Differentiation of materials and laser powder bed fusion processing regimes from airborne acoustic emission combined with machine learning
Rita Drissi-Daoudi, Vigneashwara Pandiyan, Roland E. Logé, Sergey Shevchik, Giulio Masinelli, Hossein Ghasemi‐Tabasi, Annapaola Parrilli, Kilian Wasmer
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 (<em>lack of fusion pores</em>, <em>conduction mode</em> and <em>keyhole pores</em>) and three alloys (316L stainless steel, bronze (CuSn<sub>8</sub>), 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.