Artificial intelligence (AI) assisted fatigue fracture recognition based
on morphing and Fully Convolutional Networks
Abstract
Fatigue fracture is one of the most common metallic component failure
cases in manufacturing industries. The observation on fractography can
provide the direct evidence for failure analysis. In this study, an
image segmentation method based on Fully Convolutional Networks (FCNs)
was proposed to figure out the boundary between fatigue crack
propagation and fast fracture regions from optical microscope (OM)
fractography images. Furthermore, novel morphing-based data augmentation
method was adopted to enable few-shot learning of sample images. The
proposed framework can successfully segment two categories, namely the
crack propagation and fast fracture regions, thus differentiating the
boundary of two regions in one image. The artificial intelligence (AI)
assisted fatigue analysis architecture can complete the failure analysis
procedure in 0.5 second and prove the feasibility of fatigue failure
analysis. The segmentation accuracy of developed network achieves 95.4%
for the fatigue crack propagation region, as well as 97.2% for the fast
fracture region, which possesses comparable accuracy to the segmentation
results using Mask R-CNN Regional Convolutional Neural Network (Mask
R-CNN), one state-of-the-art deep learning networks.