Prediction of spatter-related defects in metal laser powder bed fusion by analytical and machine learning modelling applied to off-axis long-exposure monitoring

Abstract

Laser powder bed fusion of metals is increasingly used for fabricating complex parts requiring good mechanical properties. Simultaneously, researchers in the field are intensifying the efforts to reduce defects, such as internal porosities, which hinder a wider industrial adoption of this technology, urging process monitoring to a pivotal role in defect identification and mitigation. Therefore, understanding the correlation between in-process monitoring signals and post-process actual defects is fundamental to taking informed decisions and potential corrective actions during the process. This work focuses on developing models to predict spatter-related defects from specific process signatures detected through off-axis long-exposure imaging. Layer-wise images were properly aligned with corresponding cross-sections from tomographic reconstructions to investigate the relationship between spatter-related signatures and actual defects measured by X-ray computed tomography. This relationship was used as a knowledge basis to develop an analytical image-processing approach and a machine learning-based methodology, which were then compared in terms of their correlation performances. The advantages and limitations of both methods are discussed in the paper. Both approaches led to promising results in the prediction of lack-of-fusion defects caused by spatters, with the machine learning approach showing a prediction accuracy in the order of 90 % for defects with equivalent diameter above 90 µm, while the analytical model needed equivalent diameters larger than 130 µm to reach a prediction accuracy in the order of 80 %. Furthermore, the machine learning method led to strong results regarding early defect detection, with most of the investigated defects properly predicted by analysing two consecutive layers after the signature detection.