Introduction to Solid Modeling
Lecture 12 - Introduction to Solidworks
221
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Filling Holes in (imported) Geometry
• Open Part • Insert –> Face -> Delete • Select the inner face of one or several holes … • … or draw a rubber band inside the part from inside a hole to inside a hole • OK
224
222
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Split one part into individual parts
• In many import files we see that several bodies are modeled in only one part (e.g. PCB with components). In this case we have to split these bodies in order to assign different materials or boundary, separately. Choose Insert -> Features -> Split • In the Split dialog select the face where the components should be cut off (green highlighted. • Press „cut part“ and choose all components you want to split from the PCB. After selecting they are highlighted in magenta. • In folder solid bodies we find the split components from where they could be selected individually e.g. for material etc.
Kittel Introduction to solid state physics基特尔固体物理导论
Facaeks “Magnetism and electron correlation”
同步辐射，x光技术
Warren: X-ray scattering Als-Jen: Modern x-ray technique
马里敦编：同步辐射技术
Thesis of Sawatzky group Huffner: Photoemission Spectroscopy
Kotani: High energy spectroscopoies
特别专题，技术等
1. 核磁共振 Slichter: Nuclear magnetic resonance 2. 光谱: Gruner: Electrodynamics of solids 3. 中子散射 4. Gruner: Charge density wave 5. 相变 Geballe: Long range order in solids
Mattuck: Green’s Function
Kittel, Quantum theory of solid 李正中《固体理论》
Mohan: Many body physics
元激发/对称性
P. W. Anderson: Basic concept of condensed matter Pines: elementary excitations in solids
Schrieffer: Superconductivity Anderson: High-Tc superconductivity 章其瑞《高温超导电性》 周午众《高温超导基础研究》 韩汝珊《高温超导》 陈良尧译《光电子谱研究高温超导》
强关联体系
Cox: Transition metal oxides An introduction to their electronic structure and properties Yanase: Electronic Conduction in Oxides
IntroductiontoSolidStatePhysics第八版课程设计Wiley
Introduction to Solid State Physics 第八版课程设计Wiley概述本文档为《Introduction to Solid State Physics 第八版》课程设计文档，旨在介绍本课程设计的目的、内容及实现方法。
本次课程设计基于Wiley出版社的《Introduction to Solid State Physics》第八版。
通过本次课程设计，学生可以进一步加深对固体物理学的理解，学习先进的物理计算工具和理论知识，并掌握固体物理学实验技能。
目的《Introduction to Solid State Physics 第八版》是为大学本科生和研究生编写的一本全面介绍固体物理学的教材。
本课程设计的主要目的是：1.提高学生对固体物理学的兴趣和理解，培养学生对固体物理学研究的兴趣和热情；2.学习计算机模拟和理论计算工具，学生可以加深对固体物理学理论和实验的认识和理解；3.初步掌握固体物理实验技能，提高学生的实验能力。
内容本次课程设计的主要内容包括以下三个方面：1.固体物理学的基础知识：包括基础概念、晶体学、电子结构和声子学等基础知识。
通过学习这些基础知识，学生可以对固体物理学有更深入的了解和理解；2.物理模拟和计算工具的使用：包括使用计算机模拟软件进行实验和计算，掌握先进的理论计算工具；3.固体物理实验技能的掌握：包括光学实验、电学实验和热学实验等固体物理学实验，以及对实验数据的处理和分析。
实现方法为了达到上述目的和内容，本次课程设计将采取以下的实现方法：1.通过课堂讲解和教材阅读，学生将掌握固体物理学的基础知识；2.使用计算机模拟软件和理论计算工具，学生将学会使用先进的物理计算工具，在理论分析和实验数据处理方面获得更丰富的经验；3.通过实验教学和实验报告，学生将掌握基本的固体物理实验技能，并了解实验数据处理方法。
活动安排本次课程设计涵盖的活动包括以下内容：1.课堂讲解和学习小组讨论，旨在深入理解固体物理学的基本概念和理论；2.通过使用计算机模拟软件和理论计算工具，学习基础的固体物理学计算方法和理论分析方法；3.固体物理实验，包括光学实验、电学实验和热学实验等，学习实验技能和数据处理方法；4.实验报告和课堂展示，对实验结果进行数据处理和分析，并在课堂上展示成果。
AutoCAD 3D Solid Modeling教程说明书
GEN10117The Hitchhiker's Guide to AutoCAD 3D Solid ModelingDieter SchlaepferAutodesk, Inc.Learning Objectives∙Learn how to use the basic 3D solid modeling commands∙Learn how to apply practical 3D solid modeling techniques∙Learn how to avoid common pitfalls∙Learn the next steps for becoming proficient in 3D solid modelingDescriptionYou will learn the basics of 3D solid modeling using only ten commands. Included are practical techniques, tips, and caveats with real-life models.My goal is to give you a solid introduction, demos, and a roadmap to 3D solid modeling that will make you functional with as few commands as possible, and avoid overwhelming you with information. Your AU ExpertsDieter Schlaepfer is a principal technical writer at Autodesk, Inc., creating AutoCAD documentation and training guides. In prior employment he provided on-site consultative CAD/CAM/CAE training to manufacturing, architecture, engineering, and construction firms. He has 35 years of experience in the field, and he specializes in 3D modeling, training, and technical writing.Definitions for Context∙Isometric drafting – think flat, “2½ D”∙Wireframe modeling –think “pipe cleaners”∙Surface modeling –think “paper thin”∙Mesh modeling – think sculpting, smoothing chicken wire ∙Solid modeling – think volume and mass2D Commands Used With 3D Solids2D Geometry Commands Used in 3D Modeling∙MOVE, COPY, ROTATE, MIRROR, ERASE, PEDIT, FILLET∙Ortho mode and direct distance entry∙PLINE, RECTANG, CIRCLE∙BOUNDARY (typically in plan view)∙HELIX (spirals, springs, threads)2D Inquiry, Visibility, and Controls Used in 3D modeling ∙ID, MEASUREGEOM, PROPERTIES∙GROUP, UNGROUP for assemblies∙Isolate and Hide objects on the status barThe 10 Essential Commands for 3D Solid Modeling Viewing in 3D∙3DORBIT (3DO)o Perspective vs. orthographico Visual styles (VS)o Options > Display tab > Colorso Quick: Shift + press mouse wheel∙PLANo XY plane of the current UCSo Mechanical Design vs. Architectural conventionsThe User Coordinate System∙Orientation: Construction plane for creating 2D objects∙Orthogonal directions: X, Y, and Z for direct distance entry, Ortho mode ∙Rotation: The Z axis is the “hinge”Tip: Turn off dynamic UCS by setting UCSDETECT = 0 [F6]∙UCS – The essential options:o UCS 3P – Locating the XY plane for 2D geometry, Orthoo UCS ZA – Specifying the Z Axis direction for rotatingo UCS World – Getting back homeTip: Enter UCS directly at the Command prompt∙UCSICON – Control the display of the UCS icono Off for screenshotso On + display at Origin for modelingNote: UCS display – 2D wireframe vs all other visual stylesProfile Operations∙EXTRUDE (direction)∙REVOLVE (axis)∙SWEEP (path)o2D polylineso+ profilesTip: Set DELOBJ = 0 to retain profile geometryo You will often need to revise and referenceo Keep profile and reference geometry on separate Reference layerso Choose a distinctive color for profile and reference geometryBoolean Operations∙UNION∙SUBTRACT∙INTERSECTBest Practices and Advice∙Learn using simple models, become comfortable with the commands ∙Use layers to manage visual complexity∙Create 2D profiles first (closed polylines and circles)∙Move and rotate 2D profiles and 3D objects into place∙Create and keep profile geometry (set DELOBJ to 0)∙Check and recheck distances and dimensions∙Limit the detail to what is justified for your goals∙Delay filleting to preserve sharp corners for measuring and locating ∙Use GROUP to associate objects that you don’t want to Union∙Create blocks from repetitive objects to reduce DWG size∙Save a version of a model at each stage so you can revert∙3D landscaping – purchase and insert as blocks∙People – Use transparent extrusionsNext Steps∙Download the class presentation, notes, and drawing files∙Review the presentation, try things with the 24 class models∙Create some simple models∙Review the Further Study section below∙Explore the 3D Basics workspace∙Experiment and have fun!Further StudyViewing and Display∙ViewCube, LENSLENGTH (perspective view), CAMERA, TARGET, VISUALSTYLES (VS), PERSPECTIVE ∙Transparency (0-90%) – CETRANSPARENCY, set ByLayer or individually using the Properties palette by entering a value; use for glass windows and walls, “shadow” people bu t also notetransparent materials for rendering∙Wireframe display controls: ISOLINES, VIEWRES, DISPSILH∙Rendered visual style display: FACETRES∙Sectioning: SLICE (3D trim), SECTION, SECTIONPLANE3D Object Creation∙LOFT, INTERFERE, PRESSPULL, POLYSOLID, REGION with BooleansUCS∙UCS X, Y, Z rotation (90 degrees), right-hand rotation rule (thumb=Z axis, fingers curl positive) ∙Isometric dimensioning with the UCSEditing∙ROTATE3D, MIRROR3D, ALIGN∙Subobject selection (Ctrl + select + right click options)∙Shell a 3D solid – SOLIDEDIT /Body /Shell (remove faces that are not to be shelled)∙Separate noncontiguous 3D solids termed “lumps” (SOLIDEDIT /Body /seParate)∙Convert surfaces and meshes to solids: THICKEN, SURFSCULPT, CONVTOSOLID∙SOLIDHIST for retaining component solidsAnalysis∙Massing studies, sun and shadow studies, wind studies∙MASSPROP, DIST, MEASUREGEOM – Volume, centroid, moments of inertia, etc.∙AREA /Object – Surface area, including any fully enclosed volumes∙FEM/FEA analysisOutput and Processing∙2D drawings: FLATTEN, FLATSHOT, SOLVIEW, SOLDRAW, SOLPROF, Fusion 360, the AutoCAD Model Documentation feature set for mechanical design: VIEW* commands ∙Rendering, materials: RENDER, MATERIALS, etc.∙EXPORT: STL (3D printing), SAT (CNC) outputList of Drawings∙10 Kitchen.dwg – a real-life kitchen remodel project, EXTRUDE profiles∙20 Playscape.dwg – a wireframe model for UCS practice∙30 Glass.dwg – the profile of a real-life wine glass, REVOLVE profile about centerline∙31 Bike Rim.dwg – a heavy duty bike rim design, REVOLVE profile about axel∙32 Chair.dwg – a chair design, SWEEP objects along a path∙40 Walkway – a real-life walkway and driveway design, EXTRUDE and then UNION profiles ∙41 Florette-S.dwg – a real-life tip of an electric foil blade used in sport of fencing∙42 Bowsight.dwg – an old-fashioned bow sight bracket, EXTRUDE and then INTERSECT profiles ∙43 Roof.dwg – a hip roof, EXTRUDE and INTERSECT profiles∙44 Envelope.dwg – an envelope of a building or part, EXTRUDE and INTERESECT three profiles ∙45 Box.dwg – create a plastic box with draft angles, EXTRUDE and INTERSECT profiles∙46 Eclipse.dwg – a real-life model of a scoring machine used in the sport of fencing∙50 Keyboards.dwg – two keyboards with different levels of detail∙51 Stairs.dwg – two sets of stairs with different levels of detail∙52 Interference.dwg – HVAC duct meets brace, brace wins, INTERFERE∙53 Arbor.dwg – a real-life 2D drawing of an arbor design∙54 Arbor Profiles.dwg – profiles converted into plines and rotated into place∙55 Arbor 3D.dwg – 3D model of arbor done in pieces with EXTRUDE and INTERSECT∙56 Deck – a real-life deck design. Stress analysis performed by an architect before it was built ∙57 Interfere2.dwg – estimated cut from the interference volume between a building footprint and a solid that was lofted using contour lines, LOFT and INTERFERE∙58 Room 3 render.dwg – a room to render, RENDER and MATERIALS∙59 3D House.dwg – a house to experiment with∙60 Campus.dwg – lots of experiments here, pan and zoom within 3DORBITBuilding models - Boston Redevelopment Authority/planning/urban-design/urban-design-technology-group /document-center?doctype=10&sortby=name&sortdirection=asc。
《计算机辅助几何造型技术》1
计算机辅助几何造型技术主讲教师：秦开怀教授、博导qkh-dcs@所在单位：清华大学计算机科学与技术系 时间：2007年9月~2008年1月Textbooks/ReferencesJ. Hoschek& D. Lasser, Fundamentals of Computer Aided Geometric Design A K Peters Computer Aided Geometric Design, A K Peters, Ltd, Massachusetts, 1993.David F Rogers Introduction to NURBS Morgan David F Rogers,Introduction to NURBS, Morgan Kaufmann,2001.L Piegl&W Tiller The NURBS Book(2L. Piegl & W. Tiller, The NURBS Book (2nd Edition), Springer-Verlag Berlin Heidelberg, NewYork, 1997.York1997Carl deBoor, A Practical Guide to Splines, New York, Springer Verlag, 1978.York Springer-Verlag1978(Continued)M. E. Mortenson, Geometric Modeling , J h W l &S I 1985John Waley & Sons, Inc., 1985. G. Farin, Curves and Surfaces for ,Computer Aided Geometric Design (5th Edition), Elsevier Inc., 2002.(李双喜译，),,(CAGD 曲线曲面，科学出版社，2006）E J Stollnitz T DeRose &D H Salesin E. J. Stollnitz, T. DeRose & D. H. Salesin, Wavelets for Computer Graphics, Theory & Morgan Kaufmann PublishersApplications , Morgan Kaufmann Publishers, Inc., San Francisco, 1996.(Continued)Denis Zorin & Peter Schroder, Subdivision for M d li d A i ti SIGGRAPH 2000Modeling and Animation , SIGGRAPH 2000 Course Notes #23, 2000. R. Barzel, Physically-Based Modeling for Computer Graphics, A Structured Approach,Academic Press, Inc., San Diego, 1992.D. N. Metaxas, Physic-Based Deformable ,yModels, Applications to Computer Vision, Graphics & Medical Imaging , Kluwer Academicp g g ,Publishers, Massachusetts, 1997.(Continued)Donald Hearn & M.Pauline Baker, C t G hi ith O GL (Thi d Computer Graphics with OpenGL (Third Edition), Pearson Education, 2004 (中译本赫恩等著本：赫恩等著, 蔡士杰等译,《计算机图形学（第三版）》, 电子工业出版社, 200506)2005-06.) J. D. Foley, et al, Computer Graphics: y,,p pPrinciples & Practice (2nd Edition in C),Addison-Wesley, Reading, MA, 1996.y,g,,G di P li Grading PolicyThree assignments 30%Discussions/learning in classroom 5% One project substituting for the final p j g examination 65%R kRemarksThe three assignment is to be completed individually on yourself, but discussions among fellow students areyourself but discussions among fellow students areallowed.The project substitutes for the final examination Two The project substitutes for the final examination. Twostudents can work together as a group.Absolutely no sharing or copying of any code for both Absolutely no sharing or copying of any code for boththe assignments and the project! Offenders will be givena failure grade and the case will be reported to theg pdepartment.You are welcome to turn off your mobile phone before You are welcome to turn off your mobile phone beforeattending lectures.This course concentrates on seven main issues:i iNURBS curves and surfaces (including Bezier, B-spline curves and surfaces)gTriangular surfacesGordon-Coons surfacesSubdivision surfaces of arbitrary topologySubdivision surfaces of arbitrary topologyThe 2nd generation wavelets for multi-resolution modelingmodelingSolid modelingNew technology for geometric modelingContents of This Course1.Introduction2.