Comparative evaluation of supervised machine learning algorithms in the prediction of the relative density of 316L stainless steel fabricated by selective laser melting

Abstract

To find a robust combination of selective laser melting (SLM) process parameters to achieve the highest relative density of 3D printed parts, predicting the relative density of 316L stainless steel 3D printed parts was studied using a set of machine learning algorithms. The SLM process brings about the possibility to process metal powders and built complex geometries. However, this technology’s applicability is limited due to the inherent anisotropy of the layered manufacturing process, which generates porosity between adjacent layers, accelerating wear of the built parts when in service. To reduce interlayer porosity, the selection of SLM process parameters has to be properly optimized. The relative density of these manufactured objects is affected by porosity and is a function of process parameters, rendering it a challenging optimization task to solve. In this work, seven supervised machine learning regressors (i.e., support vector machine, decision tree, random forest, gradient boosting, Gaussian process, K-nearest neighbors, multi-layer perceptron) were trained to predict the relative density of 316L stainless steel samples produced by the SLM process. For this purpose, a total of 112 data sets were assembled from a deep literature review, and 5-fold cross-validation was applied to assess the regressor error. The accuracy of the predictions was evaluated by defining an index of merit, i.e., the norm of a vector whose components are the statistical metrics: root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2). From this index of merit, it is established that the use of gradient boosting regressor shows the highest accuracy, followed by multi-layer perceptron and random forest regressor.