# Crystal Plasticity Parameter Optimization in Cyclically Deformed Electrodeposited Copper—A Machine Learning Approach

## TL;DR

This paper uses machine learning to optimize parameters for modeling the behavior of electrodeposited copper under cyclic deformation.

## Contribution

A machine learning approach is introduced for parameter optimization in elasto-viscoplastic models without requiring further optimization for new materials.

## Key findings

- The method achieved reasonable agreement in stress–strain curves for cyclic deformation in a low-cycle fatigue regime.
- The approach is robust and applicable to challenging problems like crystal plasticity and self-consistent models.
- Parameters for new materials can be obtained without additional optimization.

## 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.