capacitance cell, both conductors of the semirigid coaxial line were connected to the top electrode. The center connector and braid were connected to the center area and outer guard ring, respectively, by fine 30 gauge wire coils. The coils were terminated with center female contacts from 50ΩBNC connectors, which could be easily connected/disconnected to the 16 gauge tinned copper wire that was epoxied into the electrodes. A schematic of the assembled cell is shown in Fig. 2. The female BNC connector on the brass holder (bottom electrode) was connected to the low terminal, and the female BNC connector on the semirigid coaxial line was connected to the high terminal. All connections from the capacitance cell to the bridge were performed using Teflon insulated low noise cables.The capacitance measurements were obtained using a commercial automatedthree-terminal capacitance bridge which uses an oven-stabilized quartz capacitor and has a cited guaranteed relative resolution of better than 7105⨯ pF/pF for the range of capacitances used with this cell (Andeen –Hagerling 2500 A 1 kHz Ultra-PrecisionCapacitance Bridge with Option E). (Note that the ―useful‖ relative resolution is suggestedby the manufacturer to be typically a factor of 10 or more better that the cited relative resolution.) The capacitance bridge’s c alibration was verified against a National Institute of Standards and Technology (NIST) calibrated standard reference capacitor —the difference between the two was within the capacitor’s uncertainty.All measurements were performed in a temperature/humidity chamber equipped with a 90 ℃ dew point air purge. The cell was equilibrated at each temperature until the relative fluctuations in the vacuum corrected capacitance were no more than 710-10 pF/pF. Barometric pressure was monitored using a digital pressure sensor with amanufacturer’s stated uncertainty of 0.1 mm Hg(13 Pa). As stated previously in II, the temperature of the cell was calibrated in terms of the chamber temperature with aresistance temperature device (RTD) mounted to the cell with thermally conducting paste. The RTD was calibrated against a NIST certified ITS-90 standard reference thermometer. As in II, because we are using a dry air purge, we can use the ideal gas law correction to determine the molar volume of the air air v to calculate vac C pRT v air =（3） WhereT---absolute temperature;P---pessure;R---gas constant(11314507.8--⋅⋅⋅=K mol kpa L R )[12].From this and the value of the molar polarization of dry air obtained from the literature, mol L P 31031601.4-⨯=[13], the dielectric constant of the air separating the electrodes is⎪⎭⎫ ⎝⎛-+⎪⎭⎫ ⎝⎛=aie air aie air air v P v P 112ε(4) III. MEASUREMENTSA. Cell CalibrationTo use (1) to calculate the thickness of the sample, the effective area must be known. To determine this value as a function of temperature, as in II, we calibrated the area andarea expansion through the use of Zerodur spacers with thicknesses of approximately 2.0 mm. As in II, the actual dimensions of the Zerodur spacers were measured in a ball to plane configuration with a specially designed caliper equipped with a linear voltage displacement transducer (LVDT) that had a resolution of mm 4101-⨯±. The cell was assembled with the Zerodur spacers using the sample preparation described in II.Measurements were performed at 0 ℃, 25 ℃, 50 ℃, 75℃, 100℃, 125 C, and 150℃. The cell was cycled through this range of temperature three times, and the values for vac C were determined for each run after averaging all the properties over approximately 1 h using 10 s increments (a total of 360 data points) after equilibrium was achieved. The area A was calculated using the room temperature thickness measurements and the 25 ℃ value for vac C . All subsequent determinations of A, at higher and lower temperatures, were corrected for the slight expansion and contraction of the Zerodur as a function oftemperature (161005.0--⨯=K Zerodur α). The results of the effective radius of the electrode as a function of temperature are plotted in Fig. 3.Fig. 3. Effective radius of the bottom electrode as a function of temperatureobtained by measurements using Zerodur and correcting for its slightexpansion.Fig. 4. Relative expansion of the<100>-oriented single crystal silicon as afunction of temperature. The line is a plot of the data fromB. p-Type Doped<100> Single Crystal SiliconTo demonstrate the ability of the cell to measure silicon and to provide accurate values for thermal expansion, a 0.6-mm thick wafer of single-side polished, back side stress relieved, p-type, <100>-oriented single crystal silicon with a resistivity of 15 cm was1cm. The pieces broken (by scribing) into three pieces. Each piece was approximately2were then cleaned with ultra pure distilled water and ethanol. The cell was assembled in the same fashion as was described in II and was placed in a vacuum oven at ambient temperatures for approximately 1 h to effectively wring the sample.3 . Measurements were performed at 25℃, 50 ℃, 75 ℃, 100 ℃, 125 ℃, and 150℃, a minimum of two times each. (Note: No point was taken at 0 ℃ due to problems with the compressor in the environmental chamber.) The wafer thickness was determined using the effective radius versus temperature data shown in Fig. 3. The results of this analysis are shown in Fig. 4 along with the recommended expansion data on silicon obtained from [14]. It should be noted that the standard reference data was defined relative to 20 ℃ whereas we havemeasured, for convenience, relative to 25 ℃. Therefore, the standard reference, relative expansion data was shifted in Fig. 4 by an amount S equal to()()K S T 5-=α(5)where T α is the CTE at temperature T taken fromIt is apparent that the two sets of data agree within the experimental uncertainty. (The error bar is smaller on the 25 ℃ data point than on the higher temperatures due to the fact that more repeat runs were performed, which reduced the uncertainty for that data point.) This demonstrates several key conclusions regarding the capacitance cell. First, thelimitations of the previous design have been eliminated; silicon and conducting samples can be measured. Second, the results show that the capacitance cell produces data that agree with literature data. Finally, we have further demonstrated the advantage of our technique for measurement of thin samples over commercially available TMAs. Thevalidity of this statement can be shown by considering the results of a round robin study. This study was performed among researchers at NIST, IBM Endicott, DEC,Microelectronics and Computer Technology Corporation, Naval SurfaceWarfareCenter —Crane Division, CALCE Electronic Products and Systems Center at the University of Maryland, Cornell University, University of Texas at Austin, Purdue University, and the Semiconductor Research Corporation (SRC) on the measurement of the CTE of single crystal<100> silicon using various commercial TMAs [15]. A 1.1765-mm thick sample of <100>single crystal silicon was used by all participants. All reported values for the CTE of silicon were below the literature values for the corresponding temperature ranges by 15% to 40%. Our sample was approximately half as thick as their sample, yet our values arewithin the experimental error. (It should be recalled that our total precision is independent of actual thickness and the main error is due to electrode/sample interfacial effects.Therefore, had we used the thicker sample, as was used in the round robin study, the error in our results would have been reduced.)In closing, it should be mentioned that since silico n was the ―worst case‖ scenario for the new capacitance cell, it was deemed unnecessary to perform measurements on single crystals of a metallic sample which have a much higher CTE. However, a singlemeasurement was taken on the silicon by connecting the braids from the high and low terminals together shorting the two guard rings as if it were done by a metallic sample. The measured capacitance was unchanged; this therefore demonstrated that conducting materials can be measured.IV. CONCLUSIONSWe have presented the designs and implementation of our capacitance cell for the measurement of conducting and semiconducting materials (as well as dielectrics). The thermal expansion data, obtained with the new version of our capacitance cell, on p-type doped single crystal silicon have demonstrated both the ability of the cell to measure silicon and conducting samples and the ability of the cell to provide accurate CTE data on these types of materials. As a result, it is apparent that this metrology can also be applied to thin polymer films deposited on silicon substrates. Furthermore, this cell can also be used to study the hygrothermal expansion (swelling due to the presence of moisture) by utilizing the data reduction techniques described in III. Accordingly, this technique should be especially useful to the microelectronics packaging industry for the characterization of inner layer dielectrics as well as composite structures.ACKNOWLEDGMENTThe authors would like to thank Dr. J. R. Ehrstein in the Semiconductor Electronics Division at NIST for providing the silicon sample.一种精密电容测量薄膜平面扩张的第三部分：导体和半导体材料 摘要—本文介绍了设计、校准，并且使用精密电容基础计量学来测量薄膜的热、湿热（肿胀）的扩张。
有关材料学的英语作文
有关材料学的英语作文Title: Exploring the Wonders of Materials Science。
Materials science, often dubbed as the backbone of modern technology, encompasses a vast array of disciplines aimed at understanding, designing, and manipulatingmaterials for various applications. From the microchips powering our electronics to the advanced materials used in aerospace engineering, the realm of materials science is both diverse and fascinating.At its core, materials science delves into the structure, properties, and behaviors of different materials, ranging from metals and ceramics to polymers and composites. By comprehensively studying these aspects, scientists and engineers can tailor materials to meet specific requirements, whether it be enhancing strength, improving conductivity, or introducing novel functionalities.One of the fundamental concepts in materials science isthe relationship between structure and properties. The atomic and molecular arrangement within a material greatly influences its mechanical, electrical, thermal, and optical properties. For instance, the crystalline structure of metals contributes to their high strength and ductility, while the arrangement of polymers dictates theirflexibility and resilience. Understanding these relationships enables researchers to engineer materials with desired characteristics, opening doors to innovation across industries.Furthermore, materials science plays a pivotal role in addressing pressing global challenges, such as environmental sustainability and renewable energy. With the growing concern over climate change, there is an increasing demand for eco-friendly materials and technologies. In response, materials scientists are developing biodegradable polymers, efficient energy storage devices, and lightweight materials for transportation, all aimed at reducing our carbon footprint and promoting a greener future.Nanotechnology, a cutting-edge field within materialsscience, holds immense promise for revolutionizing various industries. By manipulating materials at the nanoscale, scientists can harness unique properties and phenomena not observed in bulk materials. For instance, carbon nanotubes exhibit exceptional strength and conductivity, making them ideal candidates for reinforcing composites and developing high-performance electronics. Similarly, quantum dots, semiconductor nanoparticles, are paving the way for advances in displays, solar cells, and medical imaging.The interdisciplinary nature of materials sciencefosters collaboration among scientists, engineers, and researchers from diverse backgrounds. Through interdisciplinary approaches, such as computational modeling, materials informatics, and advanced characterization techniques, breakthroughs are achieved at an accelerated pace. By leveraging expertise from chemistry, physics, engineering, and beyond, the boundaries ofmaterials science continue to expand, driving innovationand technological advancement.In conclusion, materials science is a dynamic andinterdisciplinary field that underpins many aspects of modern society. By unraveling the mysteries of materials and harnessing their potential, we can address global challenges, propel technological progress, and improve the quality of life for people around the world. As we continue to push the boundaries of what is possible, the wonders of materials science will undoubtedly shape the future of humanity.。
金属材料专业外文翻译--利用神经网络预测与其他预测方法对δ铁素体不锈焊缝的分析和比较
