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The Use of genetic algorithm and neural network to predict rate-dependent tensile flow behaviour of AA5182-O sheets

  • Published:2016-03 Issue: Volume:94 Page:262-273
  • ISSN:0264-1275
  • Container-title:Materials & Design
  • language:en
  • Short-container-title:Materials & Design

Author:

Jenab ArashORCID,Sari Sarraf Iman,Green Daniel E.,Rahmaan Taamjeed,Worswick Michael J.

Funder

Automotive Partnership Canada programme

Amino North America Corp.

ArcelorMittal Dofasco

Ford Motor Company

CanmetMATERIALS-Natural Resources Canada

Novelis Inc.

Publisher

Elsevier BV

Subject

Mechanical Engineering,Mechanics of Materials,General Materials Science

Reference51 articles.

1. Reducing vehicle weight and improving U.S. energy efficiency using integrated computational materials engineering;Joost;JOM,2012

2. Tensile ductility of several commercial aluminum alloys at elevated temperatures;Taleff;Metall. Mater. Trans. A Phys. Metall. Mater. Sci.,2001

3. Recent development in aluminium alloys for the automotive industry;Miller;Mater. Sci. Eng. A,2000

4. Solute-drag creep and tensile ductility in aluminum alloys, in: creep behavior of advanced materials for the 21st century;Taleff,1999

5. Experimental investigation of the hot deformation behavior of AA7075: development and comparison of flow localization parameter and dynamic material model processing maps;Jenab;Int. J. Mech. Sci.,2014

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