IMR OpenIR
Artificial intelligence-assisted fatigue fracture recognition based on morphing and fully convolutional networks
Lyu, Yetao1; Yang, Zi1; Liang, Hao2; Zhang, Beini3; Ge, Ming4; Liu, Rui5; Zhang, Zhefeng5; Yang, Haokun2
Corresponding AuthorLyu, Yetao(aaronlyu@hkpc.org) ; Yang, Haokun(hkyang@hkpc.org)
2022-04-06
Source PublicationFATIGUE & FRACTURE OF ENGINEERING MATERIALS & STRUCTURES
ISSN8756-758X
Pages13
AbstractFatigue 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.
Keywordartificial intelligence failure analysis fatigue fracture fully convolutional network mask R-CNN morphing-based data augmentation
Funding OrganizationCRD Program of Hong Kong Productivity Council ; Shenzhen Institute of Artificial Intelligence and Robotics for Society ; National Natural Science Foundation of China (NSFC)
DOI10.1111/ffe.13693
Indexed BySCI
Language英语
Funding ProjectCRD 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 Research AreaEngineering ; Materials Science
WOS SubjectEngineering, Mechanical ; Materials Science, Multidisciplinary
WOS IDWOS:000781012900001
PublisherWILEY
Citation statistics
Cited Times:7[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.imr.ac.cn/handle/321006/172810
Collection中国科学院金属研究所
Corresponding AuthorLyu, Yetao; Yang, Haokun
Affiliation1.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
Recommended Citation
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.
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