Abstract

In this paper, a data-driven approach for constructing elastoplastic constitutive law of microstructured materials is proposed by combining the insights from plasticity theory and the tools of artificial intelligence (i.e., constructing yielding function through ANN) to reduce the required amount of data for machine learning. Illustrative examples show that the constitutive laws constructed by the present approach can be used to solve the boundary value problems (BVPs) involving elastoplastic materials with microstructures under complex loading paths (e.g., cyclic/reverse loading) effectively. The limitation of the proposed approach is also discussed.

Issue Section:
Research Papers
Keywords:
data-driven, constitutive law, microstructured elastoplastic materials, yielding function, artificial neural network (ANN)
Topics:
Artificial neural networks, Constitutive equations, Particulate matter, Plasticity, Stress, Cantilever beams

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