|About this Abstract
|7th World Congress on Integrated Computational Materials Engineering (ICME 2023)
|A Machine Learning-based Virtual Lab to Predict Yield Surfaces from Crystal Plasticity Simulation
|Anderson Nascimento, Sharan Roongta, Martin Diehl, Irene J. Beyerlein
|On-Site Speaker (Planned)
At the continuum level, the plastic anisotropy of a wide range of metals and alloys is well described by advanced phenomenological yield surfaces. Relevant difficulties in their usage, however, are associated with the non-trivial parameter identification process and the non-uniqueness of the anisotropy coefficients. Alternative avenues for plastic flow prediction have been studied, and machine learning based approaches have gained notoriety due to their high fitting capabilities. We present a Machine Learning Virtual Lab for yield surface prediction, a framework that integrates crystal plasticity with deep neural network models and provides performance comparable to 3D yield functions. Important features such as well-defined flow vector, convexity, and multi-axial yield prediction are analyzed and compared against benchmark yield criteria.