曲轴的加工工艺及夹具设计外文翻译样本.docx

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1、曲轴的加工工艺及夹具设计外文翻译样本 毕业设计外文翻译 重庆交通大学 Proceedings of IMECE ASME International Mechanical Engineering Congress and Exposition October 31-November 6, , Boston, Massachusetts, USA IMECE -67447 MULTI-OBJECTIVE SYSTEM OPTIMIZATION OF ENGINE CRANKSHAFTS USING AN INTEGRATION APPROACH Albert Albers/IPEK Instit

2、ute of Product Development University of Karlsruhe Germany Noel Leon/CIDyT Center for Innovation andDesign Monterrey Institute of Technology,Mexico Humberto Aguayo/CIDyT Center forInnovation and Design, Monterrey Institute ofTechnology, Mexico Thomas Maier/IPEK Institute of Product Development Unive

3、rsity of Karlsruhe Germany ABSTRACT The 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

4、 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 imple

5、mentation. 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. CAEis used for evaluating dynamic restrictions (the eigenfrequencies). A Java interface is programmed to link the

6、 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 result

7、s in an imbalance response. 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 f

8、low during forging. The natural frequency was considered as an additional objective function during a second approach. The optimization process runs fully automated and the CADprogram is on hold waiting for new set of parameters to receive and process, saving computing time, which is otherwise lost

9、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

10、, 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

11、 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

12、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

13、 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 functional parameters, Genetic Algorithms as the optimization method, the use of splines

14、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

15、by the strategies are: Approach the target of imbalance of a V6 engine crankshaft, without affecting either its weight or its manufacturability. Develop interface programming that allows integration of the different software: CAD for modeling and geometric evaluations, CAE for simulation analysis an

16、d 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” approach Shape optimization with

17、 genetic algorithms Genetic 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 rela

18、tively 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

19、 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 optimiz

20、ation 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 on

21、ly 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

22、 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 given parts under certain cond

23、itions. 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.

24、 DAKOTA is an optimization framework with the original goal of providing 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

25、 the CADmodels and to the CAEanalysis. This paper presents an approach to this task an also someapproaches that can be used to build up a strategy on crankshaft design anddevelopment. Multi-objective considerations of crankshaft performance The crankshaft can be considered an element from where diff

26、erent 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

27、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