Classification of defects in additively manufactured nickel alloys using supervised machine learning

The presence of undesirable microstructural features in additively manufactured components, such as cracks, pores and lack of fusion defects presents a challenge for engineers, particularly if these components are applied in structure-critical applications. Such features might need to be manually classified, counted and their size distributions measured during metallographic evaluation, which is a time-consuming task. In this study, the performance of two supervised machine learning methods (kth-nearest neighbours and decision trees) to automatically classify typical defects found during metallographic examination of additively manufactured nickel alloys is briefly outlined and discussed.