Machine learning-based prediction of pore characteristics in laser powder bed fusion-fabricated 316L stainless steel

Abstract

Precise prediction and control of porosity in laser powder bed fusion (L-PBF) directly enhances the performance of additively manufactured components. This study addresses the need for comprehensive machine learning (ML) analysis of pore characteristics through comparative evaluation of multiple ML models based on accuracy and reliability. Five supervised ML models-linear regression (LR), Gaussian process regression, decision tree regression, artificial neural networks (ANNs), and random forest regression (RFR)-were utilized to predict pore characteristics in 316L stainless steel components fabricated via L-PBF. Model performance was systematically assessed using three key metrics: root mean square error, mean absolute error, and coefficient of determination ( R 2 ). These metrics were calculated from process-condition averages under grouped cross-validation to ensure robust evaluation. Material characterization of specimens produced using pore-optimized printing parameters further validated the predictive accuracy of the models. Our results revealed that distinct models were optimal for different pore-related targets: LR outperformed others for porosity prediction, RFR excelled in estimating average pore diameter, and ANN delivered the highest accuracy for average pore roundness. Response surfaces generated from the optimal models delineated a processing window (laser power: 150–250 W; scan speed: 800–1200 mm/s; layer thickness: 0.04 mm; and hatch spacing: 0.09 mm) associated with minimized porosity and improved pore morphology. Notably, a significant inverse correlation was observed between predicted porosity and critical mechanical properties (including yield strength, tensile strength, and elongation at break), which further corroborated the practical utility of the proposed predictive workflow.