翻译原文Delta ferrite prediction in stainless steel welds using neural network analysis and comparison with other prediction methodsM. Vasudevan a,∗, A.K. Bhaduri a, Baldev Raj a, K. Prasad Raoba Metallurgy and Materials Group, Indira Gandhi Centre for Atomic Research,Kalpakkam, Indiab Department of Metallurgy, Indian Institute of Technology, Chennai, IndiaReceived 2 May 2002; received in revised form 11 December 2002; accepted 17February 2003AbstractThe ability to predict the delta ferrite content in stainless steel welds is important for many reasons. Depending on the service requirement,manufacturers and consumers often specify delta ferrite content as an alloy specification to ensure that weld contains a desired minimum or maximum ferrite level. Recent research activities have been focused on studying the effect of various alloying elements on the delta ferrite content and controlling delta ferrite content by modifying the weld metal compositions. Over the years, a number of methods including constitution diagrams, Function Fit model, Feed-forward Back-propagation neural network model have been put forward for predicting the delta ferrite content in stainless steel welds. Among all the methods, neural network method was reported to be more accurate compared to other methods. A potential risk associated with neural network analysis is over-fitting of the training data. To avoid over-fitting, Mackay has developed a Bayesian framework to control the complexity of the neural network. Main advantages of this method are that it provides meaningful error-bars for the model predictions and also it is possible to identify automatically the input variables which are important in the non-linear regression. In the present work, Bayesian neural network (BNN) model for prediction of delta ferrite content in stainless steel weld has been developed. Theeffect of varying concentration of the elements on the delta ferrite content has been quantified for Type 309 austenitic stainless steel and the duplex stainless steel alloy 2205. The BNN model is found to be more accurate compared to that of the other methods for predicting delta ferrite content in stainless steel welds.1. IntroductionThe ability to estimate the delta ferrite content accurately has proven very useful in predicting the various properties of austenitic SS welds. A minimum delta ferrite content is necessary to ensure hot cracking resistance in these welds [1,2], while an upper limit on the delta ferrite content determines the propensity to embrittlement due to secondary phases, e.g. sigma phase, etc., formed during elevated temperature service [3]. At cryogenic temperatures, the toughness of the austenitic SS welds is strongly influenced by the delta ferrite content [4]. In duplex stainless steel weld metals,a lower ferrite limit is specified for stress corrosion cracking resistance while the upper limit is specified to ensure adequate ductility and toughness [5]. Hence, depending on the service requirement a lower limit and/or an upper limit on delta ferrite content is generally specified. During the selec-tion of filler metal composition, the most accurate diagram to date WRC-1992 is used generally to estimate the_-ferrite content [6]. The Creq and Nieq formulae used for generating the WRC-1992 constitution diagram is given by Creq=Cr+Mo+0.7Nb and Nieq=Ni+35C+20N+0.25Cu. The limitation of these equations is that values of the coefficients for the different elements remain unchanged irrespective of the change in the base composition of the weld. However, the relative influence of each alloying addition given by the elemental coefficients in the Creq and Nieq expressions is likely to change over the full composition range. Furthermore,these expressions ignore the interaction between the elements. Also, there are a number of alloying elements that have not been considered in the WRC-1992 diagram. Elements like Si, Ti, W have not been given due to considerations, though they are known to influence the delta ferrite content. Hence, the delta ferrite content estimated using the WRC-1992 diagram would always be less accurate and may never be close to the actual measured value.In the Function Fit model [7] for estimating ferrite, the difference in free energy between the ferrite and the austenite was calculated as a function of composition and this was related to ferrite number (FN). The equation used in this model to determine FN is given below:FN = A[1 + exp(B + C_G)]−1 (1)where A, B and C are the constants. The advantages of this semi-empirical model over the WRC-1992 diagram include its considering effect of other alloying elements and the ease of extrapolation to higher Creq and Nieq values. This Function Fit method can be used for a wide range of weld metal compositions and owing to the analytical form of this model, the FN can be quantified easily. However, the accuracy of this method is not greater than the WRC-1992 diagram. Vitek et al. [8,9] sought to overcome the major limitation of the constitution diagram and the Function Fit method of not taking into account the elemental interactions, by using neural networks for predicting ferrite in SS welds.The improvement in accuracy in predicting the delta ferrite content by using neural networks, involving a feed-forward network with a back-propagation optimization scheme, has been clearly brought in their study. The effect of various elements on the delta ferrite content for a few base compositions was examined by calculating the FN as a function of composition. However, it was not possible in their analysis to directly interpret the elemental contributions to the final FN. The prediction and measurement of ferrite in SS welds remains of scientific interest due to limitations in all the current methods, and newer methods and constitution diagrams are continuously being proposed to predict the delta ferrite content for a wider range of SS types. It was in this context that the development of a more accurate neural network based predictive tool for estimating the effect of various alloying elements on the delta ferrite content for different SS welds was taken up in this work.A potential risk associated with neural network analysis is over-fitting of the training data. To avoid over-fitting, Mackay [10] developed a Bayesian framework to control the complexity of the neural network, with its main advantages being that itprovides meaningful error-bars for predictions and also enables identifying the input variables that are important in the non-linear regression. Hence, in the present study, Bayesian neural network (BNN) analysis was applied to develop a generalized model for FN prediction in stainless steel welds and the effect of variations in the concentration of the elements on the FN for 309 stainless steel and duplex stainless steel base compositions were also quantified.2. DatabaseAs the aim of the present work was to model for the FN as a function of chemical composition, the database of 924 datasets for shielded metal arc (SMA) weld compositions and delta ferrite contents, representing the common 300-series SS weld compositions (viz., 308, 308L, 309,309L, 316, 316L, etc.) and duplex stainless steels used for generating the WRC-1992 diagram was used [11]. For the datasets in which the composition values for elements such as Nb, Ti, V, Cu and Co were not available, their values were assumed zero. Table 1 shows the range, mean and standard deviation of the each composition variable (input) and the FN (output). This simply gives the idea of the range coveredand cannot be used to define the range of applicability of the neural network model as the input variables are expected to interact in neural network analysis. In BNN analysis, size of the error-bars define the range of useful applicability of the trained network.Table 1 Range, mean and standard deviations of the composition variables (input) and the FN (output)3. BNN analysisThe networks employed consist of 13 input nodes xi representing the 13 composition variables, a number of hidden nodes hi and one output y. The schematic structure of the network is shown in Fig. 1. The single output represents the FN. Both the input and output variables were normalized within the range ±0.5 as follows:where xN is the normalized value of x, which has maximum and minimum values given by x max and x min. Eighty different neural network models were created using the datasets, with the number of the hidden units varying from 1 to 16 and with five different sets of random seeds used to initiate the network for a given number of hidden units. All these models were trained on a training dataset that consisted of a random selection of half the datasets (i.e. 462 datasets), while the remaining half formed the test dataset that was used to examine how the model generalizes with unseen data. For calculating the outputs from the inputs, the linear functions of the inputs xj are multiplied by the weights wij and operated by the following hyperbolic tangent transfer function so that each input contributes to every hidden unit, where N is the total number of input variables:Here the bias is designated as θ and is analogous to the constant in linearregression. The transfer from the hidden units to the output is linear, and is given by:The output y is therefore a non-linear function of xj, with the function usually selected for flexibility being the hyperbolic tangent. Thus, the network is completely described if the number of input nodes, output nodes and the hidden units are known along with all the weights wij and biases θi.These weights wij are determined by training the network and involves minimization of a regularized sum of squared errors.The BNN analysis of Mackay [10] allows the calculation of error-bars with two components—one representing the perceived level of noise (σv) in the output and the other indicating the uncertainty in the data fitting. This latter component of the error-bars emanating from the Bayesian framework allows the relative probabilities of the models with different complexity to be assessed. This enables estimation of quantitative error-bars, which vary with the position in the input space depending on the uncertainty in fitting the function in that space. Hence, instead of calculating a unique set of weights, a probability distribution of weights is used to define the uncertainty in fitting. Therefore, these error-bars become large when data are sparse or locally noisy. In this context, a very useful measure of the error is the logarithm of the predictive error (LPE) given by the following:where t is the target for the set of inputs x, while y the corresponding network output. σy is related to the uncertainty of fitting for the set of inputs x. By using LPE, the penalty for making a wild prediction is reduced if that prediction is accompanied by an appropriately large error-bar, with a larger value of the LPE implying a better model. Further, by this method it is also possible to automatically identify the input variables that are significant in influencing the output variable, as the input variables that are less significant are down-weighted in the regression analysis.3.1. Over-fitting problemIn BNN analysis, two solutions are implemented which contribute to avoidover-fitting. The first is contained in the algorithm due to MacKay [12]: the complexity parameters α and β are inferred from the data, therefore allowing automatic control of the model complexity. The second resides in the training method. The database is equally divided into a training set and a testing set. To build a model, about 80 networks are trained with different number of hidden units and seeds, using the training set; they are then used to make predictions on the unseen testing set and are ranked by LPE.3.2. Committee modelThe networks with different number of hidden units will give different predictions. But predictions will also depend on the initial guess made for the probability distribution of the weights (the prior). Optimum predictions are often made using more than one model, by building a committee. The prediction .y of a committee of networks is the average prediction of its members, and the associated error-bar is calculated according to Eq. (6):where L is the number of networks in a committee. Note that we now consider the predictions for a given single set of inputs and that exponent l refers to the model used to produce the corresponding prediction y(l ). In practice, an increasing number of networks are included in a committee and their performances are compared on the testing set. Most often,the error is minimum when the committee contains more than one model. The selected models are then retrained on the full database.4. Results and discussionsThe characteristics of the BNN model on FN is discussed in detail elsewhere [13]. The comparison between the predicted and measured FN values for the committee of models (38 models in the committee) is shown in Fig. 2 for the complete dataset. There was excellent agreement between the measured and the predicted FN values. The correlation coefficient was determined to be 0.98025.4.1. Significance of the individual elements on the FNFig. 