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Abstract

Constitutive modelling of anisotropic plastic material behavior traditionally follows a deductive scheme, relying on empirical observations that are cast into analytic equations, the so-called phenomenological yield functions. Recently, data-driven constitutive modeling has emerged as an alternative to phenomenological models as it offers a more general way to describe the material behavior with no or fewer assumptions. In data-driven constitutive modeling, methods of statistical learning are applied to infer the yield function directly from a data set generated by experiments or numerical simulations. We present a new generic descriptor for crystallographic texture that allows an explicit consideration of the microstructure in data-driven constitutive modeling. We prove its ability to capture the structure-property relationship between a variety of textures and their anisotropic plastic behavior described with the yield function Yld2004-18p by applying methods of supervised machine learning. In the context of data-driven constitutive modeling, the descriptor enables consideration of microstructure evolution.