Comparison of Machine Learning Models and Analytical Scaling Law for Predicting Melt-Pool Depth in Laser Powder Bed Fusion (LPBF) Additive Manufacturing

Abstract

Abstract Laser powder bed fusion (LPBF) is a widely used Additive Manufacturing (AM) process involving a high-powered laser to fuse metal powder and build up successive layers to create complex structures. Process parameter selection significantly impacts the dimensional characteristics of the melt pool morphology, including its depth and width, which is vital to the final print quality. Accurately predicting the melt-pool depth is critical for ensuring the quality of the final product. Various methods for different materials have been investigated, for example, thermal image analysis, analytical methods like scaling law, machine learning, and so on. However, most of the promising results are only studies for one material, and it is not necessarily fully applied to another material. In this paper, our study involves utilizing machine learning models to make predictions of melt-pool depth in the LPBF process and compare their performance to an analytical approach based on scaling law with Nickel alloys IN625 and IN718. We trained three different machine learning models which are Gaussian Process Regression (GPR), Support Vector Machines (SVM), and Neural Networks (NN), and evaluated their performance using R-squared error and Root Mean Squared Error (RMSE) against the performance of the analytical scaling law. Our results show that the machine learning models outperform the scaling law approach significantly. This study demonstrates the potential of machine learning for improving the accuracy of melt-pool depth prediction in LPBF and provides insights into the strengths and limitations of different machine learning models for this task.