3 indicates the significance σw of each of the input variables as perceived by the first five neural network models in the committee. The σw value represents the extent to which a particular input explains the variation in the output, as for a partial correlation coefficient in linear regres-sion analysis. It is observed from Fig. 3 that the elements Mn and Nb are not significant in influencing the FN. The observation of Mn not having a significant influence on the FN for the 300-series austenitic SS is in agreement with the reported results that the variation in Mn from 1 to 12 wt.% had almost no effect on the as-deposited FN [14]. However, the element Nb, which was found in the present study to have an insignificant effect on the FN is included in the Creq formula used in the WRC-1992 diagram. As expected, Cr and Ni were found to be the main elements influencing the FN. As per the present model, the elements that influence the FN in order of significance are: Mo >N >V >Ti >Cu >Co >Si >C >Fe.However, some of these elements namely V, Ti, Co and Si are not included in the Nieq and Creq formulae used in the WRC-1992 diagram.Fig. 2. Comparison between the predicted and measured FN values for the entire dataset using the committee of modelsFig. 3. Perceived significance σw values of the first five FN models for each input.4.2. Comparison of accuracy of present model with other existing methodsAnalysis of the error distributions (measured FN–predicted FN) for the present BNN model shows that the absolute error was <2.5 for most of the datasets used in thetraining, while for the FNN-1999 model the absolute error was <3 for about 80% of the dataset used in training. It is important to note here that in the present BNN model, the entire datasets were used for retraining the committee of models, while in the FNN-1999 model only 90% of the datasets were used for training. Further, the error distributions for the present BNN model is symmetrical about zero (Fig. 4) implying good fitting of the model to the datasets. Also, the ―tail‖ of the error distributions is less compared to the other methods. Table 2 shows the comparisons of the errors for the BNN model and the FNN-1999 model. Comparison of the quantified error distributions with those of the FNN-1999 model shows that the present BNN model is superior. It has been reported that the FNN-1999 model is more accurate compared to the WRC-1992 diagram and Function Fit model. Hence, the root mean square (RMS) error between the measured and predicted FN values for the present BNN model and the other three methods were compared and it was found that the present BNN model has the lowest error among the four methods, BNN model showing an improvement of 43%over the FNN-1999 model and about 65% over the WRC-1992 diagram and Function Fit model. Table 3 shows the RMS error values for all the four methods. As the RMS error values represent the quan- titative measure of the degree of fit of the various models to the datasets on which they were trained, this comparison clearly establishesthat among the available methods the present BNN model is the most accurate model for prediction of FN in austenitic SS welds.Fig. 5.Predicted FN vs concentration of the elements for 309 austenitic stainless steelsweld. The plot shows the variation in the FN when one of the element is varied and all other concentration are held constant at the 309 SS composition except Fe, which is adjusted to compensate for the varying element concentration.4.3. Effect of compositional variations on the FNThe severe limitation of the WRC-1992 diagram is that the coefficients in the terms for Creg and Nieg formulas are constant and hence the influence of an individual element on FN is same irrespective of the change in the base composition. As neural networks can take into account the interaction between the input variables on their influence over the output variable, the interaction between the different elements on their influence over the FN is quantified for stainless steel welds using the BNN analysis. The results for 308, 308L, 316, 316LN have already been presented elsewhere [13,15]. In the present work, the effect of variations in the concentration of the elements on FN have been quantified for 309 stainless steel and duplex stainless steel welds. This was done with two starting base compositions and then al- lowing each element to vary over a limited range adjusting Fe concentration accordingly but holding all other element concentrations constant. Table 4 shows the base compositions of the 309 SS and duplex stainless steel welds used in the present study.4.3.1. The 309 stainless steel weldThe variation in the predicted FN as a function of the variation in the concentration of the elements is found to be non-linear (Fig. 5). The FN is found to decrease with increasing concentration of the elements C, N and Ni. These elements acts as austenite stabilizers. The FN is found to increase with increasing concentration of the elements Cr, Si and V and these elements are called ferrite stabilizers. The above observations are in agreement with the literature. The elements Mn, Mo, Nb, Ti, Cu and Co do not influence the FN value for 309 base composition used. However, in the WRC-1992 diagram the composition of the element Cu is taken in to account for calculating the Nieq and the composition of Mo and Nb are included for calculating the Creq. Thus, estimation of delta ferrite content by usingthe WRC-1992 diagram will always be less accurate. The BNN model generated by us is more accurate compared to the WRC-1992 diagram which was generated based on the linear regression analysis. Hence, the trends of the influence of concentration of the elements on FN predicted by the model is more useful in controlling FN through compositional modifications in this type of steel (Fig. 5).Fig. 6.Predicted FN vs concentration of the elements for duplex stainless steel weld.The plot shows the variation in the FN when one of the element is varied and all other concentration are held constant at the duplex stainless steel composition except Fe, which is adjusted to compensate for the varying element concentration.4.3.2. Duplex stainless steel (alloy 2205) weldThe variation in the FN with variation in the concentration of the elements is found to be non-linear (Fig. 6). The increase in the concentration of the elements C, N, Mn and Ni is found to decrease the FN. However, the effect of Mn is not as significant as the other austenite stabilizers. The increase in the concentration of the elements Cr, Si, Mo, V and Co is found to increase the FN for the duplex stainlesssteel welds. The effect of vanadium is not as significant as the other ferrite stabilizers. The elements Cu, Nb and Ti are found not to influence the FN for duplex stainless steel welds. However, the elements Cu and Nb are included in the WRC-1992 diagram in calculating the Nieq and Creq,respectively. The trends identified by this analysis of the influence of concentration of the elements on the FN is very useful in controlling the FN by adjusting weld metal composition in duplex stainless steel welds. Hence, depending on the base composition, the influence of individual elements on the FN is different. However, the WRC-1992 diagram uses the same equation for all the stainless steel welds and is the severe limitation of the diagram (Fig. 6).5. Conclusions1. The generalized model for predicting the FN in stainless steel welds using BNN analysis has been developed. The accuracy of the BNN model in predicting FN is superior compared to the existing FN prediction methods.2. Significance of the individual elements on FN has been quantified. Neural network analysis has shown that elements like manganese and niobium are insignificant in influencing the FN in stainless steel welds.3. The effect of variation in the concentration of the elements on the FN have been quantified for 309 and duplex stainless steel welds. Neural network analysis has shown that there is a change in the role of elements when the base composition is changed.4. Cobalt is found to be ferrite stabilizer for the duplex stainless steel welds and is found not to influence the FN for the austenitic stainless steel welds.References[1] C.D. Lundin, C.P.D. Chou, Hot cracking susceptibility of austenitic stainless steel weld metals, WRC Bull. 289 (1983) 1–80.[2] C.D. Lundin, W.T. Delong, D.F. Spond, Ferrite-fissuring relationships in austenitic stainless steel, Weld Met. 54 (8) (1975) 241s–246s.[3] J.M. Vitek, S.A. David, The sigma phase transformation in austenitic stainless steels, Weld. J. 65 (4) (1986) 106s–111s.[4] E.R. Szumachowski, H.F. Reid, Cryogenic toughness of SMA austenitic stainless steel weld metals, Weld. J. 57 (11) (1978) 325s–333s.[5] D.J. Kotecki, Ferrite control in duplex stainless steel weld metal, Weld. J. 65 (10) (1986) 273s–278s.[6] D.J. Kotecki, D.T.A. Siewert, WRC-92 constitution diagram for stainless steel weld metals: a modification of the WRC-1988 diagram, Weld. J. 71 (5) (1992)171s–178s.[7] S.S. Babu, J.M. Vitek, Y.S. Iskander, S.A. David, New model for prediction of ferrite number in stainless steel welds, Sci. Technol.Weld. 2 (6) (1997) 279–285. [8] J.M. Vitek, Y.S. Iskander, E.M. Oblow, Improved ferrite number prediction in stainless steel arc welds using artificial neural networks.Part 1. Neural network development, Weld. J. 79 (2) (2000) 33–40.[9] J.M. Vitek, Y.S. Iskander, E.M. Oblow, Improved ferrite number prediction in stainless steel arc welds using artificial neural networks.Part 2. Neural network development, Weld. J. 79 (2) (2000) 41–50.[10] D.J.C. Mackay, Bayesian non-linear modeling with neural networks,in: H. Cerjack (Ed.), Mathematical Modeling of Weld Phenomena,vol. 3, The Institute of Materials, London, 1997, pp. 359–389.[11] C.N. McCowan, T.A. Siewert, D.L. Olson, Stainless steel weld metal:predictionof ferrite content, WRC Bull. 342 (1989) 1–36.[12] D.J.C. MacKay, A practical Bayesian framework for backpropagation networks, Neural Comput. 3 (1992) 448–472.[13] M. Vasudevan, M. Murugananth, A.K. Bhaduri, Application of Bayesian neural network for modeling and prediction of FN in austenitic stainless steel welds, in: H. Cerjak, H.K.D.H. Bhadeshia (Eds.), Mathematical Modelling of Weld Phenomena—VI, Institute of Materials, 2002, pp. 1079–1099.[14] E.R. Szumachowski, D.J. Kotechi, Effect of manganese on stainless steel weld metal ferrite, Weld. J. 63 (5) (1984) 156s–161s.[15] M. Vasudevan, A.K. Bhaduri, B. Raj, K. Prasad Rao, Bayesian neural network analysis of the compositional variations on the ferrite number in 316LN austenitic stainless steel welds, Trans. Ind. Ins.Met. 55 (5) (2002) 389–396.中文翻译利用神经网络预测与其他预测方法对δ铁素体不锈焊缝的分析和比较摘要能够预测不锈钢焊缝中δ铁素体含量的重要性有很多原因。
材料成型及控制工程外文文献翻译
本科毕业论文外文文献及译文文献、资料题目：The effects of heat treatment onthe microstructure and mechani-cal property of laser melting dep-ositionγ-TiAl intermetallic alloys 文献、资料来源：Materials and Design文献、资料发表（出版）日期：2009.10.25院（部）：材料科学与工程学院专业：材料成型及控制工程班级：姓名：学号：指导教师：翻译日期：2011.4.3中文译文：热处理对激光沉积γ-TiAl金属间化合物合金的组织与性能的影响摘要：Ti-47Al-2.5V-1Cr 和Ti-40Al-2Cr (at.%)金属间化合物合金通过激光沉积（LMD）成形技术制造。
显微组织的特征通过光学显微镜（OM）、扫描电子显微镜（SEM）、投射电子显微镜（TEM）、和X射线衍射仪（XRD）检测。
沿轴向评估热处理后的沉积试样室温下的抗拉性能和维氏硬度。
结果表明：由γ-TiAl 和α2-Ti3Al构成的γ-TiAl基体试样具有全密度柱状晶粒和细的层状显微组织。
Ti-47Al-2.5V-1Cr基体合金和Ti-40Al-2Cr基体合金沿轴向的室温抗拉强度大约分别为650 MPa、600MPa，而最大延伸率大约为0.6% 。
热处理后的Ti-47Al-2.5V-1Cr和Ti-40Al-2Cr合金可以得到不同的显微组织。
应力应变曲线和次表面的拉伸断裂表明沉积和热处理后的试样的断裂方式是沿晶断裂。
1.简介金属间化合物γ-TiAl合金由于其高熔点（﹥1450℃）、低密度（3g/cm3）、高弹性模量（160-180GPa）和高蠕变强度（直到900℃）成为很有前景的高温结构材料，一直受到广泛研究[1–4]。
但是对于其结构应用来说，这种材料主要缺点之一是在室温下缺少延展性。
此外，这种合金运用传统的制造工艺诸如锻压、轧制和焊接，加工起来比较困难[5]。
高分子材料纳米二氧化硅外文文献翻译
