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Weld penetration identification with deep learning method based on auditory spectrum images of arc sounds

文献类型：期刊论文

作者Gao, Yanfeng1,5; Wang, Qisheng2,3; Xiao, Jianhua4; Xiong, Genliang1,5; Zhang, Hua1,5
刊名WELDING IN THE WORLD
出版日期2022-09-08
ISSN号0043-2288
关键词Weld penetration Deep learning method Arc sounds Auditory spectrum Convolution neural network
DOI10.1007/s40194-022-01373-7
通讯作者Gao, Yanfeng(gyf_2672@163.com) ; Wang, Qisheng(wangqisheng@mail.ustc.edu.cn)
英文摘要Penetration states significantly affect the service performance of weld products. For improving welding quality, it is essential to real-timely monitor the penetration states of molten pool during welding process. This study adopts arc sound signals to identify penetration states of weld seam. Firstly, the time-frequency spectrum images of arc sounds are obtained with short-time Fourier transform. And based on a convolution neural network, the penetration states of weld seam are identified. For improving the anti-interference ability of the proposed identification method, a mathematical model that simulates the functions of human auditory system is developed. The auditory spectrum images of arc sounds are acquired with this model. Based on the auditory spectrum images of arc sounds, the penetration states are identified. The experimental results show that the proposed method has high anti-interference ability. When the signal-to-noise ratio is less than 5 dB, the accuracy rate of identification keeps more than 95%.
WOS关键词RECOGNITION
资助项目Natural Science Foundation of Shanghai[21ZR1425900] ; Natural Science Foundation of Shanghai[21010501600]
WOS研究方向Metallurgy & Metallurgical Engineering
语种英语
出版者SPRINGER HEIDELBERG
WOS记录号WOS:000852269600001
资助机构Natural Science Foundation of Shanghai
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/128970]  
专题中国科学院合肥物质科学研究院
通讯作者Gao, Yanfeng; Wang, Qisheng
作者单位1.Shanghai Univ Engn Sci, Sch Mech & Automot Engn, Shanghai 201620, Peoples R China
2.Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, Huihong Bldg,Changwu Middle Rd 801, Changzhou 213164, Jiangsu, Peoples R China
3.Univ Sci & Technol China, Dept Sci Isl, Hefei 230026, Anhui, Peoples R China
4.Shanghai Univ Engn Sci, Sch Chem & Chem Engn, Shanghai 201620, Peoples R China
5.Shanghai Collaborat Innovat Ctr Intelligent Mfg R, Shanghai 201620, Peoples R China
推荐引用方式
GB/T 7714		 Gao, Yanfeng,Wang, Qisheng,Xiao, Jianhua,et al. Weld penetration identification with deep learning method based on auditory spectrum images of arc sounds[J]. WELDING IN THE WORLD,2022.
APA Gao, Yanfeng,Wang, Qisheng,Xiao, Jianhua,Xiong, Genliang,&Zhang, Hua.(2022).Weld penetration identification with deep learning method based on auditory spectrum images of arc sounds.WELDING IN THE WORLD.
MLA Gao, Yanfeng,et al."Weld penetration identification with deep learning method based on auditory spectrum images of arc sounds".WELDING IN THE WORLD (2022).

入库方式： OAI收割

来源：合肥物质科学研究院

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