A novel machine learning-based approach for in-situ surface roughness prediction in laser powder-bed fusion

Abstract

Controlling and optimizing surface roughness remain a significant challenge in laser powder bed fusion (LPBF). Surface roughness affects printed part quality, particularly fatigue life, leading to costly post-processing. Nevertheless, tailored roughness can be beneficial to specific fields in engineering and medicine, such as cooling or osteointegration. Consequently, the importance of quality assurance, in-situ monitoring, and automatic anomaly detection has been elevated among the users of the LPBF process. In-situ surface roughness detection remains a less explored area within all in-situ defect detection. Additionally, existing studies primarily employ camera-based methods, which come with inherent limitations, including sensitivity to ambient light conditions, compromises between resolution and field of view, and the requirement for additional equipment such as adaptive optical filters. This study pioneers using a photodiode sensor, offering faster response times for real-time surface roughness prediction. Integrating this sensor with machine learning (ML) algorithms establishes a robust framework for surface roughness prediction. The methodology involves analyzing the captured light intensity from the melt pool by an on-axial photodiode and incorporating additional process variables into ML models to predict surface roughness within each small area of the printed part (690 µm × 510 µm), even at edges and corners. Multiple ML algorithms are trained and rigorously validated with unseen data. This comprehensive analysis encompasses the impact of process parameter variations on a wide range of surface roughness values, affirming the efficiency of the proposed methodology.