Plastic anisotropy of AA7075-T6 alloy under quasi-static compression: experiments, classical plasticity and artificial neural networks modeling

This paper presents the experimental observations, theoretical analysis and machine learning model of plastic anisotropy of rolling AA7075-T6. Compression responses have been discussed by obtaining the instantaneous stress-strain relationship. The analytical solution of anisotropic initial yielding and hardening has been derived through generalizing the J2 flow theory and applying the evolutive constitutive parameters. In addition, a machine learning model consisting of artificial neural network optimized by genetic algorithm (GA-ANN) is utilized to simulate the plastic anisotropy of AA7075-T6. According to the comparisons among experimental, theoretical and predicted (GA-ANN) results, the machine learning model provides flexible application and is found easy to be generalized for solving such mechanical problems, but with difficultly in assessment of the model's reliability. Multi-index estimation is a feasible approach to ensure the objective of evaluation in machine learning model.

Automated summary

This paper presents the experimental observations, theoretical analysis, and machine learning model of plastic anisotropy in rolling AA7075-T6. Compression responses were discussed by obtaining the instantaneous stress-strain relationship. The analytical solution of anisotropic initial yielding and hardening was derived by generalizing the J2 flow theory and applying evolutive constitutive parameters. Additionally, a machine learning model consisting of an artificial neural network optimized by a genetic algorithm (GA-ANN) was utilized to simulate the plastic anisotropy of AA7075-T6. According to the comparisons among experimental, theoretical, and predicted (GA-ANN) results, the machine learning model provides flexible application and is found to be easily generalized for solving such mechanical problems, but assessing the model's reliability is challenging. Multi-index estimation is a feasible approach to ensure the objective evaluation of machine learning models.