Artificial intelligence-assisted fatigue fracture recognition based on morphing and fully convolutional networks

doi:10.1111/ffe.13693

IMR OpenIR

	Artificial intelligence-assisted fatigue fracture recognition based on morphing and fully convolutional networks
	Lyu, Yetao 1; Yang, Zi 1; Liang, Hao 2; Zhang, Beini 3; Ge, Ming 4; Liu, Rui 5; Zhang, Zhefeng 5; Yang, Haokun 2
通讯作者	Lyu, Yetao(aaronlyu@hkpc.org) ; Yang, Haokun(hkyang@hkpc.org)
	2022-04-06
发表期刊	FATIGUE & FRACTURE OF ENGINEERING MATERIALS & STRUCTURES
ISSN	8756-758X
页码	13
摘要	Fatigue fracture is one of the most common metallic component failure cases in manufacturing industries. The observation on fractography can provide direct evidence for failure analysis. In this study, an image semantic 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, a 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. This artificial intelligence (AI)-assisted fatigue analysis architecture can complete the failure analysis procedure in 0.5 s and prove the feasibility of fatigue failure analysis. The segmentation accuracy of self-developed network achieves 95.4% for the fatigue crack propagation region, as well as 97.2% for the fast fracture region. Not only for semantic segmentation DNN, we also prove that our novel data augmentation method can applied at the instance segmentation DNN, such as mask regional convolutional neural network (mask R-CNN), one state-of-the-art deep learning network for instance segmentation, to achieve similar accuracy.
关键词	artificial intelligence failure analysis fatigue fracture fully convolutional network mask R-CNN morphing-based data augmentation
资助者	CRD Program of Hong Kong Productivity Council ; Shenzhen Institute of Artificial Intelligence and Robotics for Society ; National Natural Science Foundation of China (NSFC)
DOI	10.1111/ffe.13693
收录类别	SCI
语种	英语
资助项目	CRD Program of Hong Kong Productivity Council[10008787] ; CRD Program of Hong Kong Productivity Council[10009455] ; Shenzhen Institute of Artificial Intelligence and Robotics for Society[AC01202005025] ; National Natural Science Foundation of China (NSFC)[51901230]
WOS研究方向	Engineering ; Materials Science
WOS类目	Engineering, Mechanical ; Materials Science, Multidisciplinary
WOS记录号	WOS:000781012900001
出版者	WILEY
引用统计	被引频次：7[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/172810
专题	中国科学院金属研究所
通讯作者	Lyu, Yetao; Yang, Haokun
作者单位	1.Hong Kong Prod Council HKPC, Robot & Artificial Intelligence Div, Hong Kong, Peoples R China 2.Hong Kong Prod Council HKPC, Smart Mfg Div, Hong Kong, Peoples R China 3.Hong Kong Univ Sci & Technol HKUST, Dept Phys, Hong Kong, Peoples R China 4.Hong Kong Ind Artificial Intelligence & Robot Ctr, Hong Kong, Peoples R China 5.Chinese Acad Sci, Shi Changxu Innovat Ctr Adv Mat, Inst Met Res, Shenyang, Peoples R China
推荐引用方式 GB/T 7714	Lyu, Yetao,Yang, Zi,Liang, Hao,et al. Artificial intelligence-assisted fatigue fracture recognition based on morphing and fully convolutional networks[J]. FATIGUE & FRACTURE OF ENGINEERING MATERIALS & STRUCTURES,2022:13.
APA	Lyu, Yetao.,Yang, Zi.,Liang, Hao.,Zhang, Beini.,Ge, Ming.,...&Yang, Haokun.(2022).Artificial intelligence-assisted fatigue fracture recognition based on morphing and fully convolutional networks.FATIGUE & FRACTURE OF ENGINEERING MATERIALS & STRUCTURES,13.
MLA	Lyu, Yetao,et al."Artificial intelligence-assisted fatigue fracture recognition based on morphing and fully convolutional networks".FATIGUE & FRACTURE OF ENGINEERING MATERIALS & STRUCTURES (2022):13.