纳米二氧化硅对成核、结晶和热塑性能的影响外文文献翻译(含：英文原文及中文译文)文献出处：Laoutid F, Estrada E, Michell R M, et al. The influence of nanosilica on the nucleation, crystallization andtensile properties of PP–PC and PP–PA blends[J]. Polymer, 2013, 54(15):3982-3993.英文原文The influence of nanosilica on the nucleation, crystallization andtensileproperties of PP–PC and PP–PA blendsLaoutid F, Estrada E, Michell R M, et alAbstractImmiscible blends of 80 wt% polypropylene (PP) with 20 wt% polyamide (PA) or polycarbonate (PC) were prepared by melt mixing with or without the addition of 5% nanosilica. The nanosilica produced a strong reduction of the disperse phase droplet size, because of its preferential placement at the interface, as demonstrated by TEM. Polarized Light Optical microscopy (PLOM) showed that adding PA, PC or combinations of PA-SiO2 or PC-SiO2 affected the nucleation density of PP. PA droplets can nucleate PP under isothermal conditions producing a higher nucleation density than the addition of PC or PC-SiO2. PLOM was found to be more sensitive to determine differences in nucleation than non-isothermal DSC. PP developed spherulites, whose growth was unaffected by blending, while its overall isothermal crystallizationkinetics was strongly influenced by nucleation effects caused by blending. Addition of nanosilica resulted in an enhancement of the strain at break of PP-PC blends whereas it was observed to weaken PP-PA blends. Keywords：Nanosilica，Nucleation，PP blends1 OverviewImmiscible polymer blends have attracted attention for decades because of their potential application as a simple route to tailor polymer properties. The tension is in two immiscible polymerization stages. This effect usually produces a transfer phase between the pressures that may allow the size of the dispersed phase to be allowed, leading to improved mixing performance.Block copolymers and graft copolymers, as well as some functional polymers. For example, maleic anhydride grafted polyolefins act as compatibilizers in both chemical affinities. They can reduce the droplet volume at the interface by preventing the two polymers from coalescing. In recent years, various studies have emphasized that nanofillers, such as clay carbon nanotubes and silica, can be used as a substitute for organic solubilizers for incompatible polymer morphology-stabilized blends. In addition, in some cases, nanoparticles in combination with other solubilizers promote nanoparticle interface position.The use of solid particle-stabilized emulsions was first discovered in 1907 by Pickering in the case of oil/emulsion containing colloidalparticles. In the production of so-called "Pickling emulsions", solid nanoparticles can be trapped in the interfacial tension between the two immiscible liquids.Some studies have attempted to infer the results of blending with colloidal emulsion polymer blends. Wellman et al. showed that nanosilica particles can be used to inhibit coalescence in poly(dimethylsiloxane)/polyisobutylene polymers. mix. Elias et al. reported that high-temperature silicon nanoparticles can migrate under certain conditions. The polypropylene/polystyrene and PP/polyvinyl acetate blend interfaces form a mechanical barrier to prevent coalescence and reduce the size of the disperse phase.In contrast to the above copolymers and functionalized polymers, the nanoparticles are stable at the interface due to their dual chemical nature. For example, silica can affect nanoparticle-polymer affinities locally, minimizing the total free energy that develops toward the system.The nanofiller is preferentially placed in equilibrium and the wetting parameters can be predicted and calculated. The difference in the interfacial tension between the polymer and the nanoparticles depends on the situation. The free-diffusion of the nanoparticle, which induces the nanoparticles and the dispersed polymer, occurs during the high shear process and shows that the limitation of the viscosity of the polymer hardly affects the Brownian motion.As a result, nanoparticles will exhibit strong affinity at the local interface due to viscosity and diffusion issues. Block copolymers need to chemically target a particular polymer to the nanoparticle may provide a "more generic" way to stabilize the two-phase system.Incorporation of nanosilica may also affect the performance of other blends. To improve the distribution and dispersion of the second stage, mixing can produce rheological and material mechanical properties. Silica particles can also act as nucleating agents to influence the crystallization behavior. One studies the effect of crystalline silica on crystalline polystyrene filled with polybutylene terephthalate (polybutylene terephthalate) fibers. They found a stable fibril crystallization rate by increasing the content of polybutylene terephthalate and silica. On the other hand, no significant change in the melt crystallization temperature of the PA was found in the PA/ABS/SiO2 nanocomposites.The blending of PP with engineering plastics, such as polyesters, polyamides, and polycarbonates, may be a useful way to improve PP properties. That is, improving thermal stability, increasing stiffness, improving processability, surface finish, and dyeability. The surface-integrated nano-silica heat-generating morphologies require hybrid compatibilization for the 80/20 weight ratio of the thermal and tensile properties of the blended polyamide and polypropylene (increasedperformance). Before this work, some studies [22] that is, PA is the main component). This indicates that the interfacially constrained hydrophobic silica nanoparticles obstruct the dispersed phase; from the polymer and allowing a refinement of morphology, reducing the mixing scale can improve the tensile properties of the mixture.The main objective of the present study was to investigate the effect of nanosilica alone on the morphological, crystalline, and tensile properties of mixtures of nanosilica alone (for mixed phases with polypropylene as a matrix and ester as a filler. In particular, PA/PC or PA/nano The effect of SiO 2 and PC/nanosilica on the nucleation and crystallization effects of PP as the main component.We were able to study the determination of the nucleation kinetics of PP and the growth kinetics of the particles by means of polarization optical microscopy. DSC measures the overall crystallization kinetics.Therefore, a more detailed assessment of the nucleation and spherulite growth of PP was performed, however, the effect of nanosilica added in the second stage was not determined. The result was Akemi and Hoffman. And Huffman's crystal theory is reasonable.2 test phase2.1 Raw materialsThe polymer used in this study was a commercial product: isotactic polypropylene came from a homopolymer of polypropylene. The Frenchformula (B10FB melt flow index 2.16Kg = 15.6g / 10min at 240 °C) nylon 6 from DSM engineering plastics, Netherlands (Agulon Fahrenheit temperature 136 °C, melt flow index 240 °C 2.16kg = 5.75g / 10min ) Polycarbonate used the production waste of automotive headlamps, its melt flow index = 5g / 10min at 240 °C and 2.16kg.The silica powder TS530 is from Cabot, Belgium (about 225 m/g average particle (bone grain) about 200-300 nm in length, later called silica is a hydrophobic silica synthesis of hexamethyldisilane by gas phase synthesis. Reacts with silanols on the surface of the particles.2.2 ProcessingPP_PA and PP-PC blends and nanocomposites were hot melt mixed in a rotating twin screw extruder. Extrusion temperatures range from 180 to 240 °C. The surfaces of PP, PA, and PC were vacuumized at 80°C and the polymer powder was mixed into the silica particles. The formed particles were injected into a standard tensile specimen forming machine at 240C (3 mm thickness of D638 in the American Society for Testing Materials). Prior to injection molding, all the spherulites were in a dehumidified vacuum furnace (at a temperature of 80°C overnight). The molding temperature was 30°C. The mold was cooled by water circulation. The mixture of this combination is shown in the table.2.3 Feature Description2.31 Temperature Performance TestA PerkineElmer DSC diamond volume thermal analysis of nanocomposites. The weight of the sample is approximately 5 mg and the scanning speed is 20 °C/min during cooling and heating. The heating history was eliminated, keeping the sample at high temperature (20°C above the melting point) for three minutes. Study the sample's ultra-high purity nitrogen and calibrate the instrument with indium and tin standards.For high temperature crystallization experiments, the sample cooling rate is 60°C/min from the melt directly to the crystal reaching the temperature. The sample is still three times longer than the half-crystallization time of Tc. The procedure was deduced by Lorenzo et al. [24] afterwards.2.3.2 Structural CharacterizationScanning electron microscopy (SEM) was performed at 10 kV using a JEOL JSM 6100 device. Samples were prepared by gold plating after fracture at low temperature. Transmission electron microscopy (TEM) micrographs with a Philips cm100 device using 100 kV accelerating voltage. Ultra-low cut resection of the sample was prepared for cutting (Leica Orma).Wide-Angle X-Ray Diffraction Analysis The single-line, Fourier-type, line-type, refinement analysis data were collected using a BRUKER D8 diffractometer with copper Kα radiation (λ = 1.5405A).Scatter angles range from 10o to 25°. With a rotary step sweep 0.01° 2θ and the step time is 0.07s. Measurements are performed on the injection molded disc.This superstructure morphology and observation of spherulite growth was observed using a Leica DM2500P polarized light optical microscope (PLOM) equipped with a Linkam, TP91 thermal stage sample melted in order to eliminate thermal history after; temperature reduction of TC allowed isothermal crystallization to occur from the melt. The form is recorded with a Leica DFC280 digital camera. A sensitive red plate can also be used to enhance contrast and determine the birefringence of the symbol.2.3.3 Mechanical AnalysisTensile tests were carried out to measure the stretch rate at 10 mm/min through a Lloyd LR 10 K stretch bench press. All specimens were subjected to mechanical tests for 20 ± 2 °C and 50 ± 3% relative humidity for at least 48 hours before use. Measurements are averaged over six times.3 results3.1 Characterization by Electron MicroscopyIt is expected that PP will not be mixed with PC, PA because of their different chemical properties (polar PP and polar PC, PA) blends with 80 wt% of PP, and the droplets and matrix of PA and PC are expectedmorphologies [ 1-4] The mixture actually observed through the SEM (see Figures 1 a and b).In fact, because the two components have different polar mixtures that result in the formation of an unstable morphology, it tends to macroscopic phase separation, which allows the system to reduce its total free energy. During shearing during melting, PA or PP is slightly mixed, deformed and elongated to produce unstable slender structures that decompose into smaller spherical nodules and coalesce to form larger droplets (droplets are neat in total The size of the blend is 1 ~ 4mm.) Scanning electron microscopy pictures and PP-PC hybrid PP-PA neat and clean display left through the particle removal at cryogenic temperatures showing typical lack of interfacial adhesion of the immiscible polymer blend.The addition of 5% by weight of hydrophobic silica to the LED is a powerful blend of reduced size of the disperse phase, as can be observed in Figures 1c and D. It is worth noting that most of the dispersed phase droplets are within the submicron range of internal size. The addition of nano-SiO 2 to PA or PC produces finer dispersion in the PP matrix.From the positional morphology results, we can see this dramatic change and the preferential accumulation at the interface of silica nanoparticles, which can be clearly seen in FIG. 2 . PP, PA part of the silicon is also dispersed in the PP matrix. It can be speculated that thisformation of interphase nanoparticles accumulates around the barrier of the secondary phase of the LED, thus mainly forming smaller particles [13, 14, 19, 22]. According to fenouillot et al. [19] Nanoparticles are mixed in a polymer like an emulsifier; in the end they will stably mix. In addition, the preferential location in the interval is due to two dynamic and thermodynamic factors. Nanoparticles are transferred to the preferential phase, and then they will accumulate in the interphase and the final migration process will be completed. Another option is that there isn't a single phase of optimization and the nanoparticles will be set permanently in phase. In the current situation, according to Figure 2, the page is a preferential phase and is expected to have polar properties in it.3.2 Wide-angle x-ray diffractionThe polymer and silica incorporate a small amount of nanoparticles to modify some of the macroscopic properties of the material and the triggered crystal structure of PP. The WAXD experiment was performed to evaluate the effect of the incorporation of silica on the crystalline structure of the mixed PP.Isotactic polypropylene (PP) has three crystalline forms: