Prediction of part density in additively manufactured maraging steel with supervised machine learning using pyrometer data

Abstract

Additive Manufacturing (AM) / 3D printing technology is a game-changing technology for developing new improved solutions of product innovation with smart manufacturing advancements. One of the major challenges of AM manufactured metallic parts using laser powder bed fusion (LPBF) is the quality prediction of the printed samples under varying process parameters. This paper focuses on predicting the part density from pyrometer-based data using machine learning (ML) models, including Linear Regression (LR), Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Regression (SVR), Extreme Gradient Boosting (XGB), and Artificial Neural Network (ANN). Different pre-processing methods like Butterworth filter and thresholding have been compared with the raw pyrometer data-based analysis. Time-domain-based statistical features including mean, standard deviation, root mean square, entropy etc. have been used as inputs to the ML models. The six ML models were trained with and without feature selection (FS) to predict the part density. Among the regression algorithms used in this study, the best performance metrics R2 of 0.85 and 0.86 were obtained by RF regression using raw and filtered data respectively, while thresholding reduced model performance. Analysis reveals that the combined effect of laser power and scanning speed most influences the quality of printed parts. A subsequent experiment with the new process parameters chosen based on the data analysis was able to print parts with improved quality, thereby confirming the validity of our ML framework.