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Multi-Objective Crashworthiness Optimization of Composite Hat-Shape Energy Absorber Using GMDH-Type Neural Networks and Genetic Algorithms

[+] Author Affiliations
Naser Tavassoli, Bahram Notghi

Islamic Azad University Parand Branch, Parand, Iran

Abolfazl Darvizeh

Islamic Azad University Bandar Anzali Branch, Bandar Anzali, Iran

Mansour Darvizeh

The University of Guilan, Rasht, Iran

Paper No. IMECE2010-38693, pp. 191-196; 6 pages
doi:10.1115/IMECE2010-38693
From:
  • ASME 2010 International Mechanical Engineering Congress and Exposition
  • Volume 3: Design and Manufacturing, Parts A and B
  • Vancouver, British Columbia, Canada, November 12–18, 2010
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-4427-4
  • Copyright © 2010 by ASME

abstract

Reducing the weight of car body and increasing the crashworthiness capability of car body are two important objectives of car design. In this paper, a multi-objective optimization for optimal composite hat-shape energy absorption system is presented At the first, the behaviors of the hat shape under impact, as simplified model of side member of a vehicle body, are studied by the finite element method using commercial software ABAQUS. Two meta-models based on the evolved group method of data handling (GMDH) type neural networks are then achieved for modeling of both the absorbed energy (E) and the Tsai-Hill Failure Criterion (TS) with respect to geometrical design variables using those training and testing data obtained models. The obtained polynomial neural meta-models are finally used in a multi-objective optimum design procedure using NSGA-II with a new diversity preserving mechanism for Pareto based optimization of hat-shape. Two conflicting objectives such as maximizing the energy absorption capability (E), minimizing the Tsai-Hill Failure Criterion are considered in this work.

Copyright © 2010 by ASME

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