Performance of Optimization Algorithms in the Model Fitting of the Multi-Scale Numerical Simulation of Ductile Iron Solidification

Metals

Iñaki Garmendia¹  Alejandro Obregón²  Antton Meléndez²  Ester Villanueva²  Eva Anglada³ 

[1] Mechanical Engineering Department, Engineering School of Gipuzkoa, University of the Basque Country UPV/EHU, Plaza de Europa, 1, E-20018 Donostia-San Sebastián, Spain;TECNALIA, Basque Research and Technology Alliance (BRTA), Astondo Bidea, Edificio 700, E-48160 Derio, Spain;TECNALIA, Basque Research and Technology Alliance (BRTA), Mikeletegi Pasealekua, 2, E-20009 Donostia-San Sebastián, Spain;

关键词: model fitting;    optimization;    FEM;    metal casting;    SGI;    numerical simulation;   

DOI  :  10.3390/met10081071

来源: DOAJ

【 摘 要 】

The use of optimization algorithms to adjust the numerical models with experimental values has been applied in other fields, but the efforts done in metal casting sector are much more limited. The advances in this area may contribute to get metal casting adjusted models in less time improving the confidence in their predictions and contributing to reduce tests at laboratory scale. This work compares the performance of four algorithms (compass search, NEWUOA, genetic algorithm (GA) and particle swarm optimization (PSO)) in the adjustment of the metal casting simulation models. The case study used in the comparison is the multiscale simulation of the hypereutectic ductile iron (SGI) casting solidification. The model fitting criteria is the value of the tensile strength. Four different situations have been studied: model fitting based in 2, 3, 6 and 10 variables. Compass search and PSO have succeeded in reaching the error target in the four cases studied, while NEWUOA and GA have failed in some cases. In the case of the deterministic algorithms, compass search and NEWUOA, the use of a multiple random initial guess has been clearly beneficious.

【 授权许可】

Unknown