Differentiation of materials and laser powder bed fusion processing regimes from airborne acoustic emission combined with machine learning

Abstract

This study investigates the use of a low cost microphone combined with state-of-the-art machine learning (ML) algorithms as online process monitoring to differentiate various materials and process regimes of Laser-Powder Bed Fusion (LPBF). Three processing regimes (lack of fusion pores, conduction mode and keyhole pores) and three alloys (316L stainless steel, bronze (CuSn8), and Inconel 718) were selected. Three conventional ML algorithms and a Convolutional Neural Network (CNN) were chosen to perform the classification tasks resulting in five main findings. First, we proved that the AE features are related to the laser-material interaction and not from undesired machine or environmental noise. Second, the process regimes are classified with high accuracy (> 87%) regardless of the algorithms and materials. Third, it is possible to build a single model from the three materials and still reach high classification accuracy (>86%) of the different regimes. Forth, the AE features used for the classifications are material and regime dependent. Finally, with LPBF processing of multi-materials on the rise, a strategy for classifying the material and the process regimes simultaneously using a CNN multi-label architecture reached a very high classification accuracy (≈ 93%). The results demonstrate the potential of our approaches for online LPBF process monitoring of different materials and regimes.

Keywords

• acoustic emission
• additive manufacturing
• in situ monitoring
• machine learning
• artificial intelligence
• laser material processing