Particle swarm optimization procedure in determining parameters in Chaboche kinematic hardening model to assess ratcheting under uniaxial and biaxial loading cycles

Abstract

As parameters in Chaboche model are difficult to be determined from experimental data, a single objective particle swarm optimization procedure was employed to obtain them. Hysteresis loop and uniaxial and biaxial ratcheting simulations were conducted to validate the determined models. Chaboche models determined by particle swarm optimization give more accurate simulation of ratcheting compared with the model determined by trial and error method. Chaboche models containing different backstress components were studied. Models determined considering uniaxial ratcheting can only predict uniaxial ratcheting precisely, while giving very bad simulation of biaxial ratcheting. The linear hardening rule in the N3L1 model clearly decreases the rate of the accumulation of ratcheting strain, and the N3L1 model gives the best simulations. For biaxial ratcheting, the fourth backstress component can decrease the rate of the accumulation apparently, while it has a little influence on prediction of uniaxial ratcheting.

A Comprehensive Numerical Approach for Analyzing the Residual Stresses in AISI 301LN Stainless Steel Induced by Shot Peening

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Parameter Identification of Chaboche Model for Aluminum Alloy Sheets Based on Differential Evolution Algorithm

“…Inverse FE method has been used to this purpose [7] but involving an expensive computational cost. Recently, several advanced computational methods have been applied successfully to identify parameters of Chaboche model, for example, fuzzy logic analysis [8], genetic algorithm [9], particle swarm optimization [10]. These studies demonstrate the potential of advanced computational methods in identifying Chaboche model's parameters.…”

Parameter identification of elastoplastic model for CuCrZr alloy by the neural network‐aided Bayesian inference

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“…The traditional deterministic approach treats parameter identification as an engineering optimization problem 1 . And heuristic algorithms, such as the genetic algorithm and particle swarm optimization, are most commonly used to minimize the errors between the model responses and the experimental observations and to obtain a set of optimal solutions 2,3 . However, the deterministic approach ignores inevitable model and experimental uncertainties.…”