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

Abstract

In recent times, there has been a growing surge of interest in additive manufacturing (AM) due to its ability to bring about cost savings and weight reduction in fabricated components. Nevertheless, AM materials are susceptible to imperfections that can severely undermine their ability to withstand fatigue. Consequently, before incorporating these materials into vital structural components, it is imperative to possess a thorough comprehension of their fatigue properties. This necessitates the execution of fatigue tests and manual examination of fracture surfaces. Within this research, we put forward a novel approach involving the development of a machine-learning model tailored to identify the starting point of fatigue cracks within specimens of Titanium Ti-6Al-4V, which were produced using selective laser melting (SLM). Moreover, this model also quantifies the distance of these initiation sites from the material's surface. The proposed method encompasses the segmentation of image sections that are devoid of initiation sites, succeeded by the detection of these sites within the remaining segments. Subsequently, established computer vision techniques are harnessed to compute the distance from the surface. The outcomes of this study underscore a remarkable potential for automating the process of fractographic analysis through the integration of machine learning and computer vision models. These strides in technology hold the promise of streamlining and expediting the fractographic analysis procedure, ushering in a new era of efficiency.