∆Mathematic BasicsAffine mapsAffine mapsDivided differenceFunction spaceGeometric basics from curves and surfaces 3.∆Interpolatory Polynomial SplinesHermite interpolationHermite interpolationContents of This Course Contents of This Course (Continued)Quadric polynomial spline curvesCubic polynomial spline curvesSolving a linear system of equations with a g y q tridiagonal coefficient matrix Cubic parametric spline curves Cubic parametric spline curves4.*Bezier Curves and Surfaces Bezier curves defined by edge vectorsBernstein-Bezier curvesProperties of Bernstein-Bezier curves(Continued)De Casteljau algorithmDi t ti f B iDiscrete generation of Bezier curvesDegree elevation of Bezier curvesD d i f B iDegree reduction of Bezier curvesBezier spline curvesBezier interpolation curvesMatrix formula of Bezier curvesRational Bezier curvesProduct & inner product of Bezier curves Bezier surfaces(Continued)5.*B-spline Curves and SurfacesB-spline basis functions and their p ppropertiesB-spline curvesOpen curves and knot vectorsOpen curves and knot vectorsUniform B-spline curvesEndpoint interpolating B spline curves Endpoint interpolating B-spline curvesClosed B-spline curves(Continued)Chaikin algorithmDe Boor algorithmInserting knots in B-spline curves Inserting knots in B spline curvesBoehm algorithmOlso algorithmGeneral knot insertion for B-spline curvesDegree elevation of B-spline curves Degree elevation of B-spline curvesMarsden identity and recursive degree elevationPrautzsch algorithm(Continued)Arbitrarily high degree elevation for B-spline curvesDegree reduction of B-spline curvesB-spline surfacesInterpolating B-spline curves and p g p surfaces Matrix formulas of B-spline curves and Matrix formulas of B spline curves and surfaces(Continued)Matrix formula of uniform B_spline curvesMatrix formula of non-uniform B_splines Inner product of B-spline curvesGeneralized Marsden identityB-spline curve productInner product of B-spline basis functionsInner product of B-spline curves6.*NURBS Curves and SurfacesNURBS curvesNURBS curvesRepresenting conics using NURBS(Continued)Parameterization of curvesfNURBS surfacesRepresenting quadrics using NURBS surfacesfInterpolating NURBS curves and surfaces 7.Blossoming PrincipleLooking at de Casteljau algorithm from a Looking at de Casteljau algorithm from a blossoming point of viewKnot insertion from a blossoming point of Knot insertion from a blossoming point of view(Continued)Generating de Boor points based on the blossoming principleblossoming principleDegree raising of B-spline curves by blossoming8.* Triangular SurfacesBarycentric coordinatesgTriangular Bezier surfacesContinuity conditions for triangular Bezier ppatchesRational Triangular surfaces(Continued)9.*Gordon-Coons SurfacesCoons surfacesGordon-Coons surfaces on rectanglesGordon-Coons surfaces on triangles0Subd s o Su a s o b a y 10.*Subdivision Surfaces of ArbitraryTopologyCatmull-Clark surfacesCatmull-Clark surfacesDoo-Sabin surfacesContinuity of uniform subdivision surfaces Continuity of uniform subdivision surfacesNon-uniform subdivision surfaces(Continued)Convergence and continuity of non-uniform subdivision surfaces11.*The 2nd Generation Wavelets forMulti-resolution modelingMulti-resolution modelingB-spline wavelets for Multi-resolution modeling Endpoint interpolating B-spline wavelets Endpoint interpolating B-spline waveletsArbitrary Non-uniform B-spline waveletsB-spline wavelets with constraintsB spline wavelets with constraintsSubdivision-based Surface waveletsLoop Subdivision WaveletsCatmull-Clark Subdivision Wavelets√3-subdivision-based Bi-orthogonal Wavelets(Continued)12.∆Scattered Data Interpolation13.*Intersections of Curves and Surfaces14.Solid Modeling14*Solid Modeling15.Parameterization Modeling for ShapeDesign and Feature-based Modeling 16.New Technology for Geometric 16.*New Technology for GeometricModelingHierarchical B splinesHierarchical B-splinesPhysics-based modelingContents of This Course Contents of This Course (Continued)Modeling fractalized scenes (mountains,f lowers etc.)Particle system for modeling fires, clouds, water, forests etc.1.Introduction1. IntroductionSome Applications of CAGDRepresentation of large data setsVisualizing productsAutomatically producing sectionalAutomatically producing sectional drawingsModeling surfaces arising inModeling surfaces arising in construction of cars, ships & airplanesDesigning pipe systems, e.g. in chemical plants(continued)Drawing marine charts and city and relief i h maps in cartographyProduction and quality control, e.g. in q y ,g the sewing machine, textile and shoe industriesPlanning and controlling surgery Creating images in advertising television Creating images in advertising, television and film industries(continued)Constructing virtual environmentsDescribing robot paths and controlling their movementstheir movementsControlling milling machines used in manufacturingCurve modeling with constrained B-spline wavelets 保特征点的多分辨率曲线造型29曲线的多分辨率分段无缝表示30细分曲面带约束的样条曲面小波左图是采用经典B 样条曲面小波分片多分辨率表示的结果,右图是采用带约束B 的样条曲面小波分片多分辨率表示的结果,其中约束施加在接合线处。
SOLID MODELING翻译
SOLID MODELING实体造型6.1 Application of Solid Models实体模型的应用In mechanical engineering, a solid model is used for the following applications:在机械工程中，一个实体模型被用于以下应用：1、Graphics: generating drawings, surface and solid models图形：生成图纸，表面和实体模型2、Design: Mass property calculation, interference analysis, finite element modeling, kinematics and mechanism analysis, animation, etc.设计：质量计算、干涉分析、有限元建模、运动学及机理分析、动画等。
3 、Manufacturing: Tool path generation and verification, process planning, dimension inspection, tolerance and surface finish.制造业：刀具轨迹的生成和验证，工艺设计，尺寸检验，公差及表面处理。
4 、Component Assembly: Application to robotics and flexible manufacturing: Assembly planning, vision algorithm, kinematics and dynamics driven by solid models.组件组装：应用于机器人和柔性制造：装配规划，视觉算法，运动学和动力学模型的驱动。
6.2 Solid Model Representation实体模型表示There are three different forms in which a solid model can be represented in CAD:有三种不同的形式，其中一个实体模型可以表示在计算机辅助设计：·Wireframe Model线架模型·Surface Model曲面模型·Solid Model实体模型Wireframe Models: Joining points and curves creates wireframe models. These models can be ambiguous and unable to provide mass property calculations, hidden surface removal, or generation of shaded images. Wireframe models are mainly used for a quick verification of design ideas.线框模型：连接点和曲线创建线框模型。
基于Solid Works的双顶置凸轮轴的设计与建模
价值工程———————————————————————作者简介：王远阳（1979-），男，新疆乌鲁木齐人，助教，本科，主要从事机械设计方面的研究。
0引言目前，有多种基于不同CAD 支撑软件的标准件库，每种CAD 撑软件下又有不同的建库方式[1]。
例如，Auto CAD 环境下的建库，是利用Auto CAD 提供的图块功能，但是由于插入是不能对实体的局部尺寸进行修改，一般只用于诸如符号的简单图形库，此外，还可以利用Auto CAD 提供的Auto lisp ，VBA 等编程工具，通过编辑的方式对零件描述其图形。
这种方式可以实现参数化绘图，而且调用方便，但这种建库方式变成的工作量相当大，曾加和修改零件时都需修改程序。
这些基于Auto CAD 的图形库大都是二维图形库。
三维标准库以基于Solid Works 和MDT 居多。
例如，基于Solid Works 的标准件库设计可通过系列零件设计表和驱动功能，使用该表对标准件模型内的各种尺寸进行驱动。
Solid Works 还提供了许多API 函数作为OLE 程序借口，用户作为二次开发是可以在Visual Basic 及visual C++环境下调用他们开发自己的程序，不过是用API 函数出需要开发人员具备Windows 编程的能力外，过程也比较复杂。
在MDT 平台上建立图形库同在Solid Works 平台上相类似[2]，总之，三维标准库的优点是创建尽管容易，但具体操作视不同系统而定。
Solid Works 有全面的零件实体建模功能，变量化的草图轮廓绘制，并能够自动进行动态过约束检查。
用SolidWorks 拉伸、旋转、倒角、抽壳和倒圆角等功能可以更简便地得到要设计的实体模型。
高级的抽壳可以在同一实体上定义不同的抽壳壁厚。
在用户可定义坐标系，能自动计算零部件的物性和进行可控制的几何测量。
用高级放样、扫描和曲面拱顶等功能可以生成性状复杂的构造曲面。
通过直接对曲面的操作，能控制参数曲面的形状。
Introduction to Mathematical Modeling in Mathematica
Bruce E. Shapiro
Department of Biomathematics UCLA School of Medicine and Jet Propulsion Laboratory California Institute of Technology bshapiro@ 3 June 1998 Presented as part of Medical Informatics: A Course for Health Professionals Sponsored by the National Library of Medicine and Marine Biology Laboratory Water Street Woods Hole, MA 02543
Starting Mathematica............................12
An Example Population Biology...........23 Mathematica Reference Outline............24
Numerical Computation.........................24 Numerical Evaluation.........................24 Equation Solving..............................24 Sums and Products............................24 Integration.......................................25 Optimization....................................25 Data Manipulation.............................25 Curve Fitting...............................25 Fourier Transform.........................26 Selection of special elements...........26 Set Manipulation..........................26 Matrices and Vectors..........................27 Matrix Operations.........................27 Systems of Linear Equations...........27 Matrix Decompositions..................27 Complex Numbers............................28 Number Representation......................28 Heads..........................................28 Digits, Exponents, and Mantissas.....28 Change of Representation...............29 Infinity........................................29 Numerical Precision...........................29 Evaluation Accuracy and Precision....29 Intervals......................................29 Machine Accuracy and Precision.......30 Options...........................................30 Algebraic Computation..........................30 Basic Algebra...................................30 Formula Manipulation.......................31 Simplification..............................31 Expansion....................................31 Rearrangement..............................31 Parts of an Expression....................32 Numerators and Denominators.........32 Trigonometric Function Manipulation ..................................................32 Root and Radical Manipulation........32 Other Manipulations......................32 Bruce E. Shapiro Woods Hole, 1998
介绍solidworks英语作文
介绍solidworks英语作文SolidWorks is a powerful computer-aided design (CAD) software that has revolutionized the way engineers and designers approach the product development process. Developed by Dassault Systèmes, SolidWorks has become a widely adopted tool in various industries, including manufacturing, architecture, and engineering, due to its comprehensive features and user-friendly interface.At its core, SolidWorks is a 3D modeling and design software that allows users to create and manipulate complex geometric shapes and assemblies. The software's parametric modeling approach enables designers to easily modify and update their designs, making it an indispensable tool for iterative design processes. With SolidWorks, users can create 2D sketches, convert them into 3D models, and then further refine and optimize their designs.One of the standout features of SolidWorks is its ability to seamlessly integrate with other software and systems. The software's compatibility with a wide range of file formats, including STEP, IGES, and DXF, allows for easy collaboration and data exchange with otherCAD programs and engineering tools. This interoperability is crucial in the modern, interconnected world of product development, where teams often work across different platforms and disciplines.Another key advantage of SolidWorks is its comprehensive set of analysis and simulation tools. The software's integrated simulation capabilities enable designers to test and validate their designs before physical prototyping or manufacturing. This includes features such as finite element analysis (FEA) for structural and thermal analysis, computational fluid dynamics (CFD) for fluid flow and heat transfer analysis, and motion analysis for understanding the dynamic behavior of assemblies.The ability to perform these analyses within the SolidWorks environment saves time and resources, as designers can quickly identify and address potential issues in their designs without the need for external software or physical testing. This streamlined workflow allows for more efficient product development and faster time-to-market.SolidWorks also excels in the area of technical documentation and communication. The software's robust drawing and annotation tools make it easy to create detailed 2D and 3D technical drawings, exploded views, and assembly instructions. These resources are essential for manufacturing, assembly, and maintenance processes,as they provide clear and comprehensive information to various stakeholders involved in the product lifecycle.Furthermore, SolidWorks offers a range of advanced features that cater to specific industry needs. For example, the software's Weldments module simplifies the design and documentation of welded structures, while the Plastic Part