Partner: M Tomczak


Recent publications
1.Frydrych K., Tomczak M., Papanikolaou S., Crystal Plasticity Parameter Optimization in Cyclically Deformed Electrodeposited Copper—A Machine Learning Approach, Materials, ISSN: 1996-1944, DOI: 10.3390/ma17143397, Vol.17, No.14, pp.3397-1-3397-14, 2024
Abstract:

This paper describes an application of a 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 allowed a reasonable agreement of stress–strain curves to be obtained for cyclic deformation in a low-cycle fatigue regime. The main advantage of the proposed approach over traditional optimization schemes lies in the possibility of obtaining parameters for a new material without the necessity of conducting any further optimizations. As the power and robustness of the developed method was demonstrated for very challenging problems (cyclic deformation, crystal plasticity, self-consistent model and 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 modeling, Eshelby solution, cyclic deformation, low cycle fatigue

Affiliations:
Frydrych K.-IPPT PAN
Tomczak M.-other affiliation
Papanikolaou S.-other affiliation