The Use of Machine-Learning Techniques in Material Constitutive Modelling for Metal Forming Processes

Accurate numerical simulations require constitutive models capable of providing precise material data. Several calibration methodologies have been developed to improve the accuracy of constitutive models. Nevertheless, a model's performance is always constrained by its mathematical formulation. Machine learning (ML) techniques, such as artificial neural networks (ANNs), have the potential to overcome these limitations. Nevertheless, the use of ML for material constitutive modelling is very recent and not fully explored. Difficulties related to data requirements and training are still open problems. This work explores and discusses the use of ML techniques regarding the accuracy of material constitutive models in metal plasticity, particularly contributing (i) a parameter identification inverse methodology, (ii) a constitutive model corrector, (iii) a data-driven constitutive model using empirical known concepts and (iv) a general implicit constitutive model using a data-driven learning approach. These approaches are discussed, and examples are given in the framework of non-linear elastoplasticity. To conveniently train these ML approaches, a large amount of data concerning material behaviour must be used. Therefore, non-homogeneous strain field and complex strain path tests measured with digital image correlation (DIC) techniques must be used for that purpose.

Automated summary

This study explores using machine learning techniques to improve the accuracy of material constitutive models in metal plasticity, including parameter identification inverse methodology, constitutive model corrector, data-driven constitutive model, and general implicit constitutive model. Training these methods requires a large amount of data on material behavior, necessitating the use of non-homogeneous strain field and complex strain path tests measured with digital image correlation techniques.