Detection method of spot welding based on multi-information fusion and fractal |
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Authors: | LIU Pengfei SHAN Ping LUO Zhen SHEN Junqi QIN Hede |
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Affiliation: | [1]Luoyang Ship Material Research Institute, Luoyang 471039, China [2]School of Material Science and Engineering, Tianjin University Tianjin 300072, China [3]School of Xinxiang Etectromechanical Engineering, Xinxiang 453000, China |
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Abstract: | A novel detection method of support vector machine (SVM) based on fractal dimension of signals is presented.And models of SVM are made based on nugget size defects of spot welding. Classification using these trained SVM models is done to signals of spot welding.It is shown from effect of different SVM models that these models with different inputs.In detection of defects,these models with inputs including sound signal have a high percentage of accuracy,the detection accuracy of these models with inputs including voltage signal will reduce.So the SVM models based on fractal dimensions of sound are some optimal nondestructive detection ones.At last a comparison between SVM detection model and ANNS detection model is researched which indicates that SVM is a more effective measure than Artificial neural networks in detection of nugget size defects during spot welding. |
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Keywords: | Multi-information fusion Support vector machine Box counting dimension Detection Spot welding |
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