曲軸的加工工藝及夾具設(shè)計(jì)外文翻譯.doc

畢業(yè)設(shè)計(jì)外文翻譯題 目 曲軸的加工工藝及夾具設(shè)計(jì) 學(xué) 院 航海學(xué)院 專(zhuān) 業(yè) 輪機(jī)工程 學(xué) 生 佟寶誠(chéng) 學(xué) 號(hào) 10960123 指導(dǎo)教師 彭中波 重慶交通大學(xué) 2014年P(guān)roceedings 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 Development University 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 to our 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 an automated 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. Auxiliary information, 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 GAs, the natural parameter set of the optimization problem is coded as a finite-length string. Traditionally, GAs 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, GAs can search for many Pareto-optimal solutions in parallel. This characteristic makes GAs 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 parameterization In 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 counterweights 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 the inverse 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 evaluate the counterweights 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 first eigen-frequency objective was plotted, from which important conclusions were extracted: It is necessary to prevent the sharp edges of the counterweights shape by adding 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 Tecnolgico 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. Hewitt, 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 balancing crankshafts,” Mechanism and Machine 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 of Crankshaft Balance,”Trends in Computer Aided Innovation, USA: Springer,2007, pp. 201-210. 13 S. Rao, Mechanical vibrations, USA: Addison-Wesley, 1990. 14 C.A. Coello Coello, An 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 in 3d 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,” ACM symposium on Applied computing, USA: ACM, 1995, pp. 345-350.IMECE2008學(xué)報(bào)2008年ASME國(guó)際機(jī)械工程國(guó)會(huì)和博覽會(huì)2008年10月31-11月6日，波斯頓，馬賽諸塞州，美國(guó)IMECE2008-67447適用于多目標(biāo)系統(tǒng)優(yōu)化發(fā)動(dòng)機(jī)曲軸（阿爾伯特阿爾伯斯/ IPEK產(chǎn)品開(kāi)發(fā)研究所，德國(guó)卡爾斯魯厄大學(xué)；諾埃爾利昂/ CIDyT創(chuàng)新中心和設(shè)計(jì)，墨西哥蒙特雷理工學(xué)院；溫貝托Aguayo / CIDyT創(chuàng)新中心和設(shè)計(jì)，墨西哥蒙特雷理工學(xué)院；托馬斯邁爾/ IPEK產(chǎn)品開(kāi)發(fā)研究所，德國(guó)卡爾斯魯厄大學(xué)）摘 要隨著計(jì)算機(jī)的功能不斷增加，計(jì)算機(jī)輔助設(shè)計(jì)與工程(CAD和CAE)也不斷加強(qiáng)。目前CAD和CAE系統(tǒng)也用于設(shè)計(jì)，在一定條件下能夠選取最優(yōu)參數(shù)和結(jié)構(gòu)并且找到最佳的形狀。本文描述了一個(gè)總體戰(zhàn)略,優(yōu)化曲軸的平衡, 通過(guò)用Java編程結(jié)合CAD和CAE軟件計(jì)算出最優(yōu)的參數(shù)。要使用不同的工具設(shè)計(jì)不同的工藝。分析曲軸使用商業(yè)建模參數(shù)的三維CAD軟件。CAD適用于適應(yīng)度函數(shù)(平衡)和幾何修改。CAE適用于動(dòng)態(tài)限制(學(xué))。Java接口程序鏈接到CAE軟件的CAD模型進(jìn)行計(jì)算。我們的案例研究的是幾何修改,這是從原來(lái)的“弧形”設(shè)計(jì)用樣條函數(shù)替代砝碼的形象決定的。花鍵不平衡要文件的響應(yīng)通過(guò)控制點(diǎn)的變化來(lái)控制。首先是曲軸的平衡被定義為一個(gè)獨(dú)立的目標(biāo)函數(shù),其次是失衡的帕累托優(yōu)化兩點(diǎn)校正,并且限制物體的曲率的關(guān)鍵在于鍛造。自然頻率被認(rèn)為是另一個(gè)影響參數(shù)的方面。CAD的重復(fù)啟動(dòng)應(yīng)用程序等應(yīng)用是通過(guò)CAD程序完全自動(dòng)化過(guò)程的優(yōu)化和暫停等待接收等處理來(lái)設(shè)計(jì)出新設(shè)置的參數(shù)。前 言發(fā)動(dòng)機(jī)曲軸由于受到持續(xù)的發(fā)展演變市場(chǎng)的壓力。燃油經(jīng)濟(jì)性、耐用性和內(nèi)燃機(jī)的可靠性,呼吁減少大小、重量、振動(dòng)和噪音，成本等力量推動(dòng)著市場(chǎng)快速發(fā)展。因此競(jìng)爭(zhēng)必須從優(yōu)化引擎組件這個(gè)剛面著手。由于這種原因。曲軸這一大多數(shù)分析引擎組件必須得到改善1。這些改進(jìn)依賴(lài)于材料組成之一,隨著公司的發(fā)展,內(nèi)燃機(jī)鍛鋼曲軸實(shí)際表達(dá)了他們的意圖改變結(jié)節(jié)性鋼從發(fā)動(dòng)機(jī)曲軸。另一個(gè)重要改進(jìn)是其幾何特征的優(yōu)化方向。尤其是在鍛造上要求符合其固有頻率。分析工具可以大大提高對(duì)物理現(xiàn)象的理解與提到的相關(guān)特性, 工程師需要優(yōu)化設(shè)計(jì)編程任務(wù)可以自動(dòng)完成2。目前研究的目標(biāo)是:建立一個(gè)戰(zhàn)略發(fā)展的發(fā)動(dòng)機(jī)曲軸的集成工藝:CAD和CAE(計(jì)算機(jī)輔助設(shè)計(jì)與工程)軟件模按照型遺傳算法評(píng)價(jià)功能參數(shù)、使用樣條曲線的形狀結(jié)構(gòu)和Java語(yǔ)言編程的集成系統(tǒng)優(yōu)化方法。在這些條件