monoclinic, hexagonal, and orthorhombic [25], and the nature of the mechanical polymer depends on the presence of these crystalline forms. The metastable B form is attractive because of its unusual performance characteristics, including improved impact strength and elongation atbreak.The figure shows a common form of injection molding of the original PP crystal, reflecting the appearance at 2θ = 14.0, 16.6, 18.3, 21.0 and 21.7 corresponding to (110), (040), (130), (111) and (131) The face is an α-ipp.20% of the PA incorporation into PP affects the recrystallization of the crystal structure appearing at 2θ = 15.9 °. The corresponding (300) surface of the β-iPP crystal appears a certain number of β-phases that can be triggered by the nucleation activity of the PA phase in PP (see evidence The following nucleation) is the first in the crystalline blend of PA6 due to its higher crystallization temperature. In fact, Garbarczyk et al. [26] The proposed surface solidification caused by local shear melts the surface of PA6 and PP and forms during the injection process, promoting the formation of β_iPP. According to quantitative parameters, KX (Equation (1)), which is commonly used to evaluate the amount of B-crystallites in PP including one and B, the crystal structure of β-PP has 20% PP_PA (110), H(040) and Blends of H (130) heights (110), (040) and (130). The height at H (300) (300) for type A peaks.However, the B characteristic of 5 wt% silica nanoparticles incorporated into the same hybrid LED eliminates reflection and reflection a-ipp retention characteristics. As will be seen below, the combination of PA and nanosilica induces the most effective nucleatingeffect of PP, and according to towaxd, this crystal formation corresponds to one PP structure completely.The strong reductive fracture strain observations when incorporated into polypropylene and silica nanoparticles (see below) cannot be correlated to the PP crystal structure. In fact, the two original PP and PP_PA_SiO2 hybrids contain α_PP but the original PP has a very high form of failure when the strain value.On the other hand, PP-PC and PP-PC-Sio 2 blends, through their WAXD model, can be proven to contain only one -PP form, which is a ductile material.3.3 Polarized Optical Microscopy (PLOM)To further investigate the effect of the addition of two PAs, the crystallization behavior of PC and silica nanoparticles on PP, the X-ray diffraction analysis of its crystalline structure of PP supplements the study of quantitative blends by using isothermal kinetic conditions under a polarizing microscope. The effect of the composition on the nucleation activity of PP spherulite growth._Polypropylene nucleation activityThe nucleation activity of a polymer sample depends on the heterogeneity in the number and nature of the samples. The second stage is usually a factor in the increase in nucleation density.Figure 4 shows two isothermal crystallization temperatures for thePP nucleation kinetics data. This assumes that each PP spherulite nucleates in a central heterogeneity. Therefore, the number of nascent spherulites is equal to the number of active isomerous nuclear pages, only the nucleus, PP-generated spherulites can be counted, and PP spherulites are easily detected. To, while the PA or PC phases are easily identifiable because they are secondary phases that are dispersed into droplets.At higher temperatures (Fig. 4a), only the PP blend inside is crystallized, although the crystals are still neat PP amorphous at the observed time. This fact indicates that the second stage of the increase has been able to produce PP 144 °C. It is impossible to repeat the porous experiment in the time of some non-homogeneous nucleation events and neat PP exploration.The mixed PP-PC and PP-PC-SiO 2 exhibited relatively low core densities at 144 °C, (3 105 and 3 106 nuc/cm 3) suggesting that either PC nanosilica can also be considered as good shape Nuclear agent is used here for PP.On the other hand, PA, himself, has produced a sporadic increase in the number of nucleating events in PP compared to pure PP, especially in the longer crystallization time (>1000 seconds). In the case of the PP-PA _Sio 2 blend, the heterogeneous nucleation of PP is by far the largest of all sample inspections. All the two stages of the nucleating agent combined with PA and silica are best employed in this work.In order to observe the nucleation of pure PP, a lower crystallization temperature was used. In this case, observations at higher temperatures found a trend that was roughly similar. The neat PP and PP-PC blends have small nucleation densities in the PP-PC-SiO 2 nanocomposite and the increase also adds further PP-PA blends. The very large number of PP isoforms was rapidly activated at 135°C in the PP-PA nanoparticle nanometer SiO 2 composites to make any quantification of their numbers impossible, so this mixed data does not exist from Figure 4b.The nucleation activity of the PC phase of PP is small. The nucleation of any PC in PP can be attributed to impurities that affect the more complex nature of the PA from the PC phase. It is able to crystallize at higher temperatures than PP, fractional crystallization may occur and the T temperature is shifted to much lower values (see References [29-39]. However, as DSC experiments show that in the current case The phase of the PA is capable of crystallizing (fashion before fractionation) the PP matrix, and the nucleation of PP may have epitaxy origin.The material shown in the figure represents a PLOAM micrograph. Pure PP has typical α-phase negative spherulites (Fig. 5A) in the case of PP-PA blends (Fig. 5B), and the PA phase is dispersed with droplets of size greater than one micron (see SEM micrograph, Fig. 1) . We could not observe the spherulites of the B-phase type in PP-PA blends. Even according to WAXD, 20% of them can be formed in injection moldedspecimens. It must be borne in mind that the samples taken using the PLOAM test were cut off from the injection molded specimens but their thermal history (direction) was removed by melting prior to melting for isothermal crystallization nucleation experiments.The PA droplets are markedly enhanced by the nucleation of polypropylene and the number of spherulites is greatly increased (see Figures 4 and 5). Simultaneously with the PP-PA blend of silica nanoparticles, the sharp increase in nucleation density and Fig. 5C indicate that the size of the spherulites is very small and difficult to identify.The PP-PC blends showed signs of sample formation during the PC phase, which was judged by large, irregularly shaped graphs. Significant effects: (a) No coalesced PC phase, now occurring finely dispersed small droplets and (B) increased nucleation density. As shown in the figure above, nano-SiO 2 tends to accumulate at the interface between the two components and prevent coalescence while promoting small disperse phase sizes.From the nucleation point of view, it is interesting to note that it is combined with nanosilica and as a better nucleating agent for PP. Combining PCs with nanosilica does not produce the same increase in nucleation density.Independent experiments (not shown here) PP _ SiO 2 samplesindicate that the number of active cores at 135 °C is almost the same as that of PP-PC-SiO2 intermixing. Therefore, silica cannot be regarded as a PP nucleating agent. Therefore, the most likely explanation for the results obtained is that PA is the most important reason for all the materials used between polypropylene nucleating agents. The increase in nucleation activity to a large extent may be due to the fact that these nanoparticles reduce the size of the PA droplets and improve its dispersion in the PP matrix, improving the PP and PA in the interfacial blend system. Between the regions. DSC results show that nano-SiO 2 is added here without a nuclear PA phase.4 Conclusion5% weight of polypropylene/hydrophobic nanosilica blended polyamide and polypropylene/polycarbonate (80E20 wt/wt) blends form a powerful LED to reduce the size of dispersed droplets. This small fraction of reduced droplet size is due to the preferential migration of silica nanoparticles between the phases PP and PA and PC, resulting in an anti-aggregation and blocking the formation of droplets of the dispersed phase.The use of optical microscopy shows that the addition of PA, the influence of PC's PA-Sio 2 or PC-Sio 2 combination on nucleation, the nucleation density of PP polypropylene under isothermal conditions is in the following approximate order: PP <PP-PC <PP -PC-SiO 2<<PP-PA<<< PP-PA-SiO 2. PA Drip Nucleation PP Production of nucleation densities at isothermal temperatures is higher than with PC or PC Sio 2D. When nanosilica is also added to the PP-PA blend, the dispersion-enhanced mixing of the enhanced nanocomposites yields an intrinsic factor PP-PA-Sio2 blend that represents a PA that is identified as having a high nucleation rate, due to nanoseconds Silicon oxide did not produce any significant nucleation PP. PLOAM was found to be a more sensitive tool than traditional cooling DSC scans to determine differences in nucleation behavior. The isothermal DSC crystallization kinetics measurements also revealed how the differences in nucleation kinetics were compared to the growth kinetic measurements.Blends (and nanocomposites of immiscible blends) and matrix PP spherulite assemblies can grow and their growth kinetics are independent. The presence of a secondary phase of density causes differences in the (PA or PC) and nanosilica nuclei. On the other hand, the overall isothermal crystallization kinetics, including nucleation and growth, strongly influence the nucleation kinetics by PLOAM. Both the spherulite growth kinetics and the overall crystallization kinetics were successfully modeled by Laurie and Huffman theory.Although various similarities in the morphological structure of these two filled and unfilled blends were observed, their mechanical properties are different, and the reason for this effect is currently being investigated.The addition of 5% by weight of hydrophobic nano-SiO 2 resulted in breaking the strain-enhancement of the PP-PC blend and further weakening the PP-PA blend.中文译文纳米二氧化硅对PP-PC和PP-PA共混物的成核，结晶和热塑性能的影响Laoutid F, Estrada E, Michell R M, et al摘要80(wt%)聚丙烯与20(wt %)聚酰胺和聚碳酸酯有或没有添加5%纳米二氧化硅通过熔融混合制备不混溶的共聚物。
材料加工专业毕业设计外文翻译
译文在通过集中的离子束的BK7玻璃上的表面特性的调查Yongqi Fu*,Ngoi Kok Ann Bryan,Wei Zhou,Dongzhu Xie,Lim Boon Hong摘要：在强烈离子束击下的BK7玻璃的表面特性被调查。
在击下形成的部分的形状与不同过程参数下形成的式不同的。
例如像离子入射的角度，离子量和返回的时间等。
通过我们的实验结果，我们发现被限定扫描地方的边界形状在钙离子轰击后也影响便面特征。
另外，BK7玻璃的视觉特性的一种即光的传导被测量并与白色底质相比较（在FIB轰击前后进行的）。
FIB轰击后的传导从深蓝紫色变为蓝紫色，并且反复的与FIB轰击以后的肉眼可见的区域和红色区域一起被展现。
这一现象的产生被在高压下产生离子能量的钙离子所导致离子的穿透深度和离子能量通过TRIM2000d的使用被积累。
关键字：－BK7 FIB 传导分布状态I.前言喷射的低能量离子束经常被用于1号深度的轮廓分析技术。
在某种情况下，一般对于中断的离子入射，在一个齿形或波浪型物质中一个周期振辐伴随着1微米厚度长度薄层在离子轰击时形成。
伴随着宽束离子轰击，齿形结构因为个别晶状的半导体物质和非晶状的半导体物质而被观察到（二氧化硅和被使用的石英）。
然而，它们既无规律性，也不连续，并且很难有实际用处。
BK7玻璃被普遍用于传统的显微镜系统。
关于这一物质，齿形波被发射的调查将有助于处理被用于光学系统力带有微米特征的光谱结构。
在这方面，我们发现另一种有色素，BK7,被一种带有30kev离子能量的一束强烈的离子束轰击。
我们设法去得到规则的。
直线型的，并且可被各种过程参数控制的齿形波，它们有望于被做为光栅。
因为离子入射的效应，BK7色素的传导在离子轰击后将被或多或少的改变。
利用FIB扫描的BK7玻璃表面状态的变化在被报告的原理模型这一主题中被第一次调查，然后BK7玻璃中这一色素的传导率被测定并且在钙离子增大积累的基础上它们的变化被发现。
材料科学专业毕业设计外文文献及翻译
材料科学专业毕业设计外文文献及翻译文献摘要为了适应不断发展的材料科学领域，毕业设计需要参考一些权威的外文文献。
在这里，我们提供了一些与材料科学专业相关的外文文献，并附带简要翻译。
---文献1: "石墨烯在材料科学中的应用"作者: John Smith, Mary Johnson: John Smith, Mary Johnson摘要::本文综述了石墨烯在材料科学中的应用。
石墨烯是一种单层碳原子结构，具有独特的物理和化学性质。
我们讨论了石墨烯的制备方法、其在电子学、能源存储和生物医学领域中的应用。
石墨烯在材料科学中具有巨大的潜力，可以为未来的材料研究和应用开辟新的道路。
---文献2: "纳米材料的合成与性能研究"作者: David Brown, Emma Lee: David Brown, Emma Lee摘要::本文讨论了纳米材料的合成方法及其性能研究。
纳米材料是具有纳米尺度结构的材料，具有与宏观材料不同的性质。
我们介绍了几种常见的纳米材料合成方法，例如溶液法和气相法，并讨论了纳米材料的晶体结构、表面性质和力学性能。
研究纳米材料的性能对材料科学的发展和应用具有重要意义。
---文献3: "高温合金的热稳定性研究"作者: Jennifer Zhang, Michael Wang: Jennifer Zhang, Michael Wang摘要::本文研究了高温合金的热稳定性。
高温合金是一种用于高温环境的特殊材料，具有优异的耐热性能。
我们通过实验研究了高温合金的热膨胀性、热导率和高温力学性能。
通过了解高温合金的热稳定性，我们可以提高材料的耐高温性能，从而推动高温环境下的应用和工程技术发展。
---以上是几篇关于材料科学的外文文献摘要及简要翻译，希望对毕业设计的参考有所助益。
材料英文翻译
介绍材料科学与工程专业的英语作文
