An integrated approach for detecting and classifying pores and surface topology for fatigue assessment 316L manufactured by powder bed fusion of metals using a laser beam using μCT and machine learning algorithms

This research aims to detect and analyze critical internal and surface defects in metal components manufactured by powder bed fusion of metals using a laser beam (PBF-LB/M). The aim is to assess their impact on the fatigue behavior. Therefore, a combination of methods, including image processing of micro-computed tomography ( $\mu$CT) scans, fatigue testing, and machine learning, was applied. A workflow was established to contribute to the nondestructive assessment of component quality and mechanical properties. Additionally, this study illustrates the application of machine learning to address a classification problem, specifically the categorization of pores into gas pores and lack of fusion pores. Although it was shown that internal defects exhibited a reduced impact on fatigue behavior compared with surface defects, it was noted that surface defects exert a higher influence on fatigue behavior. A machine learning algorithm was developed to predict the fatigue life using surface defect features as input parameters.