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research-article

The use of Genetic Algorithms to calibrate Johnson-Cook strength and failure parameters of AISI/SAE 1018 steel

[+] Author and Article Information
Mario Buchely

Department of Materials Science and Engineering. Missouri University of Science and Technology, 1400 N. Bishop, McNutt Hall, Rolla, MO, 65409, USA
buchelym@mst.edu

Xin Wang

Department of Mechanical and Aerospace Engineering. Missouri University of Science and Technology, 400 West 13th Street, Toomey Hall, Rolla, MO, 65409, USA
xw95c@mst.edu

David Van Aken

Department of Materials Science and Engineering. Missouri University of Science and Technology, 1400 N. Bishop, McNutt Hall, Rolla, MO, 65409, USA
dcva@mst.edu

Ronald O'Malley

Department of Materials Science and Engineering. Missouri University of Science and Technology, 1400 N. Bishop, McNutt Hall, Rolla, MO, 65409, USA
omalleyr@mst.edu

Simon Lekakh

Department of Materials Science and Engineering. Missouri University of Science and Technology, 1400 N. Bishop, McNutt Hall, Rolla, MO, 65409, USA
lekakhs@mst.edu

K Chandrashekhara

Department of Mechanical and Aerospace Engineering. Missouri University of Science and Technology, 400 West 13th Street, Toomey Hall, Rolla, MO, 65409, USA
chandra@mst.edu

1Corresponding author.

ASME doi:10.1115/1.4042382 History: Received September 20, 2018; Revised November 16, 2018

Abstract

Johnson-Cook (JC) strength and failure models have been widely used in Finite Element Analysis (FEA) to solve wide range of mechanical design problems. There are many techniques to determine the required JC parameters; however, a best practice to obtain the most reliable JC parameters has yet to be proposed. In this paper, a Genetic-Algorithm-based optimization strategy is proposed to calibrate the JC strength and failure model parameters of AISI/SAE 1018 steel. Experimental data was obtained from tensile tests performed for different specimen geometries at varying strain-rates and temperatures. Finite element analysis was performed for each tensile test. A genetic algorithm was used to determine the optimum JC parameters that best fit the experimental force-displacement data. Calibrated JC parameters were implemented in FEA to simulate the impact tests of standard V-notch Charpy bars to verify the damage mechanism in the material. Considering good agreement of the experimental and FE results, the current strategy is suggested for calibration proposes in other kind of materials which plastic behavior could be represented by the JC strength and failure models.

Copyright (c) 2018 by ASME
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