Transfer Learning Methods for Fractographic Detection of Fatigue Crack Initiation in Additive Manufacturing

Abstract

Recently, there has been an increased interest in additive manufacturing (AM) for its potential to reduce costs and lighten the weight of manufactured parts. However, materials produced through AM are prone to defects that can significantly impact their fatigue resistance. Identifying fatigue failure sources is crucial for the characterization of critical manufacturing defects, especially for the future use of AM in main load-bearing structural parts. This requires conducting fatigue tests and manually inspecting fracture surfaces. In this research, we introduce an innovative machine-learning model designed to detect the initiation defects causing fatigue cracks in Titanium Ti-6Al-4V samples manufactured by selective laser melting (SLM). The model also measures the distance between the detected fatigue failure source and the surface of the material. Our approach involves initially segmenting out areas without initiation points, and then identifying these points in the remaining areas. We then use established computer vision techniques to calculate their distance from the surface. The results of our study highlight the significant potential of using machine learning and computer vision to automate fractographic analysis. This advancement could greatly improve the speed and efficiency of this process, marking a new phase of productivity in the field. This research not only furthers artificial intelligence by introducing an innovative method but also may possess important applications in engineering.

Keywords

• Additive manufacturing
• computer vision
• fatigue failure
• fractography
• deep learning