CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 先进机器人
Automatic recognition system of welding seam type based on SVM method
Fan, Junfeng1,2; Jing, Fengshui1,2; Fang, Zaojun1; Tan, Min1,2
Source PublicationINTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
2017-09-01
Volume92Issue:1-4Pages:989-999
SubtypeArticle
AbstractIn this paper, an automatic recognition system of welding seam type based on support vector machine (SVM) method is presented. The hardware of the proposed system consists of an industry robot with six degrees of freedom, a vision sensor, and a computer. The system has two parts including input feature vector computation and model building. In the input feature vector computation part, the depth values of a series of points of the welding joint are taken as feature vector, which are determined by four steps including main line extraction of the laser stripe, normalization of the laser stripe, selection of the left and right edge points of the welding joint, and normalization of feature vectors. In the model building part, SVM-based modeling method is used to achieve welding seam type recognition. At first, RBF kernel function is employed for classification of welding seam types. Then, the parameters of RBF are determined by a grid search method using cross-validation. After the optimal parameters of RBF being determined, the SVM model is built, and it could be used to predict welding seam type. Finally, a series of welding seam type recognition experiments are implemented. Experimental results show that the proposed system can achieve welding seam type recognition accurately and the computation cost can be reduced compared with previous methods.
KeywordWelding Seam Type Recognition Structured-light Vision Svm Method Feature Extraction
WOS HeadingsScience & Technology ; Technology
DOI10.1007/s00170-017-0202-8
WOS KeywordGTAW PROCESS ; TRACKING ; SENSOR ; ACQUISITION ; INFORMATION
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61305024 ; Foundation for Innovative Research Groups of the National Natural Science Foundation of China(61421004) ; 61273337 ; 61573358)
WOS Research AreaAutomation & Control Systems ; Engineering
WOS SubjectAutomation & Control Systems ; Engineering, Manufacturing
WOS IDWOS:000407815500079
Citation statistics
Cited Times:9[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/19951
Collection复杂系统管理与控制国家重点实验室_先进机器人
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Fan, Junfeng,Jing, Fengshui,Fang, Zaojun,et al. Automatic recognition system of welding seam type based on SVM method[J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY,2017,92(1-4):989-999.
APA Fan, Junfeng,Jing, Fengshui,Fang, Zaojun,&Tan, Min.(2017).Automatic recognition system of welding seam type based on SVM method.INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY,92(1-4),989-999.
MLA Fan, Junfeng,et al."Automatic recognition system of welding seam type based on SVM method".INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 92.1-4(2017):989-999.
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