Truncated Newton Kernel Ridge Regression for Prediction of Porosity in Additive Manufactured SS316L

Abstract

Despite the many benefits of additive manufacturing, the final quality of the fabricated parts remains a barrier to the wide adoption of this technique in industry. Predicting the quality of parts using advanced machine learning techniques may improve the repeatability of results and make additive manufacturing accessible to different fields. This study aims to integrate data extracted from various sources and use them to obtain accurate predictions of relative density with respect to the governing process parameters. Process parameters such as laser power, scan speed, hatch distance, and layer thickness are used to predict the relative density of 316L stainless steel specimens fabricated by selective laser melting. An extensive dataset is created by systematically combining experimental results from prior studies with the results of the current work. Analysis of the collected dataset shows that the laser power and scan speed significantly impact the relative density. This study compares ridge regression, kernel ridge regression, and support vector regression using the data collected for SS316L. Computational results indicate that kernel ridge regression performs better than both ridge regression and support vector regression based on the coefficient of determination and mean square error.