RT Journal Article
T1 Performance of Optimization Algorithms in the Model Fitting of the Multi-Scale Numerical Simulation of Ductile Iron Solidification
A1 Anglada, Eva
A1 Meléndez, Antton
A1 Obregón, Alejandro
A1 Villanueva, Ester
A1 Garmendia, Iñaki
AB 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.
PB Multidisciplinary Digital Publishing Institute (MDPI)
YR 2020
FD 2020
LK http://hdl.handle.net/11556/966
UL http://hdl.handle.net/11556/966
LA eng
NO Anglada, E.; Meléndez, A.; Obregón, A.; Villanueva, E.; Garmendia, I. Performance of Optimization Algorithms in the Model Fitting of the Multi-Scale Numerical Simulation of Ductile Iron Solidification. Metals 2020, 10, 1071.
NO This research was funded by the Basque Government under the ELKARTEK Program (ARGIA Project,ELKARTEK KK-2019/00068) and by the HAZITEK Program (CASTMART Project, HAZITEK ZL-2019/00562).
DS TECNALIA Publications
RD 1 jul 2024