Design module provides specialized tools for the design and analysis of plastic components. These industry-specific capabilities make SolidWorks a versatile and adaptable solution for a wide range of applications.One of the key factors contributing to the widespread adoption of SolidWorks is its user-friendly interface and intuitive workflow. The software's design-centric approach, with a focus on visual feedback and real-time modeling, makes it accessible to both experienced designers and newcomers to the CAD world. The software's extensive library of pre-built components and features, as well as its customizable toolbars and shortcuts, further enhance the user experience and streamline the design process.In addition to its technical capabilities, SolidWorks also offers a robust ecosystem of support and resources. The software's extensive online documentation, video tutorials, and user forums provide a wealth of information and guidance for users of all skill levels. Furthermore, the SolidWorks community, which includes a globalnetwork of users, resellers, and partners, offers a collaborative environment for sharing best practices, exchanging ideas, and accessing a wide range of third-party add-ons and extensions.As the product development landscape continues to evolve, the importance of effective CAD software like SolidWorks becomes increasingly evident. The ability to create and simulate complex designs, collaborate seamlessly, and streamline the overall product development process is crucial for companies looking to stay competitive in today's fast-paced and innovative market.In conclusion, SolidWorks is a comprehensive and versatile CAD software that has become an indispensable tool for engineers, designers, and product development professionals. Its powerful modeling capabilities, integrated analysis and simulation features, and user-friendly interface make it a valuable asset in a wide range of industries. As the demands of product development continue to evolve, SolidWorks remains at the forefront of innovation, providing users with the tools and resources they need to bring their ideas to life.。
机械毕业设计英文外文翻译21UG NX简介 - 副本
附录附录AUG NX Summarized AccountIntroduction to UG NX UG NX is American Unigraphics Solutions (UGS) company's PLM offering for the core components. UGS company is an American a global supplier of MCAD. PLM Solutions provide a powerful vitality of product lifecycle management (PLM) solutions, including product development, manufacturing, planning, product data management, e-commerce product solutions, but also offers a complete suite of services for product improvement. UG to the automotive and transportation, aerospace, consumer goods, General Engineering and electronic industries through its virtual product development (VPD) provide Multipole, integrated,enterprise-class products and services, including software, and a complete solution.CAD/CAM/CAE three systems tightly integrated. Users use the UG powerful solid modeling, surface modeling, virtual Assembly and create functions such as engineering drawings, you can use the CAE module for finite element analysis, kinematic analysis and simulation, to improve the design of reliability; according to theestablished 3D model, but also by CAM module directly generate CNC code, for product processing. Flexibility in the way of modeling. Composite modeling technology, is solid modeling, surface modeling, wireframe modeling, display the geometric modeling and parametric modeling. Parameter-driven, intuitive, easy to modify the image. Surface design for non-uniform rational B-spline curve-based, you can use several methods to build a complex surface, powerful. A good second development environment, users can use a variety of ways for the second development. Knowledge-driven automation (KDA), facilitate access to and reuse of knowledge.A brief, post processing both CAM software, its main purpose is to build components in the machine tool path (cutting). Generally speaking, you cannot directly transfer CAM software internally generated by the tool path to machine processing, because each type of machine in the physical structure and control system may be different, resulting in the NC program instructions and format requirements may differ. Therefore, the tool path data must be processed to fit each machine and control system for specific requirements. This processing, in most CAM software called "post-processing". Post-processing of results is to make the tool path data into machine recognizes tool path data, that is, the NC code.Visible, post-processing must have two elements: cutting-CAM homegrown tool path; after the processor — is a machine tool and its control system information. UG system provides general post processor — UG/Post, it uses the UG internal tool path data as input, output machine after via post-processing recognizes the NC code. UG/POST Organizational structureUG/Post has a strong user of capability, it can be adapted from the very simple to arbitrarily complex machine and control system for processing. Second, UG/Post composition structure mentioned UG/post processor, had to be simple to introduce MOM (Manufacturing Output Manager), i.e. processing output Manager. MOM is UG provides an event-driven tools, UG/CAM module output from it to manage, it is stored in the UG/CAM data to extract the data to generate output. UG/Post it is this tool a specific application. MOM is UG/post processor core, UG/post use MOM to start the interpreter, to explain the program provides the functionality and data, and loads the event processor (Event Handler) and the definition file (Definition File).In addition to the MOM, UG/post is primarily determined by the event generator, the event processor, definition file and output files, and so on of four elements. Once you start UG/post processor to handle UG internal tool path, its processes to the following: event Builder from beginning to end to scan theentire UG tool path data, extract each of the events and their associated parameters information, and they are passed to the MOM to MOM; then, delivered each event and its associated parameters to the user in advance to develop good event processor, and collected by the event processor based on their content to decide upon how each event for processing; then the event handler returns the data to MOM as their output, MOM read definition file content to determine how to format the output data; Finally, MOM to well-formatted output data to write to the specified output file. Figure 1 describes the concepts and content.附录BUG NX简介UG NX是美国Unigraphics Solutions（简称UGS）公司的PLM产品的核心组成部分。
simulation modelling practice
simulation modelling practiceSimulation modelling is a crucial tool in the field of science and engineering. It allows us to investigate complex systems and predict their behaviour in response to various inputs and conditions. This article will guide you through the process of simulation modelling, from its basicprinciples to practical applications.1. Introduction to Simulation ModellingSimulation modelling is the process of representing real-world systems using mathematical models. These models allow us to investigate systems that are too complex or expensiveto be fully studied using traditional methods. Simulation models are created using mathematical equations, functions, and algorithms that represent the interactions and relationships between the system's components.2. Building a Basic Simulation ModelTo begin, you will need to identify the key elements that make up your system and define their interactions. Next, you will need to create mathematical equations that represent these interactions. These equations should be as simple as possible while still capturing the essential aspects of the system's behaviour.Once you have your equations, you can use simulation software to create a model. Popular simulation softwareincludes MATLAB, Simulink, and Arena. These software packages allow you to input your equations and see how the system will respond to different inputs and conditions.3. Choosing a Simulation Software PackageWhen choosing a simulation software package, consider your specific needs and resources. Each package has its own strengths and limitations, so it's important to select one that best fits your project. Some packages are more suitable for simulating large-scale systems, while others may bebetter for quickly prototyping small-scale systems.4. Practical Applications of Simulation ModellingSimulation modelling is used in a wide range of fields, including engineering, finance, healthcare, and more. Here are some practical applications:* Engineering: Simulation modelling is commonly used in the automotive, aerospace, and manufacturing industries to design and test systems such as engines, vehicles, and manufacturing processes.* Finance: Simulation modelling is used by financial institutions to assess the impact of market conditions on investment portfolios and interest rates.* Healthcare: Simulation modelling is used to plan and manage healthcare resources, predict disease trends, and evaluate the effectiveness of treatment methods.* Education: Simulation modelling is an excellent toolfor teaching students about complex systems and how they interact with each other. It helps students develop critical thinking skills and problem-solving techniques.5. Case Studies and ExamplesTo illustrate the practical use of simulation modelling, we will take a look at two case studies: an aircraft engine simulation and a healthcare resource management simulation.Aircraft Engine Simulation: In this scenario, a simulation model is used to assess the performance ofdifferent engine designs under various flight conditions. The model helps engineers identify design flaws and improve efficiency.Healthcare Resource Management Simulation: This simulation model helps healthcare providers plan their resources based on anticipated patient demand. The model can also be used to evaluate different treatment methods and identify optimal resource allocation strategies.6. ConclusionSimulation modelling is a powerful tool that allows us to investigate complex systems and make informed decisions about how to best manage them. By following these steps, you can create your own simulation models and apply them to real-world problems. Remember, it's always important to keep anopen mind and be willing to adapt your approach based on the specific needs of your project.。
基于Solidworks的齿轮减速器的建模与结构的设计毕业论文
基于Solidworks的齿轮减速器的建模与结构的设计----毕业论文摘要本论文比较系统的介绍了利用SolidWorks软件进行机械设计及仿真的过程及结果。
本文介绍了SolidWorks软件的基本模块，功能及应用方法，同时对CAD/CAM 的发展现状进行了分析，确定利用SolidWorks进行机械设计的必要性及可行性。
最后，文章利用减速机的设计完成了该软件在机械设计，机械动力学仿真性能。
通过验证，利用CAD/CAM软件对机械设计制造进行数字化，极大地提高了产品设计的效率，缩短了产品开发的周期，提高了企业的效率。
关键词：SolidWorks;减速机AbstractThis article introduction the machine design and the simulation process and get out the result based on SolidWorks system. In the forther the artical introduced the SolidWorks software, and talk out basic module, the function and the application method of SolidWorks, the simultaneous have carried on the analysis to present situation of the CAD/CAM development, it carries on the machine design using SolidWorksn the necessity and the feasibility. Finally, the article has completed this software using thespeed reducer design in the machine design, mechanical kinetics simulation and NC automatic programming aspect performance. Through the confirmation, carries on the digitization using the CAD/CAM software to the machine design manufacture, enhanced the product design efficiency enormously, reduced the product development cycle, enhanced enterprise's efficiency.Key words : simulation ;SolidWorks ;speed reducer目录第一章 CAD/CAM技术的介绍 (1)1.1 我国CAD/CAM的发展现状 (1)1.2 CAD/CAM技术的发展趋势 (1)第二章 SolidWorksn软件介绍 (4)第三章一级齿轮减速机的设计与建模 (6)3.1 减速机的介绍 (6)3.2建模步骤 (6)3.3大齿轮的建模 (6)3.4上箱体的建模 (9)3.5下箱体的建模 (12)3.6 减速机的装配 (16)第四章结论与展望 (17)参考文献 (18)致谢 (19)第一章CAD/CAM技术的介绍CAD/CAM技术以计算机及周边设备和系统软件为基础，它包括二维绘图设计、三维几何造型设计。
Geometric Modeling
