曲轴的加工工艺及夹具设计外文翻译

毕业设计外文翻译题目曲轴的加工工艺及夹具设计学院航海学院专业轮机工程学生佟宝诚学号 10960123 指导教师彭中波重庆交通大学2014年Proceedings of IMECE20082008 ASME International Mechanical Engineering Congress and ExpositionOctober 31-November 6, 2008, Boston, Massachusetts, USAIMECE2008-67447MULTI-OBJECTIVE SYSTEM OPTIMIZATION OF ENGINE CRANKSHAFTS USINGAN INTEGRATION APPROACHAlbert Albers/IPEK Institute of Product DevelopmentUniversity of Karlsruhe GermanyNoel Leon/CIDyT Center for Innovation andDesignMonterrey Institute of Technology,MexicoHumberto Aguayo/CIDyT Center forInnovation and Design,Monterrey Institute ofTechnology, MexicoThomas Maier/IPEK Institute of Product DevelopmentUniversity of Karlsruhe GermanyABSTRACTThe ever increasing computer capabilities allow faster analysis in the field of Computer Aided Design and Engineering (CAD & CAE). CAD and CAE systems are currently used in Parametric and Structural Optimization to find optimal topologies and shapes of given parts under certain conditions. This paper describes a general strategy to optimize the balance of a crankshaft, using CAD and CAE software integrated with Genetic Algorithms (GAs) via programming in Java. An introduction to the groundings of this strategy is made among different tools used for its implementation. The analyzed crankshaft is modeled in commercial parametric 3D CAD software. CAD is used for evaluating the fitness function (the balance) and to make geometric modifications. CAE is used for evaluating dynamic restrictions (the eigenfrequencies). A Java interface is programmed to link the CAD model to the CAE software and to the genetic algorithms. In order to make geometry modifications toour case study, it was decided to substitute the profile of the counterweights with splines from its original “arc-shaped” design. The variation of the splined profile via control points results in an imbalanceresponse. The imbalance of the crankshaft was defined as an independent objective function during a first approach, followed by a Pareto optimization of the imbalance from both correction planes, plus the curvature of the profile of the counterweights as restrictions for material flow during forging. The natural frequency was considered as an additional objective function during a second approach. The optimization process runs fully automated and the CAD program is on hold waiting for new set of parameters to receive and process, saving computing time, which is otherwise lost during the repeated startup of the cad application.The development of engine crankshafts is subject to a continuous evolution due to market pressures. Fast market developments push the increase of power, fuel economy, durability and reliability of combustion engines, and calls for reduction of size, weight, vibration and noise, cost, etc. Optimized engine components are therefore required if competitive designs must be attained. Due to this conditions, crankshafts, which are one of the most analyzed engine components, are required to be improved [1]. One of these improvements relies on material composition, as companies that develop combustion engines have expressed their intentions to change actual nodular steel crankshafts from their engines, to forged steel crankshafts. Another important direction of improvement is the optimization of its geometrical characteristics. In particular for this paper is the imbalance, first Eigen-frequency and the forge-ability. Analytical tools can greatly enhance the understanding of the physical phenomena associated with the mentioned characteristics and can be automated to do programmed tasks that an engineer requires for optimizing a design [2].The goals of the present research are: to construct a strategy for the development of engine crankshafts based on the integration of: CAD and CAE (Computer Aided Design &Engineering) software to model and evaluate functionalparameters, Genetic Algorithms as the optimization method, the use of splines for shape construction and Java language programming for integration of the systems. Structural optimization under these conditions allows computers to work in anautomated environment and the designer to speed up and improve the traditional design process. The specific requirements to be satisfied by the strategies are: Approach the target of imbalance of a V6 engine crankshaft, without affecting either its weight or itsmanufacturability.Develop interface programming that allows integration of the different software: CAD for modeling and geometric evaluations, CAE for simulation analysis and evaluation ,Genetic Algorithms for optimization and search for alternatives .Obtain new design concepts for the shape of the counterweights that help the designer to develop a better crankshaft in terms of functionality more rapidly than with the use of a “manual” approachShape optimization with genetic algorithmsGenetic Algorithms (GAs) are adaptive heuristic search algorithms (stochastic search techniques) based on the ideas of evolutionary natural selection and genetics [3]. Shape optimization based on genetic algorithm (GA), or based on evolutionary algorithms (EA) in general, is a relatively new area of research. The foundations of GAs can be found in a few articles published before 1990 [4]. After 1995 a large number of articles about investigation and applications have been published, including a great amount of GA-based geometrical boundary shape optimization cases. The interest towards research in evolutionary shape optimization techniques has just started to grow, including one of the most promising areas