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Multi-Objective System Optimization of Engine Crankshafts Using an Integration Approach

[+] Author Affiliations
Albert Albers, Thomas Maier

University of Karlsruhe, Karlsruhe, Germany

Noel Leon, Humberto Aguayo

Monterrey Institute of Technology, Mexico

Paper No. IMECE2008-67447, pp. 101-109; 9 pages
  • ASME 2008 International Mechanical Engineering Congress and Exposition
  • Volume 14: New Developments in Simulation Methods and Software for Engineering Applications
  • Boston, Massachusetts, USA, October 31–November 6, 2008
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-4875-3 | eISBN: 978-0-7918-3840-2
  • Copyright © 2008 by ASME


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 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 eigen-frequencies). 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 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 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.

Copyright © 2008 by ASME
Topics: Engines , Optimization



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