Geometric ModelingGeometric modeling is a crucial aspect of computer graphics and design, playing a significant role in various fields such as engineering, architecture, animation, and gaming. It involves the creation and manipulation of geometric shapes and structures in a digital environment, allowing for the visualization and representation of complex objects and scenes. However, despite its importance, geometric modeling presents several challenges and limitations that need to be addressed in order to improve its efficiency and effectiveness. One of the primary issues in geometric modeling is the complexity of representing real-world objects and environments in a digital format. The process of converting physical objects into digital models involves capturing and processing a vast amount of data, which can be time-consuming and resource-intensive. This is particularly challenging when dealing with intricate and irregular shapes, as it requires advanced techniques such as surface reconstruction and mesh generation to accurately capture the details of the object. As a result, geometric modeling often requires a balance between precision and efficiency, as the level of detail in the model directly impacts its computational cost and performance. Another challenge in geometric modeling is the need for seamless integration with other design and simulation tools. In many applications, geometric models are used as a basis for further analysis and manipulation, such as finite element analysis in engineering or physics-based simulations in animation. Therefore, it is essential for geometric modeling software to be compatible with other software and data formats, allowing for the transfer and utilization of geometric models across different platforms. This interoperability is crucial for streamlining the design and production process, as it enables seamless collaboration and data exchange between different teams and disciplines. Furthermore, geometric modeling also faces challenges related to the representation and manipulation of geometric data. Traditional modeling techniques, such as boundary representation (B-rep) and constructive solid geometry (CSG), have limitations in representing complex and organic shapes, often leading to issues such as geometric inaccuracies and topological errors. To address this, advanced modeling techniques such as non-uniform rational B-splines (NURBS) and subdivision surfaces have been developed toprovide more flexible and accurate representations of geometric shapes. However, these techniques also come with their own set of challenges, such as increased computational complexity and difficulty in controlling the shape of the model. In addition to technical challenges, geometric modeling also raises ethical and societal considerations, particularly in the context of digital representation and manipulation. As the boundary between physical and digital reality becomes increasingly blurred, issues such as intellectual property rights, privacy, and authenticity of digital models have become more prominent. For example, the unauthorized use and reproduction of digital models can lead to copyright infringement and legal disputes, highlighting the need for robust mechanisms to protect the intellectual property of digital content creators. Similarly, the rise of deepfakes and digital forgeries has raised concerns about the potential misuse of geometric modeling technology for malicious purposes, such as misinformation and identity theft. It is crucial for the industry to address these ethical concerns and develop standards and regulations to ensure the responsible use of geometric modeling technology. Despite these challenges, the field of geometric modeling continues to evolve and advance, driven by the growing demand forrealistic and interactive digital experiences. Recent developments in machine learning and artificial intelligence have shown promise in addressing some of the technical limitations of geometric modeling, such as automated feature recognition and shape optimization. Furthermore, the increasing availability of powerful hardware and software tools has enabled more efficient and accessible geometric modeling workflows, empowering designers and artists to create intricate and immersive digital content. With ongoing research and innovation, it is likely that many of the current challenges in geometric modeling will be overcome, leading to more sophisticated and versatile tools for digital design and visualization. In conclusion, geometric modeling is a critical component of modern digital design and visualization, enabling the creation and manipulation of complex geometric shapes and structures. However, the field faces several challenges related to the representation, integration, and ethical implications of geometric models. By addressing these challenges through technological innovation and ethical considerations, the industry can continue to push the boundaries of what ispossible in digital design and create more immersive and impactful experiences for users.。
Autodesk Fusion 360 3D 制造教程指导书说明书
Introduction to 3D Modeling for ManufacturingInstructor guideCourse duration: ~930 minutesLevel: BeginnerProduct: Autodesk® Fusion 360This instructor guide is a comprehensive tool for facilitating this in the classroom. Prepare to teach this course by thoroughly reviewing this document, as well as all related course materials and resources.We’ve summarized the core Fusion 360 skills in Introduction to 3D Modeling for Manufacturing course so you can familiarize yourself with them before delivering this learning content in the classroom. It’s always recommended that you work through the course yourself in preparation for each lesson.Learning objectives:•Summarize the parametric CAD workflow.•Identify interface aspects of Fusion 360.•Create parametric and freeform designs with mechanical motion.•Apply appearances and physical materials.•Create a detailed technical drawing.•Create rendered images.Each module is listed below along with suggested time allocations for instruction. The referenced demonstrations are based on the step-by-step instruction included in the course. Review the video tutorials and step-by-step print guides for the detailed instruction in each module.This course teaches several topics covered in the Autodesk Certified Associate in CAD for Mechanical Design certification exam. We’ve included relevant certification exam objectives covered within each module.Getting startedTotal time required: 20 minutes Discuss objectives: 3 minutes Demonstrate: 10 minutes•Review course overview andlearning objectives•Download the course resources and software•Create an Autodesk ID•Install the software•Review the starter activity and articlesHands-on time: 5 minutesReview objectives: 2 minutes Introduction to modelingTotal time required: 110 minutes Discuss objectives: 3 minutes Demonstrate: 15 minutes•Create a project in the data panel •Create and edit a sketch•Create and edit a 3D model•Produce a 2D drawingHands-on time: 35 minutesReview objectives: 2 minutes Datasets:reciprocating saw.f3dbrake rotor.f3d Certification exam objectives:• 1.1.1. Digital Project Creation• 1.1.2. Sub-folder Design andManagement• 1.1.3. Import of Legacy Data• 2.1.1. Create a sketch on a plane or planar face• 2.1.2. Edit a sketch• 2.1.3. Apply dimensions to a sketch• 2.1.4. Apply constraints to a sketch• 2.3.1. Create solid features• 2.3.3. Apply a fillet or chamfer• 4.1.1. Create a detailed drawing froma designAssignments:•Practice exercise: 10 minuteso Sketch constraints.f3d •Challenge exercise: 30 minuteso Simple bolt.f3d•Module quiz: 15 minutes • 4.1.2. Place views on a drawing sheet • 4.1.4. Add drawing model dimensions and notesIntroduction to parametric sketchingTotal time required: 110 minutesDiscuss objectives: 3 minutesDemonstration: 15 minutes•Create a sketch reference plane•Create sketch parameters•Link dimensions and parameters •Create a sketch spline•Project sketch geometry Hands-on time: 30 minutesReview objectives: 2 minutesDatasets:Sketch projections.f3d Assignments:•Practice exercise: 15 minuteso Saw blade.f3d•Challenge exercise: 30 minuteso Gear Housing Cover.f3d Module quiz: 15 minutes Certification exam objectives:• 2.1.6. Create a sketch projection from an edge or face.• 2.2.1. Create an offset construction plane.Introduction to parametric modelingTotal time required: 150 minutes Discuss objectives: 3 minutes Demonstration: 15 minutes•Use a canvas image•Project edges into a sketch•Create a sketch spline•Apply appearances and physical materialsHands-on time: 35 minutesReview objectives: 2 minutes Datasets:•defeature link.f3d•saw_image.png•Assignments:•Practice exercise: 15 minuteso Trigger lock.f3d•Challenge exercise: 60 minuteso Blade Release.f3d•Module quiz: 15 minutes Certification exam objectives:• 2.1.1. Create a sketch on a plane or planar face.• 2.1.3. Apply dimensions to a sketch.• 2.1.4. Apply constraints to a sketch.• 2.3.1. Create solid features.• 2.3.3. Apply a fillet or chamfer.Introduction to freeform and direct modelingTotal time required: 150 minutesDiscuss objectives: 3 minutesDemonstration: 15 minutes•Create a form body•Use Edit Form•Insert edges•Merge edges and vertices•Use direct modeling tools Hands-on time: 40 minutesReview objectives: 2 minutesDatasets:Trigger with mechanics.f3dSaw handle casing.f3d Assignments:•Practice exercise: 15 minuteso crease.f3d•Challenge exercise: 60 minuteso Trigger form.f3d Module quiz: 15 minutes Certification exam objectives:• 2.5.1. Demonstrate how to use the press pull feature.• 2.5.2. Use Delete to remove a feature.• 2.6.3. Use Edit Form to scale aselection.• 2.6.4. Use Edit form to translate aselection.• 2.6.5. Use Edit Form to rotate aselection.• 2.6.6. Use Thicken to convert a surface to a solid form body.Introduction to assembly modelingTotal time required: 105 minutes Discuss objectives: 3 minutes Demonstration: 15 minutes•Create a component•Create a joint•Edit joint limits•Drive a joint•Create a rigid groupHands-on time: 25 minutes Review objectives: 2 minutes Datasets:Reciprocating saw motion.f3d Assignments:•Practice exercise: 15 minuteso General joints.f3d•Challenge exercise: 30 minuteso Assembly Modeling.f3d •Module quiz: 15 minutes Certification exam objectives:• 3.1.1. Create a component from abody.• 3.1.2. Create a new empty component.• 3.1.3. Organize and manage assembly components.• 3.2.1. Use Align and Capture Position to position components.• 3.2.2. Apply an as-built revolute joint.• 3.2.3. Apply a slider joint.• 3.2.4. Create a rigid group ofcomponents.• 3.3.1. Create a motion link.3.3.2. Edit a motion link.Introduction to technical drawingsTotal time required: 135 minutes Discuss objectives: 3 minutes Demonstration: 15 minutes•Create an exploded viewanimation•Place views on a drawing sheet•Add dimensions and annotations to a drawing•Add GD&T annotations•Create a parts table•Modify a title blockHands-on time: 30 minutesReview objectives: 2 minutes Datasets:Blade guard assembly.f3d Assignments:•Practice exercise: 10 minuteso Projected view.f3d•Challenge exercise: 60 minuteso Saw Mechanism.f3d•Module quiz: 15 minutes Certification exam objectives:• 4.1.1. Create a detailed drawing froma design.• 4.1.2. Place views on a drawing sheet.• 4.1.3. Edit a drawing view.• 4.1.4. Add drawing model dimensions and notes.4.1.5. Modify a drawing title block.Introduction to renderingTotal time required: 90 minutesDiscuss objectives: 3 minutesDemonstration: 15 minutes•Create a form body•Use Edit Form•Insert edges•Merge edges and vertices•Use direct modeling tools Hands-on time: 15 minutesReview objectives: 2 minutesDatasets:Reciprocating saw.f3d Assignments:•Practice exercise: 10 minuteso Custom appearance.f3d •Challenge exercise: 30 minuteso Saw Casing Concept.f3d Module quiz: 15 minutes Certification exam objectives: n/aAssignments:•Course Assessment: 45 minutes •Course Challenge: 120 minuteso Saw Assembly.f3d Module: Next stepsTotal time required: 30 minutes Review individual student outcomes for end of course test: 10 minutesCreate a student study plan: 10 minutes Retest using the end of course test: 5 minutes Review certification objectives: 5 minutes。
Introduction_to_Modeling_and_Simulation[1]
![Introduction_to_Modeling_and_Simulation[1]](https://img.taocdn.com/s3/m/25c398d8b14e852458fb571b.png)
