A robust optimization of an automobile valvetrain is presented where the variation of engine performances due to the component dimensional variations is minimized subject to the constraints on mean engine performances. The dimensional variations of valvetrain components are statistically characterized based on the measurements of the actual components. Monte Carlo simulation is used on a neural network model built from an integrated high fidelity valvetrain-engine model, to obtain the mean and standard deviation of horsepower, torque and fuel consumption. Assuming the component production cost is inversely proportional to the coefficient of variation of its dimensions, a multi-objective optimization problem minimizing the variation in engine performances and the total production cost of components is solved by a multi-objective genetic algorithm (MOGA). The comparisons using the newly developed Pareto front quality index (PFQI) indicate that MOGA generates the Pareto fronts of substantially higher quality, than SQP with varying weights on the objectives. The current design of the valvetrain is compared with two alternative designs on the obtained Pareto front, which suggested potential improvements.
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ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
September 2–6, 2003
Chicago, Illinois, USA
Conference Sponsors:
- Design Engineering Division and Computers and Information in Engineering Division
ISBN:
0-7918-3700-9
PROCEEDINGS PAPER
Robust Optimization of an Automotive Valvetrain Using a Multiobjective Genetic Algorithm
Emre Kazancioglu,
Emre Kazancioglu
University of Michigan, Ann Arbor, MI
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Guangquan Wu,
Guangquan Wu
University of Michigan, Ann Arbor, MI
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Jeonghan Ko,
Jeonghan Ko
University of Michigan, Ann Arbor, MI
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Stanislav Bohac,
Stanislav Bohac
University of Michigan, Ann Arbor, MI
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Zoran Filipi,
Zoran Filipi
University of Michigan, Ann Arbor, MI
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S. Jack Hu,
S. Jack Hu
University of Michigan, Ann Arbor, MI
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Dennis Assanis,
Dennis Assanis
University of Michigan, Ann Arbor, MI
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Kazuhiro Saitou
Kazuhiro Saitou
University of Michigan, Ann Arbor, MI
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Emre Kazancioglu
University of Michigan, Ann Arbor, MI
Guangquan Wu
University of Michigan, Ann Arbor, MI
Jeonghan Ko
University of Michigan, Ann Arbor, MI
Stanislav Bohac
University of Michigan, Ann Arbor, MI
Zoran Filipi
University of Michigan, Ann Arbor, MI
S. Jack Hu
University of Michigan, Ann Arbor, MI
Dennis Assanis
University of Michigan, Ann Arbor, MI
Kazuhiro Saitou
University of Michigan, Ann Arbor, MI
Paper No:
DETC2003/DAC-48714, pp. 97-108; 12 pages
Published Online:
June 23, 2008
Citation
Kazancioglu, E, Wu, G, Ko, J, Bohac, S, Filipi, Z, Hu, SJ, Assanis, D, & Saitou, K. "Robust Optimization of an Automotive Valvetrain Using a Multiobjective Genetic Algorithm." Proceedings of the ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 2: 29th Design Automation Conference, Parts A and B. Chicago, Illinois, USA. September 2–6, 2003. pp. 97-108. ASME. https://doi.org/10.1115/DETC2003/DAC-48714
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