Deep learning solvers for predicting alloy microstructure in additive manufacturing

  • Biros, George (The University of Texas at Austin)

Please login to view abstract download link

Predicting grain formation during alloy solidification is of great importance in additive manufacturing (AM). Numerical simulations require fine spatial and temporal discretizations that can be computationally expensive. In this talk, I will discuss GrainNN, an efficient and accurate reduced-order model for epitaxial grain growth in additive manufacturing conditions. GrainNN is a sequence-to-sequence long-short-term-memory (LSTM) deep neural network that evolves the dynamics of manually crafted features. We present results in which GrainNN can be orders of magnitude faster than phase field simulations, while delivering 5%–15% pointwise error. This speedup includes the cost of the phase field simulations for generating training data. GrainNN enables predictive simulations and uncertainty quantification of grain microstructure for situations not previously possible.