INTRODUCTION TO MODELING AND SIMULATIONAnu MariaState University of New York at Binghamton Department of Systems Science and Industrial Engineering Binghamton, NY 13902-6000, U.S.A.ABSTRACTThis introductory tutorial is an overview of simulation modeling and analysis. Many critical questions are answered in the paper. What is modeling? What is simulation? What is simulation modeling and analysis? What types of problems are suitable for simulation? How to select simulation software? What are the benefits and pitfalls in modeling and simulation? The intended audience is those unfamiliar with the area of discrete event simulation as well as beginners looking for an overview of the area. This includes anyone who is involved in system design and modification - system analysts, management personnel, engineers, military planners, economists, banking analysts, and computer scientists. Familiarity with probability and statistics is assumed.1WHAT IS MODELING?Modeling is the process of producing a model; a model is a representation of the construction and working of some system of interest. A model is similar to but simpler than the system it represents. One purpose of a model is to enable the analyst to predict the effect of changes to the system. On the one hand, a model should be a close approximation to the real system and incorporate most of its salient features. On the other hand, it should not be so complex that it is impossible to understand and experiment with it. A good model is a judicious tradeoff between realism and simplicity. Simulation practitioners recommend increasing the complexity of a model iteratively. An important issue in modeling is model validity. Model validation techniques include simulating the model under known input conditions and comparing model output with system output.Generally, a model intended for a simulation study is a mathematical model developed with the help of simulation software. Mathematical model classifications include deterministic (input and output variables are fixed values) or stochastic (at least one of the input or output variables is probabilistic); static (time is not taken into account) or dynamic (time-varying interactions among variables are taken into account). Typically, simulation models are stochastic and dynamic.2WHAT IS SIMULATION?A simulation of a system is the operation of a model of the system. The model can be reconfigured and experimented with; usually, this is impossible, too expensive or impractical to do in the system it represents. The operation of the model can be studied, and hence, properties concerning the behavior of the actual system or its subsystem can be inferred. In its broadest sense, simulation is a tool to evaluate the performance of a system, existing or proposed, under different configurations of interest and over long periods of real time.Simulation is used before an existing system is altered or a new system built, to reduce the chances of failure to meet specifications, to eliminate unforeseen bottlenecks, to prevent under or over-utilization of resources, and to optimize system performance. For instance, simulation can be used to answer questions like: What is the best design for a new telecommunications network? What are the associated resource requirements? How will a telecommunication network perform when the traffic load increases by 50%? How will a new routing algorithm affect its performance? Which network protocol optimizes network performance? What will be the impact of a link failure?The subject of this tutorial is discrete event simulation in which the central assumption is that the system changes instantaneously in response to certain discrete events. For instance, in an M/M/1 queue - a single server queuing process in which time between arrivals and service time are exponential - an arrival causes the system to change instantaneously. On the other hand, continuous simulators, like flight simulators and weather simulators, attempt to quantify the changes in a system continuously over time in response toProceedings of the 1997 Winter Simulation Conferenceed. S. Andradóttir, K. J. Healy, D. H. Withers, and B. L. Nelson7controls. Discrete event simulation is less detailed (coarser in its smallest time unit) than continuous simulation but it is much simpler to implement, and hence, is used in a wide variety of situations.Figure 1 is a schematic of a simulation study. The iterative nature of the process is indicated by the system under study becoming the altered system which then becomes the system under study and the cycle repeats. In a simulation study, human decision making is required at all stages, namely, model development, experiment design, output analysis, conclusion formulation, and making decisions to alter the system under study. The only stage where human intervention is not required is the running of the simulations, which most simulation software packages perform efficiently. The important point is that powerful simulation software is merely a hygiene factor - its absence can hurt a simulation study but its presence will not ensure success. Experienced problem formulators and simulation modelers and analysts are indispensable for a successful simulation study.Figure 1: Simulation Study Schematic The steps involved in developing a simulation model, designing a simulation experiment, and performing simulation analysis are:Step 1.Identify the problem.Step 2.Formulate the problem.Step 3.Collect and process real system data.Step 4.Formulate and develop a model.Step 5.Validate the model.Step 6.Document model for future use.Step 7.Select appropriate experimental design.Step 8.Establish experimental conditions for runs.Step 9.Perform simulation runs.Step 10.Interpret and present results.Step 11.Recommend further course of action. Although this is a logical ordering of steps in a simulation study, many iterations at various sub-stages may be required before the objectives of a simulation study are achieved. Not all the steps may be possible and/or required. On the other hand, additional steps may have to be performed. The next three sections describe these steps in detail.3HOW TO DEVELOP A SIMULATION MODEL?Simulation models consist of the following components: system entities, input variables, performance measures, and functional relationships. For instance in a simulation model of an M/M/1 queue, the server and the queue are system entities, arrival rate and service rate are input variables, mean wait time and maximum queue length are performance measures, and 'time in system = wait time + service time' is an example of a functional relationship. Almost all simulation software packages provide constructs to model each of the above components. Modeling is arguably the most important part of a simulation study. Indeed, a simulation study is as good as the simulation model. Simulation modeling comprises the following steps:Step 1.Identify the problem. Enumerate problems with an existing system. Produce requirements for a proposed system.Step 2.Formulate the problem. Select the bounds of the system, the problem or a part thereof, to be studied. Define overall objective of the study and a few specific issues to be addressed. Define performance measures - quantitative criteria on the basis of which different system configurations will be compared and ranked. Identify, briefly at this stage, the configurations of interest and formulate hypotheses about system performance. Decide the time frame of the study, i.e., will the model be used for a one-time decision (e.g., capital expenditure) or over a period of time on a regular basis (e.g., air traffic scheduling). Identify the end user of the simulation model, e.g., corporate management versus a production supervisor. Problems must be formulated as precisely as possible.Step 3.Collect and process real system data. Collect data on system specifications (e.g., bandwidth for a communication network), input variables, as well as8Mariaperformance of the existing system. Identify sources of randomness in the system, i.e., the stochastic input variables. Select an appropriate input probability distribution for each stochastic input variable and estimate corresponding parameter(s).Software packages for distribution fitting and selection include ExpertFit, BestFit, and add-ons in some standard statistical packages. These aids combine goodness-of-fit tests, e.g., χ2 test, Kolmogorov-Smirnov test, and Anderson-Darling test, and parameter estimation in a user friendly format.Standard distributions, e.g., exponential, Poisson, normal, hyperexponential, etc., are easy to model and simulate. Although most simulation software packages include many distributions as a standard feature, issues relating to random number generators and generating random variates from various distributions are pertinent and should be looked into. Empirical distributions are used when standard distributions are not appropriate or do not fit the available system data. Triangular, uniform or normal distribution is used as a first guess when no data are available. For a detailed treatment of probability distributions see Maria and Zhang (1997).Step 4.Formulate and develop a model. Develop schematics and network diagrams of the system (How do entities flow through the system?). Translate these conceptual models to simulation software acceptable form. Verify that the simulation model executes as intended. Verification techniques include traces, varying input parameters over their acceptable range and checking the output, substituting constants for random variables and manually checking results, and animation.Step 5.Validate the model. Compare the model's performance under known conditions with the performance of the real system. Perform statistical inference tests and get the model examined by system experts. Assess the confidence that the end user places on the model and address problems if any. For major simulation studies, experienced consultants advocate a structured presentation of the model by the simulation analyst(s) before an audience of management and system experts. This not only ensures that the model assumptions are correct, complete and consistent, but also enhances confidence in the model.Step 6.Document model for future use. Document objectives, assumptions and input variables in detail.4 HOW TO DESIGN A SIMULATION EXPERIMENT?A simulation experiment is a test or a series of tests in which meaningful changes are made to the input variables of a simulation model so that we may observe and identify the reasons for changes in the performance measures. The number of experiments in a simulation study is greater than or equal to the number of questions being asked about the model (e.g., Is there a significant difference between the mean delay in communication networks A and B?, Which network has the least delay: A, B, or C? How will a new routing algorithm affect the performance of network B?). Design of a simulation experiment involves answering the question: what data need to be obtained, in what form, and how much? The following steps illustrate the process of designing a simulation experiment.Step 7.Select appropriate experimental design. Select a performance measure, a few input variables that are likely to influence it, and the levels of each input variable. When the number of possible configurations (product of the number of input variables and the levels of each input variable) is large and the simulation model is complex, common second-order design classes including central composite, Box-Behnken, and full-factorial should be considered. Document the experimental design.Step 8.Establish experimental conditions for runs. Address the question of obtaining accurate information and the most information from each run. Determine if the system is stationary (performance measure does not change over time) or non-stationary (performance measure changes over time). Generally, in stationary systems, steady-state behavior of the response variable is of interest. Ascertain whether a terminating or a non-terminating simulation run is appropriate. Select the run length. Select appropriate starting conditions (e.g., empty and idle, five customers in queue at time 0). Select the length of the warm-up period, if required. Decide the number of independent runs - each run uses a different random number stream and the same starting conditions -by considering output data sample size. Sample size must be large enough (at least 3-5 runs for each configuration) to provide the required confidence in the performance measure estimates. Alternately, use common random numbers to compare alternative configurations by using a separate random number stream for each sampling process in a configuration. Identify output data most likely to be correlated.Step 9.Perform simulation runs. Perform runs according to steps 7-8 above.5 HOW TO PERFORM SIMULATION ANALYSIS?Introduction to Modeling and Simulation 9Most simulation packages provide run statistics (mean,standard deviation, minimum value, maximum value) on the performance measures, e.g., wait time (non-time persistent statistic), inventory on hand (time persistent statistic). Let the mean wait time in an M/M/1 queue observed from n runs be n 21W ...,,W ,W . It is important to understand that the mean wait time W is a random variable and the objective of output analysis is to estimate the true mean of W and to quantify its variability.Notwithstanding the facts that there are no data collection errors in simulation, the underlying model is fully known, and replications and configurations are user controlled, simulation results are difficult to interpret. An observation may be due to system characteristics or just a random occurrence. Normally, statistical inference can assess the significance of an observed phenomenon, but most statistical inference techniques assume independent, identically distributed (iid) data. Most types of simulation data are autocorrelated, and hence, do not satisfy this assumption. Analysis of simulation output data consists of the following steps.Step 10.Interpret and present results. Compute numerical estimates (e.g., mean, confidence intervals) of the desired performance measure for each configuration of interest. To obtain confidence intervals for the mean of autocorrelated data, the technique of batch means can be used. In batch means, original contiguous data set from a run is replaced with a smaller data set containing the means of contiguous batches of original observations.The assumption that batch means are independent may not always be true; increasing total sample size and increasing the batch length may help.Test hypotheses about system performance.Construct graphical displays (e.g., pie charts, histograms)of the output data. Document results and conclusions.Step 11.Recommend further course of action. This may include further experiments to increase the precision and reduce the bias of estimators, to perform sensitivity analyses, etc.6AN EXAMPLEA machine shop contains two drills, one straightener, and one finishing operator. Figure 2 shows a schematic of the machine shop. Two types of parts enter the machine shop.in sequence. Type 2 parts require only drilling and finishing. The frequency of arrival and the time to be routed to the drilling area are deterministic for both types of parts.Step 1.Identify the problem. The utilization of drills, straightener, and finishing operator needs to be assessed. In addition, the following modification to the original system is of interest: the frequency of arrival of both parts is exponential with the same respective means as in the original system.Step 2.Formulate the problem. The objective is to obtain the utilization of drills, straightener, and finishing operator for the original system and the modification . The assumptions include:♦The two drills are identical♦There is no material handling time between the threeoperations.