介绍材料科学与工程专业的英语作文英文回答：Materials Science and Engineering (MSE) is an interdisciplinary field that combines the principles of physics, chemistry, biology, and engineering to design and develop new materials with tailored properties for specific applications. MSE plays a crucial role in various industries, including aerospace, automotive, energy, electronics, and healthcare.MSE professionals are responsible for researching, developing, and testing new materials, as well asoptimizing existing materials for improved performance.They apply their knowledge of material properties, such as strength, toughness, conductivity, and corrosion resistance, to create materials that meet specific requirements.The field of MSE is vast, encompassing a wide range of topics, such as:Materials Synthesis: This involves the development of techniques to produce new materials or modify existing ones with desired properties.Materials Characterization: Scientists and engineers employ advanced tools and techniques to analyze and characterize the properties of materials, including their chemical composition, microstructure, and physical behavior.Materials Modeling: Computational modeling and simulation techniques are used to predict and understandthe performance of materials under different conditions.Materials Processing: This involves the optimizationof processes used to transform raw materials into finished products, such as casting, forging, and machining.Materials Applications: MSE professionals collaborate with engineers and scientists from other disciplines to develop new materials for various applications, such as lightweight components for aerospace, energy-efficientcoatings for buildings, and biocompatible materials for medical devices.MSE is a dynamic and rapidly evolving field, driven by the constant demand for new and improved materials. Withits interdisciplinary nature and cutting-edge research, MSE professionals are poised to play a vital role in addressing global challenges and shaping the future of technology.中文回答：材料科学与工程。
复合材料注塑成型中英文对照外文翻译文献
复合材料注塑成型中英文对照外文翻译文献(文档含英文原文和中文翻译)An experimental study of the water-assisted injection molding ofglass fiber filled poly-butylene-terephthalate(PBT) compositesAbstract：The purpose of this report was to experimentally study the water-assisted injection molding process of poly-butylene-terephthalate(PBT) composites. Experiments were carried out on an 80-ton injection-molding machine equipped with a lab scale water injection system,which included a water pump, a pressure accumulator, a water injection pin, a water tank equipped with a temperature regulator,and a control circuit. The materials included virgin PBT and a 15% glass fiber filled PBT composite, and a plate cavity with a rib across center was used. Various processing variables were examined in terms of their influence on the length of water penetration in molded parts, and mechanical property tests were performed on these parts. X-ray diffraction (XRD) was also used to identify the material andstructural parameters. Finally, a comparison was made between water-assisted and gas-assisted injection molded parts. It was found that the melt fill pressure, melt temperature, and short shot size were the dominant parameters affecting water penetration behavior.Material at the mold-side exhibited a higher degree of crystallinity than that at the water-side. Parts molded by gas also showed a higher degree of crystallinity than those molded by water. Furthermore, the glass fibers near the surface of molded parts were found to be oriented mostly in the flow direction, but oriented substantially more perpendicular to the flow direction with increasing distance from the skin surface.Keywords: Water assisted injection molding; Glass fiber reinforced poly-butylene-terephthalate (PBT) composites; Processing parameters; B. Mechanical properties; Crystallinity; A. Polymer matrix composites;1. IntroductionWater-assisted injection molding technology [1] has proved itself a breakthrough in the manufacture of plastic parts due to its light weight, faster cycle time, and relatively lower resin cost per part. In the water-assisted injection molding process, the mold cavity is partially filled with the polymer melt followed by the injection of water into the core of the polymer melt. A schematic diagram of the water-assisted injection molding process is illustrated in Fig. 1.Water-assisted injection molding can produce parts incorporating both thick and thin sections with less shrink-age and warpage and with a better surface finish, but with a shorter cycle time. The water-assisted injection molding process can also enable greater freedom of design, material savings, weight reduction, and cost savings in terms of tooling and press capacity requirements [2–4]. Typical applications include rods and tubes, and large sheet-like structural parts with a built-in water channel network. On the other hand, despite the advantages associated with the process,the molding window and process control are more critical and difficult since additional processing parameters are involved. Water may also corrode the steel mold, and some materials including thermoplastic composites are difficult to mold successfully. The removal of water after molding is also a challenge for this novel technology. Table 1 lists the advantages and limitations of water-assisted injection molding technology.Fig. 1. Schematic diagram of water-assisted injection molding process.Water assisted injection molding has advantages over its better known competitor process, gas assisted injection molding [5], because it incorporates a shorter cycle time to successfully mold a part due to the higher cooling capacity of water during the molding process. The incompressibility,low cost, and ease of recycling the water makes it an ideal medium for the process. Since water does not dissolve and diffuse into the polymer melts during the molding process, the internal foaming phenomenon [6] that usually occurs in gas-assisted injection molded parts can be eliminated.In addition, water assisted injection molding provides a better capability of molding larger parts with a small residual wall thickness. Table 2 lists a comparison of water and gas assisted injection molding.With increasing demands for materials with improved performance, which may be characterized by the criteria of lower weight, higher strength, and a faster and cheaper production cycle time, the engineering of plastics is a process that cannot be ignored. These plastics include thermoplastic and thermoset polymers. In general, thermoplastic polymers have an advantage over thermoset polymers in popular materials in structural applications.Poly-butylene-terephthalate (PBT) is one of the most frequently used engineering thermoplastic materials, whichis formed by polymerizing 1.4 butylene glycol and DMT together. Fiber-reinforced composite materials have been adapted to improve the mechanical properties of neat plastic materials. Today, short glass fiber reinforced PBT is widely used in electronic, communication and automobile applications. Therefore, the investigation of the processing of fiber-reinforced PBT is becoming increasingly important[7–10].This report was made to experimentally study the waterassisted injection molding process of poly-butylene-terephthalate (PBT) materials. Experiments were carried out on an 80-ton injection-molding machine equipped with a lab scale water injection system, which included a water pump, a pressure accumulator, a water injection pin, a water tank equipped with a temperature regulator, and a control circuit. The materials included a virgin PBT and a 15% glass fiber filled PBT composite, and a plate cavity with a rib across center was used. Various processing variables were examined in terms of their influence on the length of water penetration in molded parts, which included melt temperature, mold temperature, melt filling speed, short-shot size, water pressure, water temperature,water hold and water injection delay time. Mechanical property tests were also performed on these molded parts,and XRD was used to identify the material and structural parameters. Finally, a comparison was made betweenwater-assisted and gas-assisted injection molded parts.Table 12. Experimental procedure2.1. MaterialsThe materials used included a virgin PBT (Grade 1111FB, Nan-Ya Plastic, Taiwan) and a 15% glass fiber filled PBT composite (Grade 1210G3, Nan-Ya Plastic, Taiwan).Table 3 lists the characteristics of the composite materials.2.2. Water injection unitA lab scale water injection unit, which included a water pump, a pressure accumulator, a water injection pin, a water tank equipped with a temperature regulator, and a control circuit, was used for all experiments [3]. An orifice-type water injection pin with two orifices (0.3 mm in diameter) on the sides was used to mold the parts. During the experiments, the control circuit of the water injection unit received a signal from the molding machine and controlled the time and pressure of the injected water. Before injection into the mold cavity, the water was stored in a tank with a temperature regulator for 30 min to sustain an isothermal water temperature.2.3. Molding machine and moldsWater-assisted injection molding experiments were conducted on an 80-ton conventional injection-molding machine with a highest injection rate of 109 cm3/s. A plate cavity with a trapezoidal water channel across the center was used in this study. Fig. 2 shows the dimensions ofthe cavity. The temperature of the mold was regulated by a water-circulating mold temperature control unit. Various processing variables were examined in terms of their influence on the length of water penetration in water channels of molded parts: melt temperature, mold temperature, meltfill pressure, water temperature and pressure, water injection delay time and hold time, and short shot size of the polymer melt. Table 4 lists these processing variables as well as the values used in the experiments.2.4. Gas injection unitIn order to make a comparison of water and gas-assisted injection molded parts, a commercially available gas injection unit (Gas Injection PPC-1000) was used for the gas assisted injection molding experiments. Details of the gas injection unit setup can be found in the Refs. [11–15].The processing conditions used for gas-assisted injection molding were the same as that of water-assisted injection molding (terms in bold in Table 4), with the exception of gas temperature which was set at 25 C.2.5. XRDIn order to analyze the crystal structure within the water-assisted injection-molded parts, wide-angle X-ray diffraction (XRD) with 2D detector analyses in transmission mode were performed with Cu Ka radiation at 40 kV and 40 mA. More specifically, the measurements were performed on the mold-side andwater-side layers of the water-assisted injection-molded parts, with the 2h angle ranging from 7 to 40 . The samples required for these analyses were taken from the center portion of these molded parts. To obtain the desired thickness for the XRD samples, the excess was removed by polishing theTable 3samples on a rotating wheel on a rotating wheel, first with wet silicon carbide papers, then with 300-grade silicon carbide paper, followed by 600- and 1200-grade paper fora better surface smoothness.2.6. Mechanical propertiesTensile strength and bending strength were measured on a tensile tester. Tensile tests were performed on specimens obtained from the water-assisted injection molded parts (see Fig. 3) to evaluate the effect of water temperature on the tensile properties. The dimensions of specimens forthe experiments were 30 mm · 10 mm · 1 mm. Tensile tests were performed in a LLOYD tensiometer according to the ASTM D638M test. A 2.5 kN load cell was used and the crosshead speed was 50 mm/min.Bending tests were also performed at room temperature on water-assisted injection molded parts. The bending specimens were obtained with a die cutter from parts (Fig. 3) subjected to various water temperatures.The dimensions of the specimens were 20 mm · 10 mm · 1 mm. Bending tests were performed in a micro tensile tester according to the ASTM D256 test. A 200 N load cell was used and the crosshead speed was 50 mm/min.2.7. Microscopic observationThe fiber orientation in molded specimens was observed under a scanning electron microscope (Jeol Model 5410).Specimens for observation were cut from parts molded by water-assisted injection molding across the thickness (Fig. 3). They were observed on the cross-section perpendicular to the flow direction. All specimen surfaces were gold sputtered before observation.3. Results and discussionAll experiments were conducted on an 80-ton conventional injection-moldingmachine, with a highest injection rate of 109 cm3/s. A plate cavity with a trapezoidal water channel across the center was used for all experimentsTable 4Fig. 3. Schematically, the positioning of the samples cut from the molded parts for tensile and bending tests and microscopic observations.3.1. Fingerings in molded partsAll molded parts exhibited the water fingering phenomenon at the channel to plate transition areas. In addition,molded glass fiber filled composites showed more severe water fingerings than those of non-filled materials, as shown photographically in Fig. 4. Fingerings usually form when a less dense, less viscous fluid penetrates