Frydrych, K.; Tomczak, M.; Papanikolaou, S. Crystal Plasticity Parameter Optimization in Cyclically Deformed Electrodeposited Copper—A Machine Learning Approach. Materials2024, 17, 3397.
Frydrych, K.; Tomczak, M.; Papanikolaou, S. Crystal Plasticity Parameter Optimization in Cyclically Deformed Electrodeposited Copper—A Machine Learning Approach. Materials 2024, 17, 3397.
Frydrych, K.; Tomczak, M.; Papanikolaou, S. Crystal Plasticity Parameter Optimization in Cyclically Deformed Electrodeposited Copper—A Machine Learning Approach. Materials2024, 17, 3397.
Frydrych, K.; Tomczak, M.; Papanikolaou, S. Crystal Plasticity Parameter Optimization in Cyclically Deformed Electrodeposited Copper—A Machine Learning Approach. Materials 2024, 17, 3397.
Abstract
The paper describes an application of machine learning approach for parameter optimization. The method is demonstrated for the elasto-viscoplastic model with both isotropic and kinematic hardening. It is shown that the proposed method based on long-short term memory networks enabled to obtain reasonable agreement of stress-strain curves for cyclic deformation in low-cycle fatigue regime. The main advantage of the proposed approach over traditional optimization schemes lies in the possibility to get parameters for a new material without the necessity to conduct any further optimizations. As the power and robustness of the developed method was demonstrated on the very challenging problem (cyclic deformation, crystal plasticity, self-consistent model, isotropic and kinematic hardening), it is directly applicable to other experiments and models.
Keywords
crystal plasticity; optimization; machine learning; long short term memory networks; self-consistent modelling; Eshelby solution; cyclic deformation; low cycle fatigue
Subject
Engineering, Mechanical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