♦Machine availability implies operator availability.♦Parts are processed on a FIFO basis.♦All times are in minutes.Step 3.Collect and process real system data. At the job shop, a Type 1 part arrives every 30 minutes, and a Type 2 part arrives every 20 minutes. It takes 2 minutes to route a Type 1 part and 10 minutes to route a Type 2 part to the drilling area. Parts wait in a queue till one of the two drilling machines becomes available. After drilling, Type 1parts are routed to the straightener and Type 2 parts are10Mariarouted to the finishing operator. After straightening, Type 1 parts are routed to the finishing operator.The operation times for either part were determined to be as follows. Drilling time is normally distributed with mean 10.0 and standard deviation 1.0. Straightening time is exponentially distributed with a mean of 15.0. Finishing requires 5 minutes per part.Step 4.Formulate and develop a model. A model of the system and the modification was developed using a simulation package. A trace verified that the parts flowed through the job shop as expected.Step 5.Validate the model. The utilization for a sufficiently long run of the original system was judged to be reasonable by the machine shop operators.Step 6.Document model for future use. The models of the original system and the modification were documented as thoroughly as possible.Step 7.Select appropriate experimental design. The original system and the modification described above were studied.Step 8.Establish experimental conditions for runs. Each model was run three times for 4000 minutes and statistical registers were cleared at time 1000, so the statistics below were collected on the time interval [1000, 4000]. At the beginning of a simulation run, there were no parts in the machine shop.Step 9.Perform simulation runs. Runs were performed as specified in Step 8 above.Step 10.Interpret and present results. Table 1 contains the utilization statistics of the three operations for the original system and the modification (in parentheses).Table 1: Utilization StatisticsDrilling Straightening Finishing Mean Run #1 0.83 (0.78) 0.51 (0.58) 0.42 (0.39) Mean Run #2 0.82 (0.90) 0.52 (0.49) 0.41 (0.45) Mean Run #3 0.84 (0.81) 0.42 (0.56) 0.42 (0.40) Std. Dev. Run #1 0.69 (0.75) 0.50 (0.49) 0.49 (0.49) Std. Dev. Run #2 0.68 (0.78) 0.50 (0.50) 0.49 (0.50) Std. Dev. Run #3 0.69 (0.76) 0.49 (0.50) 0.49 (0.49) Mean utilization represents the fraction of time a server is busy, i.e., busy time/total time. Furthermore, the average utilization output for drilling must be divided by the number of drills in order to get the utilization per drill. Each drill is busy about 40% of the time and straightening and finishing operations are busy about half the time. This implies that for the given work load, the system is underutilized. Consequently, the average utilization did not change substantially between the original system and the modification; the standard deviation of the drilling operation seems to have increased because of the increased randomness in the modification. The statistical significance of these observations can be determined by computing confidence intervals on the mean utilization of the original and modified systems.Step 11.Recommend further course of action. Other performance measures of interest may be: throughput of parts for the system, mean time in system for both types of parts, average and maximum queue lengths for each operation. Other modifications of interest may be: the flow of parts to the machine shop doubles, the finishing operation will be repeated for 10% of the products on a probabilistic basis.7 WHAT MAKES A PROBLEM SUITABLE FOR SIMULATION MODELING AND ANALYSIS?In general, whenever there is a need to model and analyze randomness in a system, simulation is the tool of choice. More specifically, situations in which simulation modeling and analysis is used include the following:♦ It is impossible or extremely expensive to observe certain processes in the real world, e.g., next year's cancer statistics, performance of the next space shuttle, and the effect of Internet advertising on a company's sales.♦ Problems in which mathematical model can be formulated but analytic solutions are either impossible (e.g., job shop scheduling problem, high-order difference equations) or too complicated (e.g., complex systems like the stock market, and large scale queuing models).♦ It is impossible or extremely expensive to validate the mathematical model describing the system, e.g., due to insufficient data.Applications of simulation abound in the areas of government, defense, computer and communication systems, manufacturing, transportation (air traffic control), health care, ecology and environment, sociological and behavioral studies, biosciences, epidemiology, services (bank teller scheduling), economics and business analysis.8 HOW TO SELECT SIMULATION SOFTWARE?Although a simulation model can be built using general purpose programming languages which are familiar to the analyst, available over a wide variety of platforms, and less expensive, most simulation studies today are implemented using a simulation package. TheIntroduction to Modeling and Simulation 11advantages are reduced programming requirements; natural framework for simulation modeling; conceptual guidance; automated gathering of statistics; graphic symbolism for communication; animation; and increasingly, flexibility to change the model. There are hundreds of simulation products on the market, many with price tags of $15,000 or more. Naturally, the question of how to select the best simulation software for an application arises. Metrics for evaluation include modeling flexibility, ease of use, modeling structure (hierarchical v/s flat; object-oriented v/s nested), code reusability, graphic user interface, animation, dynamic business graphics, hardware and software requirements, statistical capabilities, output reports and graphical plots, customer support, and documentation.The two types of simulation packages are simulation languages and application-oriented simulators (Table 2). Simulation languages offer more flexibility than the application-oriented simulators. On the other hand, languages require varying amounts of programming expertise. Application-oriented simulators are easier to learn and have modeling constructs closely related to the application. Most simulation packages incorporate animation which is excellent for communication and can be used to debug the simulation program; a "correct looking" animation, however, is not a guarantee of a valid model. More importantly, animation is not a substitute for output analysis.Table 2: Simulation PackagesType OfSimulationPackageExamplesSimulation languages Arena (previously SIMAN), AweSim! (previously SLAM II), Extend, GPSS, Micro Saint,SIMSCRIPT, SLXObject-oriented software: MODSIM III, SIMPLE++ Animation software: Proof AnimationApplication -Oriented Simulators Manufacturing: AutoMod, Extend+MFG,FACTOR/AIM, ManSim/X, MP$IM,ProModel, QUEST, Taylor II, WITNESS Communications/computer: COMNET III,NETWORK II.5, OPNET Modeler, OPNETPlanner, SES/Strategizer, SES/workbench Business: BP$IM, Extend+BPR, ProcessModel, ServiceModel, SIMPROCESS, Time machine Health Care: MedModel9BENEFITS OF SIMULATION MODELING AND ANALYSISAccording to practitioners, simulation modeling and analysis is one of the most frequently used operations research techniques. When used judiciously, simulation modeling and analysis makes it possible to:♦Obtain a better understanding of the system by developing a mathematical model of a system ofinterest, and observing the system's operation in detail over long periods of time.♦Test hypotheses about the system for feasibility.♦Compress time to observe certain phenomena over long periods or expand time to observe a complex phenomenon in detail.♦Study the effects of certain informational, organizational, environmental and policy changes on the operation of a system by altering the system's model; this can be done without disrupting the real system and significantly reduces the risk of experimenting with the real system.♦Experiment with new or unknown situations about which only weak information is available.♦Identify the "driving" variables - ones that performance measures are most sensitive to - and the inter-relationships among them.♦Identify bottlenecks in the flow of entities (material, people, etc.) or information.♦Use multiple performance metrics for analyzing system configurations.♦Employ a systems approach to problem solving.♦Develop well designed and robust systems and reduce system development time.10WHAT ARE SOME PITFALLS TO GUARD AGAINST IN SIMULATION?Simulation can be a time consuming and complex exercise, from modeling through output analysis, that necessitates the involvement of resident experts and decision makers in the entire process. Following is a checklist of pitfalls to guard against.♦Unclear objective.♦Using simulation when an analytic solution is appropriate.♦Invalid model.♦Simulation model too complex or too simple.♦Erroneous assumptions.♦Undocumented assumptions. This is extremely important and it is strongly suggested that assumptions made at each stage of the simulation modeling and analysis exercise be documented thoroughly.♦Using the wrong input probability distribution.♦Replacing a distribution (stochastic) by its mean (deterministic).♦Using the wrong performance measure.♦Bugs in the simulation program.♦Using standard statistical formulas that assume independence in simulation output analysis.♦Initial bias in output data.♦Making one simulation run for a configuration.12MariaIntroduction to Modeling and Simulation 13♦ Poor schedule and budget planning.♦ Poor communication among the personnel involvedin the simulation study.REFERENCESBanks, J., J. S. Carson, II, and B. L. Nelson. 1996.Discrete-Event System Simulation, Second Edition,Prentice Hall.Bratley, P., B. L. Fox, and L. E. Schrage. 1987. A Guideto Simulation, Second Edition, Springer-Verlag.Fishwick, P. A. 1995. Simulation Model Design andExecution: Building Digital Worlds, Prentice-Hall.Freund, J. E. 1992. Mathematical Statistics, Fifth Edition,Prentice-Hall.Hogg, R. V., and A. T. Craig. 1995. Introduction toMathematical Statistics, Fifth Edition, Prentice-Hall.Kleijnen, J. P. C. 1987. Statistical Tools for SimulationPractitioners, Marcel Dekker, New York.Law, A. M., and W. D. Kelton. 1991. SimulationModeling and Analysis, Second Edition,McGraw-Hill.Law, A. M., and M. G. McComas. 1991. Secrets ofSuccessful Simulation Studies, Proceedings of the1991 Winter Simulation Conference, ed. J. M.Charnes, D. M. Morrice, D. T. Brunner, and J. J.Swain, 21-27. Institute of Electrical and ElectronicsEngineers, Piscataway, New Jersey.Maria, A., and L. Zhang. 1997. Probability Distributions,Version 1.0, July 1997, Monograph, Department ofSystems Science and Industrial Engineering, SUNYat Binghamton, Binghamton, NY 13902.Montgomery, D. C. 1997. Design and Analysis ofExperiments, Third Edition, John Wiley.Naylor, T. H., J. L. Balintfy, D. S. Burdick, and K. Chu.1966. Computer Simulation Techniques, John Wiley.Nelson, B. L. 1995. Stochastic Modeling: Analysis andSimulation, McGraw-Hill.AUTHOR BIOGRAPHYANU MARIA is an assistant professor in the departmentof Systems Science & Industrial Engineering at the StateUniversity of New York at Binghamton. She receivedher PhD in Industrial Engineering from the University ofOklahoma. Her research interests include optimizing theperformance of materials used in electronic packaging(including solder paste, conductive adhesives, andunderfills), simulation optimization techniques, geneticsbased algorithms for optimization of problems with alarge number of continuous variables, multi criteriaoptimization, simulation, and interior-point methods.。
