Data-driven Prediction of Temperature Evolution in Metallic Additive Manufacturing Process

Abstract

In this study, a data-driven deep learning model for fast and accurate prediction of temperature evolution and melting pool size of metallic additive manufacturing processes are developed. The study focuses on bulk experiments of the M4 high-speed steel material powder manufactured by Direct Energy Deposition. Under non-optimized process parameters, many deposited layers (above 30) generate large changes of microstructure through the sample depth caused by the high sensitivity of the cladding material on the thermal history. A 2D finite element analysis (FEA) of the bulk sample, validated in a previous study by experimental measurements, is able to achieve numerical data defining the temperature field evolution under different process settings. A Feed-forward neural networks (FFNN) approach is trained to reproduce the temperature fields generated from FEA. Hence, the trained FFNN is used to predict the history of the temperature fields for new process parameter sets not included in the initial dataset. Besides the input energy, nodal coordinates, and time, five additional features relating layer number, laser location, and distance from the laser to sampling point are considered to enhance prediction accuracy. The results indicate that the temperature evolution is predicted well by the FFNN with an accuracy of 99% within 12 seconds.