Analytical Target Cascading in Automotive Vehicle Design-Reference-Cited by-同舟云学术

Analytical Target Cascading in Automotive Vehicle Design

Published:2003-09-01 Issue:3 Volume:125 Page:481-489
ISSN:1050-0472
Container-title:Journal of Mechanical Design
language:en
Short-container-title:

Author:

Kim Hyung Min¹,Rideout D. Geoff¹,Papalambros Panos Y.¹,Stein Jeffrey L.¹

Affiliation:

1. Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109

Abstract

Target cascading in product development is a systematic effort to propagate the desired top-level system design targets to appropriate specifications for subsystems and components in a consistent and efficient manner. If analysis models are available to represent the consequences of the relevant design decisions, analytical target cascading can be formalized as a hierarchical multilevel optimization problem. The article demonstrates this complex modeling and solution process in the chassis design of a sport-utility vehicle. Ride quality and handling targets are cascaded down to systems and subsystems utilizing suspension, tire, and spring analysis models. Potential incompatibilities among targets and constraints throughout the entire system can be uncovered and the trade-offs involved in achieving system targets under different design scenarios can be quantified.

Publisher

ASME International

Subject

Computer Graphics and Computer-Aided Design,Computer Science Applications,Mechanical Engineering,Mechanics of Materials

Link

http://asmedigitalcollection.asme.org/mechanicaldesign/article-pdf/125/3/481/5799906/481_1.pdf

Reference17 articles.

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2. Kim, H. M., 2001, “Target Cascading in Optimal System Design,” Doctoral Dissertation, Department of Mechanical Engineering, University of Michigan, Ann Arbor.

3. Sobieski, J., James, B., and Riley, M., 1987, “Structural Sizing by Generalized, Multilevel Optimization,” AIAA J., 25(1), pp. 139–145.

4. Cramer, E., Dennis, J., Frank, P., Lewis, R., and Shubin, G., 1994, “Problem Formulation for Multidisciplinary Optimization,” SIAM J. Control Optim., 4(4), pp. 754–776.

5. Braun, R., 1996, “Collaborative Optimization: An Architecture For Large-Scale Distributed Design,” Doctoral Dissertation, Stanford University, Stanford.

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