Optimization of Chaboche Material Parameters with a Genetic Algorithm

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.