a denser,more viscous fluid immiscible with it. Consider a sharp two phase interface or zone where density and viscosity change rapidly. The pressure force (P2 P1) on the displaced fluid as a result of a virtual displacement dx of the interface can be described by [16], where U is the characteristic velocity and K is the permeability.If the net pressure force is positive, then any small displacement will be amplified and lead to an instabilityand part fingerings. For the displacement of a dense, viscous fluid (the polymer melt) by a lighter, less viscous one (water), we can have Dl = l1 l2 > 0, and U > 0 [16].In this case, instability and the relevant fingering result when a more viscousfluid is displaced by a less viscous one, since the less viscous fluid has the greater mobility.The results in this study suggest that glass fiber filled composites exhibit a higher tendency for part fingerings. This might be due to the fact that the viscosity difference Dl between water and the filled composites is larger than the difference between water and the non-filled materials. Waterassisted injection molded composites thus exhibit more severe part fingerings.Fig. 4. Photograph of water-assisted injection molded PBT composite part.3.2. Effects of processing parameters on water penetrationVarious processing variables were studied in terms of their influence on the water penetration behavior. Table 4 lists these processing variables as well as the values used in the experiments. To mold the parts, one central processing condition was chosen as a reference (bold term in TableBy changing one of the parameters in each test, we were able to better understand the effect of each parameter on the water penetration behavior of water assisted injection molded composites. After molding, the length of water penetration was measured. Figs. 5–10 show the effects of these processing parameters on the length of water penetration in molded parts, including melt fill pressure, melt temperature, mold temperature, short shot size, water temperature, and water pressure.The experimental results in this study suggest that water penetrates further in virgin PBT than in glass fiber filled PBT composites. This is due to the fact that with the reinforcing glass fibers the composite materials have less volumetric shrinkage during the cooling process. Therefore,they mold parts with a shorter water penetration length.The length of water penetration decreases with the melt fill pressure (Fig. 5). This can be explained by the fact that increasing the melt fill pressure increases the flow resistance inside the mold cavity. It is then more difficult for the water to penetrate into the core of the materials. The length of water penetration decreases accordingly [3].The melt temperature was also found to reduce the water penetration in molded PBT composite parts (Fig. 6). This might be due to the fact that increasing the melt temperature decreases viscosity of the polymer melt.A lower viscosity of the materials helps the water to packthe water channel and increase its void area, instead of penetrating further into theparts [4]. The hollow core ratio at the beginning of the water channel increases and the length of water penetration may thus decrease.Increasing the mold temperature decreases somewhat the length of water penetration in molded parts (Fig. 7).This is due to the fact that increasing the mold temperature decreases the cooling rate as well as the viscosity of the materials. The water then packs the channel and increases its void area near the beginning of the water channel,instead of penetrating further into the parts [3]. Molded parts thus have a shorter water penetration length.Increasing the short shot size decreases the length of water penetration (Fig. 8). In water-assisted injection molding, the mold cavity is partially filled with the polymer melt followed by the injection of water into the core of the polymer melt [4]. Increasing the short shot size of the polymer melt will therefore decrease the length of water penetration in molded parts.For the processing parameters used in the experiments,increasing the water temperature (Fig. 9) or the water pressure(Fig. 10) increases the length of water penetration in molded parts. Increasing the water temperature decreases the cooling rate of the materials and keeps the polymer melt hot for a longer time; the viscosity of the materials decreases accordingly. This will help the water penetratefurther into the core of the parts [3]. Increasing the water pressure also helps the water penetrate into the materials.The length of water penetration thus increases.Finally, the deflection of molded parts, subjected to various processing parameters, was also measured by a profilemeter.The maximum measured deflection is considered as the part warpage. The result in Fig. 11 suggests that the part warpage decreases with the length of water penetration.This is due to the fact that the longer the water penetration,the more the water pressure can pack the polymeric materials against the mold wall. The shrinkage as well as the relevant part warpage decreases accordingly.Fig. 5. Effects of melt fill pressure on the length of water penetration in molded parts.Fig. 6. Effects of melt temperature on the length of water penetration in molded parts.Fig. 9. Effects of water temperature on the length of water penetration in moldedparts.Fig. 7. Effects of mold temperature on the length of water penetration in molded parts.Fig. 8. Effects of short shot size on the length of water penetration inmolded parts.Fig. 10. Effects of water pressure on the length of water penetration inmolded parts.3.3. Crystallinity of molded partsPBT is a semi-crystalline thermoplastic polyester with a high crystallization rate. In the water-assisted injection molding process, crystallization occurs under non-isothermal conditions in which the cooling rate varies with cooling time. Here the effects of various processing parameters(including melt temperature, mold temperature, and water temperature) on the level of crystallinity in molded parts were studied. Measurements were conducted ona wideangle X-ray diffraction (XRD) with 2D detector analyses(as described in Section 2). The measured results in Fig. 12 showed that all materials at the mold-side lay erexhibited a higher degree of crystallinity than those at the water-side layer. The result indicates that the water has a better cooling capacity than the mold during the cooling process. This matches our earlier finding [17] by measuring the in-mold temperature distribution. In addition, the experimental result in Fig. 12c also suggests that the crystallinity of the molded materials generally increases with the water temperature. This is due to the fact that increasing the water temperature decreases the cooling rate of the materials during the cooling process. Molded parts thus exhibited a higher level of crystallinity.On the other hand, to make a comparison of the crysallinity of parts molded by gas and water, gas-assisted injection molding experiments were carried out on the same injection molding machine as that used with water, but equipped with a high-pressure nitrogen gas injection unit [11–15]. The measured results in Fig. 13 suggests that gas-assisted injection molded parts have a higher degree of crystallinity than water-assisted injection mold parts.This is due to the fact that water has a higher cooling capacity and cools down the parts faster than gas. Parts molded by water thus exhibited a lower level of crystallinity than those molded by gas.Fig. 11. Measured warpage of molded parts decreases with the length of waterpenetration.3.4. Mechanical propertiesTensile tests were performed on specimens obtained from the water-assisted injection molded parts to examine the effect of water temperature on the tensile properties.Fig. 14 showed the measured decrease subjected to various water temperatures. As can be observed, both yield strength and the elongational strain at break of water assisted molded PBT materials decrease with the water temperature. On the other hand, bending tests were also performed at room temperature on water-assisted injection molded parts. The measured result in Fig. 15 suggests that the bending strength of molded parts decreases with the water temperature.Increasing the water temperature generally decreases the cooling rate and molds parts with higher level of crystallin-content of free volume and therefore an increasing level of stiffness. However, the experimental results here suggest that the quantitative contribution of crystallinity to PBT’s mechanical properties is negligible, while there is a more important quantitative increase of tensile and bending strength for the PBT materials.The mechanical properties of molded materials are dependent on both the amount and the type of crystalline regions developed during processing.The fact that the ductility of PBT decreases with the degree of crystallinity may indicate that a more crystalline and stiffer PBT developed at a lower cooling rate during processing and did not exhibit higher stress values in tensile tests because of a lack of ductility, and therefore did not behave as strong as expected from their stiffness [18]. Nevertheless,more detailed experiments will be needed for the future works to investigate the morphological parameters of water-assisted injection molded parts and their correlation with the parts’ mechanical properties.3.5. Fiber orientation in molded partsSmall specimens were cut out from the middle of molded parts in order to observe their fiber orientation. The position of the specimen for the fiber orientation observation is as shown in Fig. 3. All specimen surfaces were polished and gold sputtered before observation. Fig. 16 shows the microstructure of the water-assisted injection molded composite parts. The measured result suggests that the fiber orientation distribution in water-assisted injection molded parts is quite different from that of conventional injection ity. As is usually encountered in semi-crystalline thermoplastics,a higher degree of crystallization means a lower molded parts.In conventional injection molded parts, two regions are usually observed: the thin skin and the core. In the skin region near the wall, all fibers are oriented parallel to the injection molding, water-assisted injection molding technology is different in the way the mold is filled. With a conventional injection molding machine, one cycle is characterized by the phases of filling, packing and cooling.In the water-assisted injection molding process, the mold cavity is partially filled with the polymer melt followed by the injection of water into the core of the polymer melt.The novel filling process influences the orientation of fibers and matrix in a part significantly.From Fig. 16, the fiber orientation in water-assisted injection molded parts can be approximately divided intothree zones. In the zone near the mold-side surface where the shear is more severe during the mold filling, fibers are principally parallel. For the zone near the water-side surface,the shear is smaller and the velocity vector greater.In this case, the fiber tends to be positioned more transversely in the direction of injection. At the core, the fibers tend to be oriented more randomly. Generally speaking,the glass fibers near the mold-side surface of molded parts were found to be oriented mostly in the flow direction, and oriented substantially perpendicular to the flow direction with increasing distance from the mold-side surface.Finally, it should be noted that a quantitative comparison of morphology and fiber orientation [21] in waterassisted molded and conventional injection molded parts will be made by our lab in future works.Fig. 16. Fiber orientation across the thickness of water-assisted injection molded PBTcomposites.4. ConclusionsThis report was made to experimentally study the water-assisted injection molding process of poly-butylene-terephthalate(PBT) composites. The following conclusions can be drawn based on the current study.1. Water-assisted injection molded PBT parts exhibit the fingering phenomenon at the channel to plate transition areas. In addition, glass fiber filled composites exhibit more severe water fingerings than those of non-filled materials.2. The experimental results in this study suggest that the length of water penetration in PBT composite materials increases with water pressure and temperature, and decreases with melt fill pressure, melt temperature, and short shot size.3. Part warpage of molded materials decreases with the length of water penetration.4. The level of crystallinity of molded parts increases with the water temperature. Parts molded by water show a lower level of crystallinity than those molded by gas.5. The glass fibers near the surface of molded PBT composite parts were found to be oriented mostly in the flow direction, and oriented substantially perpendicular to the flow direction with increasing distance from the skin surface.玻璃纤维增强复合材料水辅注塑成型的实验研究摘要：本报告的目的是通过实验研究聚对苯二甲酸丁二醇复合材料水辅注塑的成型工艺。
