Optimizing crystal plasticity model parameters via machine learning-based optimization algorithms

Rongfei Juan, Binh Nguyen Xuan, Wenqi Liu, Junhe Lian

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

40 Lataukset (Pure)

Abstrakti

The field of materials science and engineering is constantly evolving, and new methods are being developed to improve our understanding of the relationship between microstructure and properties. One such method is crystal plasticity (CP) modeling, which is widely used for predicting the mechanical properties of crystals and phases. However, determining the constitutive parameters for CP models has been a significant challenge, with current methods relying on either direct chemical composition or inverse fitting, both of which can be time-consuming and lack accuracy. In this study, we propose an automated, advanced, and more efficient method for determining constitutive parameters by using a genetic algorithm (GA) optimization method coupled with machine learning. Our proposed method is applied to two widely used CP models, and the reference data for the calibration is the stress-strain curve from tensile tests. The results of the automated calibration process are then compared to numerical simulation results of CP models with known parameters, demonstrating the efficiency and accuracy of our proposed method.
AlkuperäiskieliEnglanti
OtsikkoMaterial Forming: The 26th International ESAFORM Conference on Material Forming - ESAFORM 2023 - held in Kraków, Poland, April 19-21, 2023
ToimittajatLukasz Madej, Mateusz Sitko, Konrad Perzynsk
KustantajaMaterials Research Forum LLC
Sivut1417-1426
ISBN (elektroninen)978-1-64490-247-9
ISBN (painettu)978-1-64490-246-2
DOI - pysyväislinkit
TilaJulkaistu - 2023
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaInternational ESAFORM Conference on Material Forming - Kraków, Puola
Kesto: 19 huhtik. 202321 huhtik. 2023
Konferenssinumero: 26

Julkaisusarja

NimiMaterials Research Proceedings
Vuosikerta28
ISSN (painettu)2474-3941
ISSN (elektroninen)2474-395X

Conference

ConferenceInternational ESAFORM Conference on Material Forming
LyhennettäESAFORM
Maa/AluePuola
KaupunkiKraków
Ajanjakso19/04/202321/04/2023

Sormenjälki

Sukella tutkimusaiheisiin 'Optimizing crystal plasticity model parameters via machine learning-based optimization algorithms'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

Siteeraa tätä