Material parameters identification: Gradient-based, genetic and hybrid optimization algorithms

TLDR: In this paper, a hybrid algorithm was proposed to improve the performance of the gradient-based algorithm, which is strongly dependent on the initial set of results, in order to analyse the effectiveness of this optimization procedure, numerical and experimental results for an EN AW-5754 aluminium alloy are compared.

About: This article is published in Computational Materials Science.The article was published on 2008-12-01 and is currently open access. It has received 192 citations till now. The article focuses on the topics: Meta-optimization & Hybrid algorithm.

Figures

Fig. 5. Evolution of the cost function with iterations for the identifications performed with the derivative-based method (Sets 1, 2 and 3) and the identification performed with the hybrid identification scheme (DD3MAT + SiDoLo).

Fig. 6. Experimental and numerical results of the variation in plane of the initial values of the uniaxial yield stress obtained with the derivative-based and the hybrid algorithm.

Fig. 9. Experimental and numerical results for the cyclic test obtained with the derivative-based and the hybrid algorithm in the rolling direction.

Fig. 10. Experimental and numerical results obtain for width plastic strain versus thickness plastic strain in tension, in the rolling direction. The numerical results presented were obtained for sets 1 and 4.

Table 1 Diagram of the genetic algorithm used

Table 2 Experimental data obtained for the EN AW-5754-O aluminium alloy