Development of a surrogate model for high-fidelity laser powder-bed fusion using tensor train and gaussian process regression

multi-physics high-fidelity computational model is required to study the melting and grain growth phenomena in a laser powder-bed fusion (LPBF) additive manufacturing process. The major challenge with the high-fidelity model is long computational time, which makes it unsuited for any feasible process parameter optimization study in a high dimensional process design space. To address this challenge, surrogate models are a good option to replace the high-fidelity model, resulting in a significant shortening of the computational time at the expense of an acceptable drop in accuracy. In this study, a tensor train (TT) and Gaussian process regression (GPR) based methodology is proposed to develop a surrogate of the high-fidelity powder-scale model. An in-house developed powder-scale model is used to generate the training data by simulating a microscale model of the powder-bed for different values of laser power. The trained TT-GPR model can predict the thermal history of the powder-bed and melt pool geometry for a specified value of laser power, while the computation time required for prediction of any set of process conditions is less than one second. Here we can achieve an approximate computational speedup of 10 ^4 with the surrogate model. We provide evidence to claim that the proposed surrogate model provides high computational efficiency without compromising accuracy.