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.

Fatigue behavior of Al-Al and Al-steel refill friction stir spot welding joints Haokun Yang1a*, Detao Cai2,3a, Jiaxin Li1, Chin Yung Kwok1, Yunqiang Zhao3, Yu Li4, Haisheng Liu4, Waiwah Lai11 Smart Manufacturing Division (SMD), Hong Kong Productivity Council (HKPC), Hong Kong 999077, People’s Republic of China2 State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, Hunan 410082, People’s Republic of China3 China-Ukraine Belt and Road Joint Laboratory on Materials Joining and Advanced Manufacturing, China-Ukraine Institute of Welding Guangdong Academy of Sciences, Guangzhou, Guangdong 510650, People’s Republic of China4 Center for Industrial Analysis and Testing, Guangdong Academy of Science, Guangzhou, Guangdong 510650, People’s Republic of Chinaa These authors contribute equallyCorresponding author: Haokun Yang, +852 27885679,hkyang@hkpc.orgKeywords: Refill friction stir spot welding, Fatigue fracture, Aluminum, Steel

A comparison study of fatigue behavior in Al-Al and Al-steel refill friction stir spot welding jointHaokun Yang1a*, Detao Cai2,3a, Jiaxin Li1, Chin Yung Kwok1, Yunqiang Zhao3, Yu Li4, Haisheng Liu4, Waiwah Lai11 Smart Manufacturing Division (SMD), Hong Kong Productivity Council (HKPC), Hong Kong 999077, People’s Republic of China2 State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, Hunan 410082, People’s Republic of China3 China-Ukraine Belt and Road Joint Laboratory on Materials Joining and Advanced Manufacturing, China-Ukraine Institute of Welding Guangdong Academy of Sciences, Guangzhou, Guangdong 510650, People’s Republic of China4 Center for Industrial Analysis and Testing, Guangdong Academy of Science, Guangzhou, Guangdong 510650, People’s Republic of Chinaa These authors contribute equallyCorresponding author: Haokun Yang, +852 27885679,hkyang@hkpc.orgKeywords: Refill friction stir spot welding, Fatigue fracture, Aluminum, Steel