for EA-based shape optimization applications: mechanical engineering. There are applications for shape determination during design of machine components and for optimization of functional performance of these the components, e.g. antennas [5], turbine blades [6], etc. In the ield of mechanical engineering, methods for structural and topological optimization based on evolutionary algorithms are used to obtain optimal geometric solutions that were commonly approached only by costly and time consuming iterative process. Some examples are the computer design and optimization of cam shapes for diesel engines [7]. In this case the objective of the cam design was to minimize the vibrations of the system and to make smooth changes to a splined profile.In this article the shape optimization of a crankshaft is discussed, with focus on the geometrical development of the counterweights. The GAs are integrated with CAD and CAE systems that are currently used in Parametric and Structural Optimization to find optimal topologies and shapes of givenparts under certain conditions. Advanced CAD and CAE software have their own optimization capabilities, but are often limited to some local search algorithms, so it is decided to use genetic algorithms, such as those integrated in DAKOTA (Design Analysis Kit for Optimization Applications) [8] developed at Sandia Laboratories. DAKOTA is an optimization framework with the original goal ofproviding a common set of optimization algorithms for engineers who need to solve structural and design problems, including Genetic Algorithms. In order to make such integration, it is necessary to develop an interface to link the GAs to the CAD models and to the CAE analysis. This paper presents an approach to this task an also some approaches that can be used to build up a strategy on crankshaft design anddevelopment.Multi-objective considerations of crankshaft performanceThe crankshaft can be considered an element from where different objective functions can be derived to form an optimization problem. They represent functionalities and restrictions that are analyzed with software tools during the design process. These objective function are to be optimized (minimized or maximized) by variation of the geometry. The selected goal of the crankshaft design is to reach the imbalance target and reducing its weight and/or increasing its first eigenfrequency. The design of the crankshaft is inherently a multiobjective optimization (MO) problem. The imbalance is measured in both sides of the crankshaft so the problem is to optimize the components of a vector-valued objective function consisting of both imbalances [9]. Unlike the single-objective optimization, the solution to this problem is not a single point, but a family of points known as the Pareto-optimal set. Each point in this set is optimal in the sense that no improvement can be achieved in one objective component that does not lead to degradation in at least one of the remaining components [10].The objective functions of imbalance are also highly nonlinear. Auxiliaryinformation, like the derivatives of the objective function, is not available. The fitness-function is available only in the form of a computer model of the crankshaft, not in analytical form. Since in general our approach requires taking the objective function as a black box, and only the availability of the objective function value can be guaranteed, no further assumptions were considered. The Pareto-based optimization method, known as the Multiple Objective Genetic Algorithm (MOGA) [11], is used in the present MO problem, to finding the Pareto front among these two fitness functions.In GA’s, the natural parameter se t of the optimization problem is coded as afinite-length string. Traditionally, GA’s use binary numbers to represent such strings: a string has a finite length and each bit of a string can be either 0 or 1. By maintaining a population of solutions, GA’s c an search for many Pareto-optimal solutions in parallel. This characteristic makes GA’s very attractive for solving MO problems. The following two features are desired to solve MO problems successfully:1) the solutions obtained are Pareto-optimal and2) they are uniformly sampled from the Pareto-optimal set.NOMENCLATURECAD: Computer Aided Design; GAs: Genetic Algorithms; EA: Evolutionary Algorithms; MO: Multi-objective; MOGA: Multi-objective Genetic Algorithm; CW: Counterweight; FEM: Finite Element Method.OPTIMIZATION OF BALANCE WITH GEOMETRICALFig. 1: Imbalance graph from the original crankshaft DesignCrankshaft shape parameterizationIn order to make geometry modifications it is decided to substitute the current shape design of the crankshaft under analysis, from the original “arc-shaped” design representation of the counterweight’s profile, to a profile using spline curvesThe figure 2 shows a counterweight profile of the crankshaft.Fig. 2: Profile of a counterweight represented by a splineOptimization StrategiesThe general procedure of the strategy is described below. During the optimization loop the CAD software is automatically controlled by an optimization algorithm, i.e. by a Genetic Algorithms (GA). The y coordinates of the control points that define the splined profile of the crankshaft can be parametrically manipulated thanks to an interface programmed in JAVA. The splined profiles allow shapes to be changed by genetic algorithms because the codified control points of the splines play the role of genes. The Java interface allows the CAD software to run continually with the crankshaft model loaded in the computer memory, so that every time an individual is generated the geometry automatically adapts to the new set of parameters.Fig. 3: Profile Shapes of CW1, CW2, CW8 and CW9 from an individual in the Pareto FrontierA corresponding constraint to the optimization strategy is formulated next. An additional objective function was added: the measure of the curvature of all the splines from the profiles of counterweights. As it is known, the curvature is theinverse of the radius of an inscribed circle of the curve. In this case it was decided to integrate into the geometry the required inscribed circles and analysis features to extract the maximum curvature along the profiles of the four varyingFig. 