Fast Prediction of Thermal Data Stream for Direct Laser Deposition Processes Using Network-based Tensor Regression

The objective of this research is to study an effective thermal history prediction method for additive manufacturing (AM) processes using thermal image streams in a layer-wise manner. The need for immaculate integration of in-process sensing and data-driven approaches to monitor process dynamics in AM has been clearly stated in blueprint reports released by various U.S. agencies such as NIST and DoD over the past five years. Reliable physics-based models have been developed to delineate the underlying thermo-mechanical dynamics of AM processes; however, the computational cost is extremely high. We propose a tensor-based surrogate modeling methodology to predict the layer-wise relationship in the thermal history of the AM parts, which is time-efficient compared to available physics-based prediction models. We construct a network-tensor structure for freeform shapes based on thermal image streams obtained in metal-based AM process. Subsequently, we simplify the network-tensor structure by concatenating images to reach layer-wise structure. Succeeding layers are predicted based on antecedent layer using the tensor regression model. Generalized multilinear structure, called the higher-order partial least squares (HOPLS) is used to estimate the tensor regression model parameters. Through proposed method, high-dimensional thermal history of AM components can be predicted accurately in a computationally efficient manner. The proposed thermal history prediction is applied on simulated thermal images from finite element method (FEM) simulations. This shows that the proposed model can be used to enhance their performance alongside simulation-based models.