An integrated fuzzy logic and machine learning platform for porosity detection using optical tomography imaging during laser powder bed fusion

Abstract

Traditional methods such as mechanical testing and x-ray computed tomography (CT), for quality assessment in laser powder-bed fusion (LPBF), a class of additive manufacturing (AM), are resource-intensive and conducted post-production. Recent advancements in in-situ monitoring, particularly using optical tomography (OT) to detect near-infrared light emissions during the process, offer an opportunity for in-situ defect detection. However, interpreting OT datasets remains challenging due to inherent process characteristics and disturbances that may obscure defect identification. This paper introduces a novel machine learning-based approach that integrates a self-organizing map, a fuzzy logic scheme, and a tailored U-Net architecture to enhance defect prediction capabilities during the LPBF process. This model not only predicts common flaws such as lack of fusion and keyhole defects through analysis of in-situ OT data, but also allows quality assurance professionals to apply their expert knowledge through customizable fuzzy rules. This capability facilitates a more nuanced and interpretable model, enhancing the likelihood of accurate defect detection. The efficacy of this system has been validated through experimental analyses across various process parameters, with results validated by subsequent CT scans, exhibiting strong performance with average model scores ranging from 0.375 to 0.819 for lack of fusion defects and from 0.391 to 0.616 for intentional keyhole defects. These findings underscore the model’s reliability and adaptability in predicting defects, highlighting its potential as a transformative tool for in-process quality assurance in AM. A notable benefit of this method is its adaptability, allowing the end-user to adjust the probability threshold for defect detection based on desired quality requirements and custom fuzzy rules.