构建模型的英语
构建模型的英语Building a Model: Key Steps and ConsiderationsIntroduction:Building a model is a crucial process in various fields such as machine learning, data analysis, and statistical modeling. It involves constructing a representation of a system or phenomenon to understand, predict, or analyze its behavior. In this article, we will discuss the key steps and considerations involved in building a model.1. Defining the Problem:The first step in model building is to clearly define the problem or objective. This involves understanding what needs to be achieved, identifying the available data, and setting realistic expectations. A well-defined problem statement helps guide the entire modeling process.2. Data Collection and Preparation:Once the problem is defined, the next step is to gatherthe relevant data. This may involve sourcing it from various databases, utilizing existing datasets, or conducting experiments to generate new data. Data preparation is equally crucial, which includes cleaning, transforming, andformatting the data in a suitable manner for modeling.3. Exploratory Data Analysis (EDA):EDA involves analyzing the collected data to understandits characteristics, identify patterns, and detect outliers.It helps to gain insights into the data, validate assumptions, and select appropriate modeling techniques.4. Feature Selection and Engineering:Feature selection refers to the process of identifyingthe most relevant variables or features from the data that contribute significantly to the outcome. Feature engineeringinvolves creating new features or transforming existing ones to improve the model's performance. This step requires domain knowledge and creativity.5. Selecting a Modeling Technique:Choosing an appropriate modeling technique depends on the nature of the problem, available data, and desired outcome.It could range from traditional statistical methods such as linear regression and decision trees to more advanced techniques like neural networks and deep learning.6. Model Training and Evaluation:Once the modeling technique is selected, the model needs to be trained using the prepared data. This involvessplitting the data into training and validation sets,defining performance metrics, and fine-tuning the model parameters. After training, the model's performance is evaluated using various metrics such as accuracy, precision, recall, or mean squared error.7. Model Optimization:Model optimization aims to improve the model's performance by fine-tuning its parameters or exploring different algorithms. Techniques like cross-validation, hyperparameter tuning, and regularization can be employed to prevent overfitting and achieve better generalization.8. Model Deployment and Monitoring:After optimizing the model, it is deployed for real-world usage, integrating it into the existing architecture or systems. It is important to continuously monitor the model's performance, assess its accuracy, and retrain or update it periodically to adapt to changing data patterns.9. Model Interpretation and Communication:Model interpretation helps in understanding the factors influencing the model's predictions and gaining insights. It is crucial for decision-making and explaining the model'soutcomes to stakeholders. Effectively communicating the model's results and limitations is essential for gainingtrust and facilitating its practical implementation.Conclusion:Building a model involves a series of well-defined steps, from problem definition to model interpretation. Each step requires careful consideration and expertise in order to develop an accurate and reliable model. By following these steps and considering the specific requirements of the problem at hand, one can build effective models to gain insights, make predictions, or solve complex problems in various industries.。
西门子PLM软件Parasolid 产品介绍说明书
Siemens PLM Software ParasolidSummaryParasolid® software is the world’s premier 3D geometric modeling component, selected by leading application vendors and end-user organizations spanning multiple industries as their preferred platform for delivering innovative 3D solutions with unparalleled modeling power, versatility and interoperability. A key offering within Siemens PLM Software’s PLM Components family of software products, Parasolid is tar-geted at a broad range of applications across the product lifecycle and provides robust, high-quality functionality that is easy to use and cost-effective to implement.World-class geometric modeling for demanding 3D applications Parasolid supports solid modeling, facet modeling, generalized cellular modeling, direct modeling and freeform surface/sheet modeling within an integrated framework. Parasolid is available in three commercial packages: Designer, Editor and Communicator – each of which is offered with convergent modeling technology as an option – and is also available to the aca-demic community via an Educator package. The functional scope and typical application at each level are outlined below. The table on the next page summarizes the corre-sponding functionality.Parasolid Designer delivers the full power of Parasolid functionality for unlimited creation, manipulation, interrogation and storage of 3D models. Over 900 object-based API functions provide the most comprehensive and robust 3D modeling platform for demanding 3D applications. Parasolid Editor provides an extended subset of Parasolid functionality that is ideal for analysis, manufacturing and other downstream applications that need to easily manipulate, edit, repair or simplify 3D models without the need for advanced modeling operations.Parasolid benefits• Provides ideal foundationfor innovative 3D applica-tion development• Reduces development costsand risks by providing aproven 3D modelingsolution• Ensures state-of-the-artquality and robustness• Convergent modeling tech-nology seamlesslyintegrates classic b-rep andfacet b-rep modeling opera-tions in a unifiedarchitecture• Offers world-class technicalsupport for rapidtime-to-market• Enables instantcompatibility with otherParasolid-based applicationsthrough translation-freeexchange of 3D data/plmcomponentsParasolidPLM COMPONENTSParasolid Communicator comprises versatile base functionality, including interoper a bility, visualization and data interro g ation capabili-ties, that provides a platform for applications to consume existing 3D models.Parasolid Educator, complementing the above commercial packages, provides academic institutions with the full power of Parasolid functionality for teaching,research and industrial collaboration.Parasolid facts• Fully integrated modeling of 3D curves, surfaces and solids with over 900 API functions • Modeling foundation for hundreds of the world’s leading CAD, CAM and CAE applications• Corporate standard forSiemens’ NX™, Solid Edge®, Femap™ and Teamcenter® software solutions • Used in over 3.5 million seats of application soft-ware globally• Licensed by 170 software vendors for integration into more than 350 applications • Provides industry-leading robustness with over a mil-lion quality tests run daily • Provides unmatched two-way data compatibility via Parasolid native XT format Parasolid usageParasolid is the component of choice for both cloud-based solutions and traditional stand-alone workstations. Parasolid is deployed across a wide range of PLM application domains, including:• Mechanical CAD • CAM/manufacturing • CAE/validation • AEC• Visualization • Data exchange • Interoperability • Knowledge-based engineering • CMM/inspection • CNC/machine tools • Corporate R&D • Academic R&DPLM COMPONENTSFoundation capabilitiesParasolid is built on critical foundation capabilities that enable Parasolid to be deployed successfully in a wide variety of software applications. Enabled across all relevant functionality, Parasolid foundation capabilities include:• Tolerant modeling for intrinsically reliable modeling with imported data• Convergent modeling technology, available as a licensed option, seamlessly integrates classic b-rep and facet b-rep modeling operations in a unified architecture• Attributes and callbacks for application-specific character-istics and behavior• Session and partitioned rollback for flexible history and undo/redo implementation• Data management and tracking for managing models and associated data as they evolve• Thread safety and symmetric multi-processing support for optimal performance on multi-processor machines• Model storage in forwards and backwards compatible native XT format• .NET Binding to integrate Parasolid into .NET applications written in C#• Broad platform coverage including comprehensive support for Windows, Linux, Unix and MacGetting startedParasolid is delivered with a compre h ensive set of documen-tation and developer resources, including a complete Jumpstart Kit of tools that promote easy integration of Parasolid into new and existing applications:• Full Product Documentation Suite in html and pdf formats • Parasolid Workshop prototypingenvironment for Windows• Example Application Resourcesto get you up and running• Code Example Suite illustratesbest implementation practice• Parasolid ‘Getting Started’ Guideanswers your questions• Parasolid Overview summarizesParasolid capabilities• Parasolid API Training Materialsto educate the team Support, training and consultingParasolid has a renowned technical support, training and consulting team, dedicated to helping customers achieve the best possible implementation by providing expert advice on all matters related to Parasolid usage.Responsive telephone and email support is backed by an online support center that provides round-the-clock access to frequent product updates, as well as customer-specific issue reporting and tracking.In addition, specialized training and consulting services are available that can be tailored to customer requirements. Whether you are starting fresh, extending an existing appli-cation or transitioning from other modeling technology, the Parasolid support, training and consulting team is with you every step of the way.Interoperability productsThe Parasolid product suite is augmented by a range of add-on products that provide high-quality interoperability with third-party CAD data. These include Parasolid Bodyshop, a specialized tool for boosting the success of 3D data exchange by cleaning and repairing imported models, and Parasolid Translator toolkits for converting model data between Parasolid and other major standard and proprietary CAD formats, including STEP, IGES, Catia V4, Catia V5, Pro/ Engineer and ACIS(SAT).Siemens PLM Software partners with Tech Soft 3D to offer Hoops Exchange. This highly-integrated and industry-proven 3D data collaboration solution for Parasolid provides high performance import, export, healing and visualization toolsfor a wide range of 3D file formats.Siemens PLM SoftwareAmericas +1 314 264 8499Europe +44 (0) 1276 413200Asia-Pacific +852 2230 3308/plmrespective holders.5661-Y7 3/16 BConvergent modeling: facetmodel of knee joint withb-rep surgical guide, mod-eled in single architecture.。
机械CADChapter 4 Modelling of Solids
A solid model contains both the geometry data and topological data. Topological data describe the connectivity and associability of the object entities. Solid model generation is often not unique, that is, there are often several different ways to create a solid model. CAD users need to generate solid models which can make the computer storage small and suited for later utilize.
①Domain:
While no representation can describe all possible solids, a representation should be able to represent a useful set of geometric objects. Domain should give a useful set of physical objects that can be represented.
Boundary representation (B-rep) and Constructive Solid Geometry (CSG) are two most popular schemes for solid modeling. B-reps are based on the topological notion that an object is bounded by faces. CSG is based on that an object can be divided into a set of primitives. Table 4.1 shows some widely used CAD systems.
