Increased relative density and characteristic melt pool Signals at the edge in PBF-LB/M

Abstract

Limited process control can cause metallurgical defect formation and inhomogeneous relative density in laser powder bed fusion manufactured parts. This study shows that process monitoring, based on optical melt-pool signal analysis is capable of tracing relative density variations: Unsupervised machine learning, applied to cluster multiple-slice monitoring data, reveals characteristic patterns in this noisy time-series signal, which can be co-registered with geometrical positions in the build part. For cylindrical 15–5 PH stainless steel specimens, manufactured under constant process parameters and post-analyzed by µ-computer tomography, correlations between such patterns and an increased local relative density at the edge have been observed. Finite element method (FEM) modeling of thermal histories at exemplary positions close to the edge suggest pre-heating effects caused by neighboring laser scan trajectories as possible reasons for the increased melt pool intensity at the edge. • Increased relative density at the edge of PBF-LB/M specimens observed in µ-CT scans. • Multi-layer data analysis reveals increased melt pool monitoring signal at the edge. • Unsupervised time series clustering identifies interpretable patterns in melt pool data. • FEM modeling of thermal histories suggests preheating as common cause in observed edge effects.