4: Curvature in CW9 profile showing an improvedCurvatureIn the second part of this paper an additional evaluation is going to be introduced: the dynamic response of the crankshaft in order to control the first eigen frequency, with the aim of not affecting the weight. As in this first approach, the GA is going to be used to produce automatically alternative crankshaft shapes for the FEM simulator program, to run the simulator, and finally to e valuate the counterweight’s shapes on the basis of the FEM output data.SUMMARY AND CONCLUSIONSThe use of the Java interface allowed the integration of the genetic algorithm to the CAD software, in the first part of the paper, an optimization of the imbalance of a crankshaft was performed. It was possible the development of a Pareto frontier to find the closest-to-target individual. But the shapes of the counterweights were not so suitable for forging, for that reason it was necessary to introduce an additional objective function to improve the curvature of the counterweights profile. A further integration with the CAE software, as described in the second part, was performed. It was possible to improve some shapes of the crankshaft but with not so good imbalance results. The development of a new graph with the additional firsteigen-frequency objective was plotted, from which important conclusions were extracted: It is necessary to prevent the sharp edges of the counterweight’s shape byadding extra restrictions as curvature of shapes.Simulation of the forging process is required in order to define a relationship between good shapes-curvature and manufacturability. This becomes significantly important when a proposed design outside the initial shape restrictions needs to be justified in order not to affect forge ability.This paper defined the basis and the beginning of a strategy for developing crankshafts that will include the manufacturability and functionality to compile a whole Multiobjective System Optimization.ACKNOWLEDGMENTSThe authors acknowledge the support received from Tecnológico de Monterrey through Grant No. CAT043 to carry out the research reported in this paper.REFERENCES[1] Z.P. Mourelatos, “A crankshaft system model for structural dynamic analysis of internal combustion engines,” Computers & Structures, vol. 79, 2001, pp.2009-2027.[2] P. Bentley, Evolutionary Design by Computers, USA:Morgan Kaufmann, 1999.[3] D.E. Goldberg, Genetic Algorithms in Search ,Optimization and Machine Learning, USA: Addison-Wesley Longman Publishing Co., 1989.[4] C.A. Coello Coello, “A Comprehensive Survey of Evolutionary-Based Multi-objective Optimization Techniques,” Knowledge and Information Systems, vol.1, 1999, pp. 129-156.[5] B.E. Cohanim, J.N. Hew itt, and O. de Weck, “TheDesign of Radio Telescope Array Configurations using Multiobjective Optimization: Imaging Performance versus Cable Length,” astro-ph/0405183, 2004, pp. 1-42;[6] M. Olhofer, Yaochu Jin, and B. Sendh off, “Adaptiveen coding for aerodynamic shape optimization using evolution strategies,” Evolutionary Computation, Seoul: 2001, pp. 576-583.[7] J. Lampinen, “Cam shape optimization by genetical gorithm,” Computer-Aided Design, vol. 35, 2003, pp.727-737.[8] M. Eldred et al., DAKOTA, A Multilevel ParallelObject-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, andSensitivity Analysis. Reference Manual, USA: Sandia Laboratories, 2002.[9] Y. Kang et al., “An accuracy improvement for balanci ng crankshafts,” Mechanism andMachine Theory, vol. 38,2003, pp. 1449-1467.[10] S. Obayashi, T. Tsukahara, and T. Nakamura,“Multiobjective genetic algorithm applied toaerodynamic design of cascade airfoils,” Industrial Electronics, IEEE Transactions on, vol. 47, 2000, pp.211-216.[11] C.M. Fonseca and P.J. Fleming, “An Overview of Evolutionary Algorithms in Multiobjective Optimization,” Evolutionary Computation, vol. 3, 1995,pp. 1-16[12] - ., “Comparison of Strategies forthe Optimization/Innovation o f Crankshaft Balance,”T rends in Computer Aided Innovation, USA: Springer,2007, pp. 201-210.[13] S. Rao, M echanical vibrations, USA: Addison-Wesley,1990.[14] C.A. Coello Coello, A n empirical study of evolutionary techniques for multi-objective optimization in engineering design, USA: Tulane University, 1996.[15] N. Leon-Rovira et al., “Automatic Shape Variations in3d CAD Environments,” 1st IFIP-TC5 Working Conference on Computer Aided Innovation, Germany:2005, pp. 200-210.[16] R.E. Smith, B.A. Dike, and S.A. Stegmann, “Fitness inheritance in genetic algorithms,”A CM symposium on Applied computing, USA: ACM, 1995, pp. 345-350.IMECE2008学报2008年ASME国际机械工程国会和博览会2008年10月31-11月6日，波斯顿，马赛诸塞州，美国IMECE2008-67447适用于多目标系统优化发动机曲轴（阿尔伯特·阿尔伯斯/ IPEK产品开发研究所，德国卡尔斯鲁厄大学；诺埃尔利昂/ CIDyT创新中心和设计，墨西哥蒙特雷理工学院；温贝托Aguayo / CIDyT创新中心和设计，墨西哥蒙特雷理工学院；托马斯•迈尔/ IPEK产品开发研究所，德国卡尔斯鲁厄大学）随着计算机的功能不断增加，计算机辅助设计与工程(CAD和CAE)也不断加强。