Data science for finite strain mechanical science of ductile materials

Abstract

A mechanical science of materials, based on data science, is formulated to predict process–structure–property–performance relationships. Sampling techniques are used to build a training database, which is then compressed using unsupervised learning methods, and finally used to generate predictions by means of supervised learning methods or mechanistic equations. The method presented in this paper relies on an a priori deterministic sampling of the solution space, a K-means clustering method, and a mechanistic Lippmann–Schwinger equation solved using a self-consistent scheme. This method is formulated in a finite strain setting in order to model the large plastic strains that develop during metal forming processes. An efficient implementation of an inclusion fragmentation model is introduced in order to model this micromechanism in a clustered discretization. With the addition of a fatigue strength prediction method also based on data science, process–structure–property–performance relationships can be predicted in the case of cold-drawn NiTi tubes.