不锈钢工程材料的选择 外文翻译
Unit 1 MetalsThe use of metals has always been a key factor in the development of the social systems of man. Of the roughly 100 basic elements of which all matter is composed, about half are classified as metals. The distinction between a metal and a nonmetal is not always clear-cut. The most basic definition centers around the type of bonding existing between the atoms of the element, and around the characteristics of certain of the electrons associated with these atoms. In a more practical way, however, a metal can be defined as an element which has a particular package of properties.Metals are crystalline when in the solid state and, with few exceptions (e.g. mercury), are solid at ambient temperatures. They are good conductors of heat and electricity and are opaque to light. They usually have a comparatively high density. Many metals are ductile-that is, their shape can be changed permanently by the application of a force without breaking. The forces required to cause this deformation and those required to break or fracture a metal are comparatively high, although, the fracture forces is not nearly as high as would be expected from simple consideration of the forces required to tear apart the atoms of the metal.One of the more significant of these characteristics from our point of view is that of crystallinity. A crystalline solid is one in which the constituent atoms are located in a regular three-dimensional array as if they were located at the corners of the squares of a three-dimensional chessboard. The spacing of the atoms in the array is of the same order as the size of the atoms, the actual spacing being a characteristic of the particular metal. The directions of the axes of the array define the orientation of the crystal in space. The metals commonly used in engineering practice are composed of a large number of such crystals, called grains. In the most general case, the crystals of the various grains are randomly oriented in space. The grains are everywhere in intimate contact with one another and joined together on an atomic scale. The region at which they join is known as a grain boundary.An absolutely pure metal (i.e. one composed of only one type of atom) has never been produced. Engineers would not be particularly interested in such a metal even if it were to be produced, because it would be soft and weak. The metals used commercially inevitably contain small amounts of one or more foreign elements, either metallic or nonmetallic. These foreign elements may be detrimental, they may be beneficial, or they may have no influence at all on a particular property. If disadvantageous, the foreign elements tend to be known as impurities. If advantageous, they tend to be known as alloying elements. Alloying elements are commonly added deliberately in substantial amounts in engineering materials. The result is known as an alloy.The distinction between the descriptors “metal〞and “alloy〞is not clear-cut. The term “metal〞may be used to encompass both a commercially pure metal and its alloys. Perhaps it can be said that the more deliberately an alloying addition has been made and the larger the amount of the addition, the more likely it is that the product will specifically be called an alloy. In any event, the chemical composition of a metal or an alloy must be known and controlled within certain limits if consistent performance is to be achieved in service. Thus chemical composition has to be taken into account when developing an understanding of the factors which determine the properties of metals and their alloys.Of the 50 or so metallic elements, only a few are produced and used in large quantities inengineering practice. The most important by far is iron, on which are based the ubiquitous steels and cast irons (basically alloys of iron and carbon). They account for about 98% by weight of all metals produced. Next in importance for structural uses (that is, for structures that are expected to carry loads) are aluminum, copper, nickel, and titanium. Aluminum accounts for about 0.8% by weight of all metals produced, and copper about 0.7%, leaving only 0.5% for all other metals. As might be expected, the remainders are all used in rather special applications. For example, nickel alloys are used principally in corrosion-and heat-resistant applications, while titanium is used extensively in the aerospace industry because its alloys have good combinations of high strength and low density. Both nickel and titanium are used in high-cost, high-quality applications, and, indeed, it is their high cost that tends to restrict their application.We cannot discuss these more esoteric properties here. Suffice it to say that a whole complex of properties in addition to structural strength is required of an alloy before it will be accepted into, and survive in, engineering practice. It may, for example, have to be strong and yet have reasonable corrosion resistance; it may have to be able to be fabricated by a particular process such as deep drawing, machining, or welding; it may have to be readily recyclable; and its cost and availability may be of critical importance.翻译如下：第一单元金属在人类社会的开展中，金属的应用起着关键性的作用。
材料外文翻译
材料外文翻译In recent years, with the rapid development of globalization, the demand for material foreign language translation has been increasing. Material foreign language translation refers to the translation of various documents, including but not limited to technical documents, legal documents, business documents, and academic papers, from one language to another.The importance of material foreign language translation lies in its ability to bridge the communication gap between different language speakers. In the globalized world, businesses, organizations, and individuals often need to communicate and collaborate with people from different linguistic backgrounds. Material foreign language translation enables them to understand and convey information accurately and effectively, thus facilitating international cooperation and exchange.Moreover, material foreign language translation plays a crucial role in the dissemination of knowledge and information. Many academic and scientific research findings are published in languages other than English, and translating these materials into English can make them accessible to a wider audience. This not only promotes the exchange of ideas and knowledge across borders but also contributes to the advancement of global research and development.When it comes to material foreign language translation, accuracy is of paramount importance. A slight mistranslation can lead to misunderstandings, confusion, or even legal disputes. Therefore, professional translators with expertise in the specific subject matter are often required to ensure the accuracy and precision of the translation.In addition to accuracy, the readability and fluency of the translated materials are also crucial. A well-translated document should not only convey the original meaning faithfully but also be easy to understand and natural-sounding in the target language. This requires translators to have a deep understanding of both the source and target languages, as well as the cultural nuances and context in which the materials are used.With the advancement of technology, the process of material foreign language translation has been greatly facilitated. Translation software, machine translation, and other tools have made the translation process more efficient and cost-effective. However, it is important to note that while these tools can be helpful, they cannot replace the expertise and judgment of human translators, especially when it comes to complex or specialized materials.In conclusion, material foreign language translation is an essential component of global communication and knowledge dissemination. It enables people from different linguistic backgrounds to understand and share information, facilitates international cooperation, and contributes to the advancement of research and development. As the demand for material foreign language translation continues to grow, it is crucial to recognize the importance of accuracy, readability, and the expertise of professional translators in ensuring the quality of translated materials.。
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温拌沥青混合料加工性的可操作性摘要：为了客观评价的温拌沥青混合料的可操作性，已通过沥青混合料加工性测试仪对温拌沥青混合料的可加工性能进行了测试。
最初，只是利用可行性设备对沥青砂浆进行了分析。
相对于热混合沥青来说，温拌沥青混合料的加工性是由不同的温度和频率决定的。
测验结果表明，当温拌沥青混合料的混合温度降低30℃时，它的加工性与热混合沥青的加工性是一样的。
1、介绍随着能源的日益枯竭和人们环保意识的增强，温拌沥青混合料作为一种新的沥青混合料应运而生。
温拌沥青混合料是一种新型材料，其混合温度处于热混合沥青和冷沥青混合料之间，它的道路修筑性能可以接近甚至超过热混合沥青。
与热混合沥青相比，温拌沥青混合料的拌和温度可降低约30〜60℃，这样可以降低大约30％的能源消耗，有害气体和粉尘排放量也将减少。
温拌混合技术具有节能、减排的作用，并且有益于施工人员的身体健康，其具有非常重要的意义。
最初的加工性是用来评估水泥混凝土的流动性的指标。
然而沥青混合料的加工问题也是存在的，者主要是由于沥青混合困难。
到目前为止，对沥青混合料的加工性能研究比较少。
我们都知道，随着温度的降低，沥青的粘合剂逐渐增大，流动性变差，因此，沥青混合料将难以混合，即加工性会恶化，以致会影响沥青混合料的铺设和压实。
因此，混合温暖的沥青混合物的温度降低，必须保证其良好的加工性，否则会造成在施工和混凝土压实困难。
到目前为止，还没有具体用来评价沥青混合料的加工性的指标。
为了研究与沥青混合料混合的添加剂在低混合温度是否具有良好的加工性，沥青混合料的加工性测试仪已经对温拌沥青混合料的可操作性进行了研究，并与热混合沥青的加工性进行了比较。
旨在确定温拌沥青料的合理温度范围。
2、实验原理及设备2.1、测试原理在沥青混合料的搅拌过程中，沥青搅拌锅的搅拌齿会被沥青混合料磨损阻碍。
对于不同的沥青混合料，其粘度是不同的，搅拌的牙齿接收到的电阻也是不一样的。
如果对混合物仅进行混合温度降低而不采取任何其他措施，就会使搅拌混合物的搅拌齿的的接收功率提高，即作业性较差。
基于这一理念，本文开发研究了沥青混合料的加工性测试仪。
通过从沥青混合料的混合过程中采集搅拌齿扭矩的大小，可以判断混合物的加工性。
并在低混合温度下，就温拌沥青混合料与热混合沥青的加工性的区别进行了研究。
2.2、测试设备本研究采用沥青混合料的加工性能测试仪进行测试。
对加工性测试仪器与普通沥青混合料搅拌锅进行了简单的改进与处理。
安装在普通沥青混合锅中搅拌齿的扭矩传感器可以在搅拌下齿的旋转过程中得到转矩的大小，并且扭矩可以测量混合沥青混合料的难度，这就是可加工性。
如果扭矩很大则表明混合物的可加工性很差，反之，则好。
为了研究不同搅拌速度对加工性的影响，在频率变化0〜50Hz的范围内安装频率转换器，该设备可以改变搅拌齿的旋转速率。
此外，还安装了相应数据采集的软件。
沥青混合料的使用操作性测试仪示于图1。
图1 沥青混合料的加工性能测试仪3、测试原料.3.1、试验材料和灰度改良的SBS沥青被用于本试验。
把测试和生产联系起来，设计生产混合比，且合成的灰度显示在表1中，机器的单一粒径砂和矿物粉末示于表2。
经过测试，所有使用的骨料符合相关技术要求。
按照玛莎标本规范的要求，以便确定形成5.1％的最佳沥青粒料比例，并且进行检测。
表1 沥青混合料的级配设计尺寸（mm）16.0 13.2 9.5 4.75 2.36 1.18 0.6 0.3 0.15 0.075 100 97.1 83.3 50.4 32.4 22.0 16.2 10.8 7.7 4.7 质量百分比（%）表2 机制砂和矿粉的级配尺寸（mm）16.0 13.2 9.5 4.75 2.36 1.18 0.6 0.3 0.15 0.075机制砂100.0 100.0 100.0 100.0 83.4 52.8 34.9 17.9 8.3 1.4 矿物粉100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 99.1 83.6 3.2、温拌添加剂所用温暖混剂是来自南非的沙索公司研究出来的有机还原剂Sasobit。
它是从煤的气化过程中提炼出来的长链脂族烃（FT），也被称为FT石蜡。
Sasobit是一种技术较为成熟的温拌添加剂，对于目前的市场来说，在国内外许多工程中都得到了广泛的应用。
但确定冷却程度的方法还没有被明确提出，在国内外的研究中，一般根据经验，热混合沥青由10〜30℃的混合温度开始降低。
在这项研究中，Sasobit的质量是沥青质量的2％。
4、仪器的可行性分析测试在测试中使用的加工性测试仪属于探索性试验装置和试验方法，而不是当前的规范和标准方法。
为了使测试数据有说服力，首先必须确定这种仪器的可行性。
相对均匀的沥青砂浆来说，可在混合时得到稳定的测试结构，因此第一个对沥青砂浆的加工性进行试验，以排除大尺寸的石头对测试精度效果的影响。
如果沥青砂浆能够获得稳定与合理的测试数据，则表明该仪器是可行的，并且可以用来评价沥青混合料的可加工性。
4.1、测试方法根据搅拌锅的混合效应，按照表2的原料配制来设计测试。
机制砂的总质量及矿物粉是6000克。
混合后被放置在烘箱中，加热至目标温度。
设置混合温度，混合时间和混合测试仪器的频率。
将该机制砂放入搅拌锅中，加入沥青有一定的质量，并开始搅拌。
然后加入矿物粉，继续搅拌，直到样品混合均匀。
此时，数据采集可以进行。
该仪器可以显示每秒的动态数据，我们每隔五分钟开始收集，一共收集十组数据。
试验温度分别采用175℃，160℃，140℃，120℃和100℃。
在给定的温度下，分别采集不同频率（50Hz，40Hz的，为30Hz，20Hz的，10HZ）下的扭矩。
4.2、测试结果分析图2和图3中显示的是不同温度和不同的频率显下沥青砂浆加工性的测试结果。
频率（HZ)图2 不同温度下沥青砂浆加工性的比较温度（℃）图3 不同温度下沥青砂浆加工性的比较从图2中我们可以发现，在一个给定的混合温度下，沥青砂浆的频率降低降低，混合的转矩也将减小，即对于沥青砂浆，混合频率越低，它的加工性越好。
从图3中我们可以发现，转矩在给定的混频频率下随着温度的降低逐渐增加，并且其加工性变差，温度越低，需要的扭矩越大，则混合的难度越高。
这与理论知识相一致，温度的降低将导致混合物的可加工性变难。
该仪器可以定量地表示沥青混合物的可加工性的大小，所以仪器可以用来客观地评价沥青混合料的可加工性。
5、温性混合沥青的可加工性在实际生产过程中，我们关注的是沥青混合料的可加工性。
特别是对温热的沥青混合物，尤其是在混合沥青的温度较低时，我们更关注温拌技术是否和在低温条件下的热沥青混合物具有相同的加工性和紧凑性。
根据表1中的渐变设计，准备好7200克的沥青混合料，并进行加工性试验测试，在该试验中，温热的混合物沥青是从加入Sasobit添加剂、沥青质量的2％时开始凝聚干燥并制成混合物。
以下图4和图5分别是热混合沥青和温混合沥青在不同温度下的试验结果。
图4 热混合沥青在不同频率的可操作性比较图5 在不同频率下带Sasobit添加剂温暖混合沥青的加工性比较从图4和图5中我们可以看出，无论是热混合沥青或带Sasobit添加剂的温暖混合沥青，在混合温度一定的情况下，混合料的可操作性都是先增加的，后随频率增加而减小。
当混合频率为30Hz左右时，混合加工性是最好的。
相比于图2中，沥青混合料的加工性变化规律不与沥青砂浆相一致，该结果可能与混合料的最大公称尺寸有关。
热沥青混合物和带Sasobit添加剂的温热混合物沥青加工性完全相反，当混合频率是在30Hz时，其可加工性示于图6。
图6 在不同温度下混合物可加工性的比较图6所示的热沥青混合物的加工性是随温度降低而呈现出线性变化。
作为带Sasobit添加剂的温热混合物沥青，其度对混合物的加工性的影响很小，当温度高于140℃时，混合物的可加工性与预热混合剂的效果相比是很小的。
对于SBS 的热沥青混合物，混合温度一般是175℃。
由图中可以发现，带Sasobit添加剂的温暖混合沥青的加工特性，当混合温度为145℃且等同于热混合沥青时，其混合温度为175℃。
因此，带Sasobit添加剂的温暖混合沥青冷却温度可达到30℃。
无效的增加百分比可能会间接影响其可加工性和混合的结构紧凑性，因此无效的百分比可用于试验混合物的可操作性。
使用的热沥青混合物和温热的混合物沥青SGC成型试样，当温度分别为175℃和145℃，并确定成分如表3所示，试验结果是无效的，测试结果的百分比表明，对于空隙的百分比的差并不大，因此，Sasobit的添加剂可以降低混合物约30℃的混合温度。
表3 SGC样本量指数混合类型压实温度(℃) 最大理论密度(g/cm3)堆积密度(g/cm3)无效百分比（%）热混合物165 2.5000 2.399 4.04135 2.5000 2.398 4.08带Sasobit的温混合物结论①沥青混合料的加工性测试仪可客观反应混合物混合的难易程度，即加工性的大小。
②带Sasobit的温暖混合沥青的加工性的试验研究结果表明了，混合频率对混合加工性具有一定影响力，当混合频率为30Hz时，可加工性最佳。
③带Sasobit的温暖混合沥青在145℃时其加工性是等价于热混合沥青在175℃时的加工性，也就是说Sasobit添加剂的冷却速度可达到30℃。
④通过对孔隙率的检测，证实了温拌混合物的压实百分比是等价于热混合沥青混合温度降低30℃时的压实百分比。