Optimization of Chaboche Material Parameters with a Genetic Algorithm

Author:

Dvoršek Nejc1,Stopeinig Iztok2,Klančnik Simon1ORCID

Affiliation:

1. Faculty of Mechanical Engineering, University of Maribor, Smetanova 17, 2000 Maribor, Slovenia

2. AVL-AST d.o.o., Ulica Kneza Koclja 22, 2000 Maribor, Slovenia

Abstract

The main objective of this study is to research and develop a genetic algorithm (GA) for optimizing Chaboche material model parameters within an industrial environment. The optimization is based on 12 experiments (tensile, low-cycle fatigue, and creep) that are performed on the material, and corresponding finite element models were created using Abaqus. Comparing experimental and simulation data is the objective function that the GA is minimizing. The GA’s fitness function makes use of a similarity measure algorithm to compare the results. Chromosome genes are represented with real-valued numbers within defined limits. The performance of the developed GA was evaluated using different population sizes, mutation probabilities, and crossover operators. The results show that the population size had the most significant impact on the performance of the GA. With a population size of 150, a mutation probability of 0.1, and two-point crossover, the GA was able to find a suitable global minimum. Comparing it to the classic trial and error approach, the GA improves the fitness score by 40%. It can deliver better results in a shorter time and offer a high degree of automation not present in the trial and error approach. Additionally, the algorithm is implemented in Python to minimize the overall cost and ensure its upgradability in the future.

Funder

Slovenian Research Agency

Publisher

MDPI AG

Subject

General Materials Science

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Research on Spatial Optimization Algorithm in Intelligent Aging Environment Design;2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT);2024-03-15

2. Computational Methods of the Identification of Chaboche Isotropic-Kinematic Hardening Model Parameters Derived from the Cyclic Loading Tests;Advances in Science and Technology Research Journal;2024-02-01

3. A Genetic Optimization Method for Spatial Layout of Cameras in Video Sensor Networks;2023 11th International Conference on Agro-Geoinformatics (Agro-Geoinformatics);2023-07-25

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