Benchmarking a multi-layer approach and neural network architectures for defect detection in PBF-LB/M

Abstract

The substitution of expensive non-destructive material testing by data-based process monitoring is intensively explored in quality assurance for additive manufactured components. Machine learning show promising results for defect detection but require conceptual adaption to layer wise manufacturing and line scanning patterns in laser powder bed fusion. A multi-layer approach to co-register µ-computer tomography measurements with process monitoring data is developed and a workflow for automatic data set generation is implemented. The objective of this research is to benchmark the volumetric multi-layer approach and specifically selected deep learning methods for defect detection. The volumetric approach shows superior results compared to single slice monitoring. All investigated structured neural network topologies deliver similar performance. • Machine learning identifies correlations between µ-CT porosity scans and melt pool monitoring • 3D-multi-layer-based machine learning outperforms single layer approach in defect detection • Saturation of detection performance is observed beyond optimal number of layers • Results hold independently for all investigated neural network topologies