Thermal field prediction for welding paths in multi-layer gas metal arc welding-based additive manufacturing: A machine learning approach

Abstract

Gas metal arc welding (GMAW)-based additive manufacturing (AM) is a key metal 3D printing technology for the fabrication of near-net shape parts. The thermal history of the multi-layer GMAW-based AM process has a significant influence on part quality and substrate deformations, but it is computationally expensive to accurately calculate it based on numerical method or analytical method. Existing data-driven approaches for the thermal field prediction of AM processes show great advantages in efficiency, but they are mainly applied to single-layer parts or the multi-layer parts with fixed geometry. The main contribution of this work is to realize the thermal field prediction of the multi-layer GMAW-based AM processes with arbitrary geometries by a machine learning approach. Firstly, a novel method for the discretization of deposition process was proposed to make the numerical simulation method for the thermal analysis of AM processes more adaptive and flexible. Then, a unique data structure was developed to extract the deposition state data from the results of the numerical simulation method, which generate the data for the training of the proposed machine learning model. Finally, a physics-based machine learning method based on an ensemble learning model was designed to identify the correlation between the deposition stage and its corresponding thermal field. Validation results showed that the prediction accuracy of the developed method exceeded 94 % when compared with the results of the numerical simulation method, while the time cost of a single prediction process was only at the millisecond level.