介绍模型的特点英语作文
介绍模型的特点英语作文The Unique Characteristics of Our Model.In the ever-evolving landscape of technological advancements, the introduction of innovative models has become paramount for businesses, researchers, and enthusiasts alike. The model we present here is a culmination of rigorous research, experimentation, and innovation, aimed at addressing complex problems and fulfilling diverse requirements. Its uniqueness lies not only in its design but also in its versatility, efficiency, and potential for growth.Versatility as a Core Principle.One of the defining features of our model is its versatility. Unlike traditional models that are often confined to specific tasks or applications, our model is designed to adapt and transform based on varying requirements. This versatility is achieved through the useof modular components that can be easily swapped or modified, allowing the model to be tailored to specific needs. Whether it's data analysis, machine learning, or simulation modeling, our model can be configured to deliver optimal performance in a wide range of scenarios.Efficiency through Advanced Algorithms.Efficiency is another hallmark of our model. It is powered by state-of-the-art algorithms that have been optimized for speed and accuracy. These algorithms are designed to handle large datasets efficiently, reducing processing time and improving overall performance.。
多相流数值模拟一
CHMLTECH501Computational Fluid Dynamics Introduction to Modeling Multiphase FlowsA large number offlows encountered in chemical engineering are a mixture of phases.Phys-ical phases of matter are gas,liquid,and solid,but the concept of phase in a multiphase flow system is applied in a broader sense.In multiphaseflow,a phase can be defined as an identifiable class of material that has a particular inertial response to and interaction with theflow and the potentialfield in which it is immersed.For example,different-sized solid particle of the same material can be treated as different phases because each collection of particles with the same size will have a similar dynamical response to theflowfield. Multiphase Flow RegimeMultiphaseflow can be classified by the following regimes,grouped into four categories:•gas-liquid or liquid-liquidflows–bubblyflow:discrete gaseous orfluid bubbles in a continuousfluid.–dropletflow:discretefluid droplets in a continuous gas.–slugflow:large bubbles in a continuousfluid.–stratified/free-surfaceflow:immisciblefluids separated by a clearly-defined in-terface.•gas-solidflows–particle-ladenflow:discrete solid particles in a continuous gas–pneumatic transport:flow pattern depends on factors such as solid loading, Reynolds numbers,and particle properties.Typical patterns are duneflow,slugflow,packed beds,and homogeneousflow.–fluidized beds:consists of a vertical cylinder containing particles where gas is introduced through a distributor.The gas rising through the bed suspends theparticles.Depending on the gasflow rate,bubbles appear and rise through thebed,intensifying the mixing within the bed.•liquid-solidflows–slurryflow:transport of particles in liquids.the fundamental behavior of liquid-solidflows varies with the properties of the solid particles relative to those ofthe liquid.In slurryflows,the Stokes number(St=τd/t s)is normally less than1.When the Stokes number is larger than1,the characteristic of theflow isliquid-solidfluidization.–hydrotransport:densely-distributed solid particles in a continuous liquid.–sedimentation:a tall column initially containing a uniform dispersed mixture of particles.At the bottom,the particles will slow down and form a sludge layer.At the top,a clear interface will appear,and in the middle a constant settlingzone will exist.•three-phaseflows,e.g.gas-liquid-solidflows.1Each of theseflow regimes is illustrated in Figure(1).Figure1:Multiphase Flow Regimes.2Examples of Multiphase SystemsSpecific examples of each regime are listed below:•Bubblyflow examples:absorbers,aeration,air lift pumps,cavitation,evaporators,flotation,scrubbers•Dropletflow examples:absorbers,atomizers,combustors,cryogenic pumping,dryers, evaporation,gas cooling,scrubbers•Slugflow examples:large bubble motion in pipes or tanks.•Stratified/free-surfaceflow examples:sloshing in offshore separator devices,boiling and condensation in nuclear reactors•Particle-ladenflow examples:cyclone separators,air classifiers,dust collectors,and dust-laden environmentalflows•Pneumatic transport examples:transport of cement,grains,and metal powders •Fluidized bed examples:fluidized bed reactors,circulatingfluidized beds•Slurryflow examples:slurry transport,mineral processing•Hydrotransport examples:mineral processing,biomedical and physiochemicalfluid systems•Sedimentation examples:mineral processing3APPROACHES TO MULTIPHASE MODELINGAdvances in computationalfluid mechanics have provided the basis for further insight into the dynamics of multiphaseflows.Currently there are two approaches for the numerical cal-culation of multiphaseflows:the Euler-Langrange approach and the Euler-Euler approach. The Euler-Lagrange ApproachThe Lagrangian discrete phase model(in FLuent)follows the Euler-Langrange approach. Thefluid phase is treated as a continuum by solving the time-averaged Navier-Stokes equa-tions,while the dispersed phase is solved by tracking a large number of particles,bubbles, or droplets through the calculatedflowfield.The dispersed phase can exchange momentum, mass,and energy with thefluid phase.A fundamental assumption made in this model is that the dispersed second phase occu-pies a low volume fraction,even though high mass loading(m particles m fluid)is acceptable. The particle or droplet trajectories are computed individually at specified intervals during thefluid phase calculation.This makes the model appropriate for the modeling of spray dryers,coal and liquid combustion,and some particle-ladenflows,but inappropriate for the modeling of liquid-liquid mixtures,fluidized beds,or any application where the volume fraction of the second phase is not negligible.The Euler-Euler ApproachIn the Euler-Euler approach,the different phases are treated mathematically as interpene-trating continua.Since the volume of a phase cannot be occupied by the other phases,the concept of phasic volume fraction is introduced.These volume fractions are assumed to be continuous functions of space and time and their sum is equal to one.Conservation equa-tions for each phase are derived to obtain a set of equations,which have similar structure for all phases.These equations are closed by providing constitutive relations that are obtained from empirical information,or,in the case of granularflows,by application of kinetic energy.Three different Euler-Euler multiphase models(are available in FLUENT):the volume offluid(VOF),the mixture model,and Eulerian model.The VOF ModelThe VOF model is a surface-tracking technique applied to afixed Eulerian mesh.It is designed for two or more immisciblefluids where the position of the interface between the fluids is of interest.In the VOF model,a single set of momentum equations is shared by thefluids,and the volume fraction of each of thefluids in each computational cell is tracked throughout the domain.Applications of the VOF model include stratifiedflows,free-surface flows,filling,sloshing,the motion of large bubbles in a liquid,the motion of liquid after a dam break,the prediction of jet breakup(surface tension),and the steady or transient tracking of any liquid-gas interface.The Mixture ModelThe mixture model is designed for two or more phases(fluid or particulate).As in the Eulerian model,the phases are treated as interpenetrating continua.The mixture model solves for the mixture momentum equation and prescribes relative velocities to describe the dispersed phases.Applications of the mixture model include particle-ladenflows with low loading,bubblyflows,sedimentation,and cyclone separators.The mixture model can also4be used without relative velocities for the dispersed phases to model homogeneous multi-phaseflow.The Eulerian ModelThe Eulerian model is the most complex of the multiphase models(in FLUENT).It solves a set of n momentum and continuity equations for each phase.Coupling is achieved through the pressure and interphase exchange coefficients.The manner in which this coupling is handled depends upon the type of phases involved;granularflows,the properties are obtained from application of kinetic theory.Momentum exchange between the phases is also dependent upon the type of mixture being modeled.(FLUENT’s user-defined functions allow you to customize the calculation of the momentum exchange).Applications of the Eulerian mutiphase model include bubble columns,risers,particle suspension,andfluidized beds.5。
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Ken Youssefi
Mechanical Engineering Dept.
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Methods of Creating Solid Models
• Boundary Representation (B-rep), mostly used in finite element programs. • Constructive Solid Geometry (CSG), CAD packages; Unigraphics, AutoCAD – 3D modeler. • Parametric Modeling, CAD packages; Unigraphics, SolidWorks, Inventor by AutoDesk, Pro/Engineer, ….
• The face of a B-rep represents an oriented surface, there are two sides to the surface; solid side (inside) and void side (outside), unlike faces in a wireframe. • B-rep model is created using Euler operation
Ken Youssefi
Mechanical Engineering Dept.
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Wireframe Modeling
• Contains information about the locations of all the points (vertices) and edges in space coordinates. • Each vertex is defined by x, y, z coordinate. • Edges are defined by a pair of vertices. • Faces are defined as three or more edges. • Wireframe is a collection of edges, there iss all the advantages of surface models (uniqueness, non-ambiguous, realistic, surface profile) plus volumetric information. • Allows the designer to create multiple options for a design. • 2D standard drawings, assembly drawing and exploded views are generated form the 3D model.
Ken Youssefi Mechanical Engineering Dept.
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Volumetric and Mass properties of an object can be easily obtained.
Corresponding mass properties are obtained if density is included.
Ken Youssefi
Mechanical Engineering Dept.
5
Wireframe Modeling
Disadvantages: • Do not represent an actual solids (no surface and volume). • Cannot model complex curved surfaces. • Cannot be used to calculate dynamic properties. • Ambiguous views
Ken Youssefi
Mechanical Engineering Dept.
9
Surface Modeling
Disadvantages: • Surface models provide no information about the inside of an object. • Complicated computation, depending on the number of surfaces
Ken Youssefi
Mechanical Engineering Dept.
15
Solid Modeling
Boundary Representation (B-rep)
• A solid model is formed by defining the surfaces that form its boundary (edges and surfaces)
Ken Youssefi
Mechanical Engineering Dept.
6
Wireframe Modeling
• Uniqueness problem.
Ken Youssefi
Mechanical Engineering Dept.
7
Surface Modeling
A surface model represents the skin of an object, these skins have no thickness or material type. • Surface models define the surface features, as well as the edges, of objects. • A mathematical function describes the path of a curve (parametric techniques). • Surfaces are edited as single entities.
Ken Youssefi Mechanical Engineering Dept.
13
Solid Models
Disadvantages: • More intensive computation than wireframe and surface modeling. • Requires more powerful computers (faster with more memory and good graphics), not a problem any more.
Ken Youssefi
Mechanical Engineering Dept.
2
3D Modeling
There are three basic types of three-dimensional computer geometric modeling methods:
• Wireframe modeling • Surface modeling • Solid modeling
Introduction to Solid Modeling Parametric Modeling
Ken Youssefi
Mechanical Engineering Dept.
1
Why draw 3D Models?
• • • • 3D models are easier to interpret. Simulation under real-life conditions. Less expensive than building a physical model. 3D models can be used to perform finite element analysis (stress, deflection, thermal…..). • 3D models can be used directly in manufacturing, Computer Numerical Control (CNC). • Can be used for presentations and marketing.
Ken Youssefi
Mechanical Engineering Dept.
8
Surface Modeling
Advantages:
• Eliminates ambiguity and non-uniqueness present in wireframe models by hiding lines not seen. • Renders the model for better visualization and presentation, objects appear more realistic. • Provides the surface geometry for CNC machining. • Provides the geometry needed for mold and die design. • Can be used to design and analyze complex free-formed surfaces (ship hulls, airplane fuselages, car bodies, …). • Surface properties such as roughness, color and reflectivity can be assigned and demonstrated.
Ken Youssefi Mechanical Engineering Dept.
11
Solid Models
Advantages:
• Can easily be exported to different Finite Element Methods programs for analysis. • Can be used in newly manufacturing techniques; computer integrated manufacturing (CIM), computer aided manufacturing (CAM) and design for manufacturability ans assembly (DFM, DFA) • Mass and volumetric properties of an object can be easily obtained; total mass, mass center, area and mass moment of inertia, volume, radius of gyration, …