The Use of Genetic Algorithms to Calibrate Johnson–Cook Strength and Failure Parameters of AISI/SAE 1018 Steel

Abstract

Johnson–Cook (JC) strength and failure models have been widely used in finite element analysis (FEA) to solve a variety of thermo-mechanical problems. There are many techniques to determine the required JC parameters; however, a best practice to obtain the most reliable JC parameters has not yet been 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 were obtained from tensile tests performed for different specimen geometries at varying strain rates and temperatures. FEA 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 FEA results, the current strategy is suggested for calibration proposes in other kind of materials in which plastic behavior could be represented by the JC strength and failure models.