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

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

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

35 Downloads (Pure)

Abstract

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.
Original languageEnglish
Title of host publicationMaterial Forming: The 26th International ESAFORM Conference on Material Forming - ESAFORM 2023 - held in Kraków, Poland, April 19-21, 2023
EditorsLukasz Madej, Mateusz Sitko, Konrad Perzynsk
PublisherMaterials Research Forum LLC
Pages1417-1426
ISBN (Electronic)978-1-64490-247-9
ISBN (Print)978-1-64490-246-2
DOIs
Publication statusPublished - 2023
MoE publication typeA4 Conference publication
EventInternational ESAFORM Conference on Material Forming - Kraków, Poland
Duration: 19 Apr 202321 Apr 2023
Conference number: 26

Publication series

NameMaterials Research Proceedings
Volume28
ISSN (Print)2474-3941
ISSN (Electronic)2474-395X

Conference

ConferenceInternational ESAFORM Conference on Material Forming
Abbreviated titleESAFORM
Country/TerritoryPoland
CityKraków
Period19/04/202321/04/2023

Keywords

  • Crystal Plasticity Model
  • Machine Learning
  • Parameter Calibration

Fingerprint

Dive into the research topics of 'Optimizing crystal plasticity model parameters via machine learning-based optimization algorithms'. Together they form a unique fingerprint.

Cite this