0
Research Papers

Automated Vehicle Structural Crashworthiness Design via a Crash Mode Matching Algorithm

[+] Author and Article Information
Karim Hamza

Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109-2102khamza@umich.edu

Kazuhiro Saitou

Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109-2102kazu@umich.edu

J. Mech. Des 133(1), 011003 (Dec 29, 2010) (9 pages) doi:10.1115/1.4003037 History: Received March 08, 2010; Revised November 03, 2010; Published December 29, 2010; Online December 29, 2010

This paper presents an automated algorithm for the design of vehicle structures for crashworthiness based on the analyses of the structural crash mode (CM). The CM is the history of the deformation of the different zones of the vehicle structure during a crash event. The algorithm emulates a manual design process called crash mode matching where crashworthiness is improved by manually modifying the design until its CM matches what the designers deem as optimal. Given an initial design and a desired crash mode, the proposed algorithm iteratively finds new designs that have better crashworthiness performance via stochastic sampling of the design space. In every iteration of the algorithm, a number of sample designs are generated through a normal distribution on neighboring regions of the search space to the current design, and the best among the samples is chosen as the new design. The mean and the standard deviation of the normal distributions are adjusted in each iteration by examining the crash mode of the current design and by applying a set of fuzzy logic rules that encapsulate elementary knowledge of the CM matching practice. Two case studies, examining a front half vehicle as well as fully detailed vehicle models, are presented to demonstrate the effectiveness of the proposed algorithm.

Copyright © 2011 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Figure 8

CM∗, CM(x0), and Δ(x0)

Grahic Jump Location
Figure 9

CM∗, CM(x), and Δ(x) of the best design found via the proposed algorithm

Grahic Jump Location
Figure 10

Summary of case study results. Two horizontal dashed lines indicate the maximum allowable intrusion (100 mm) and maximum cabin acceleration (30 g).

Grahic Jump Location
Figure 11

A detailed FE model of a vehicle subjected to frontal crash against an offset deformable barrier

Grahic Jump Location
Figure 12

Baseline, DOE samples, MSCGA results, and clustering analysis

Grahic Jump Location
Figure 7

FE model of a vehicle subjected to frontal crash

Grahic Jump Location
Figure 6

Membership functions for the fuzzy adjustment rules

Grahic Jump Location
Figure 5

Example of fuzzy design adjustment rule

Grahic Jump Location
Figure 4

Overview of the automated CM matching algorithm

Grahic Jump Location
Figure 3

Examples of crash mode metric δij(x): (a) well matched deformation, (b) too little deformation, and (c) too much deformation

Grahic Jump Location
Figure 2

Example of crash mode cmij(t) obtained by FE crash simulation and its approximation as a step function

Grahic Jump Location
Figure 1

(a) Substructure of a vehicle subjected to crash conditions, (b) desirable crash mode of the substructure, and (c) undesirable crash mode of the substructure

Grahic Jump Location
Figure 13

CM∗, CM(x), and Δ(x) of algorithm starting points

Grahic Jump Location
Figure 14

Results of algorithm runs

Grahic Jump Location
Figure 15

CM∗, CM(x), and Δ(x) of the results of the algorithm runs

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In