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

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AbstractThis 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 ( 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.