A quantitative estimation technique for welding quality using local mean decomposition and support vector machine

Kuanfang He () and Xuejun Li
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Kuanfang He: Hunan University of Science and Technology
Xuejun Li: Hunan University of Science and Technology

Journal of Intelligent Manufacturing, 2016, vol. 27, issue 3, No 3, 525-533

Abstract: Abstract The experimental nonlinear time series of welding current contain the arc feature information related to welding quality. The local mean decomposition (LMD) combining with the support vector machine (SVM) is put forward to quantitatively estimate the rationality of welding parameters and welding formation quality. The LMD is used to investigate the time–frequency distribution of arc energy, and the energy entropy is employed to quantitatively judge the welding arc characteristics related to welding quality. The collected current signal is decomposed into a number of product functions (PFs) by LMD. The energy entropy of each PF is calculated to establish the welding arc energy feature vectors, which are inputted into support vector machine classifier. The LMD combining with SVM can quantitatively estimate the time–frequency energy distribution characteristics of the arc current signal at different welding parameters and welding formation quality. Experimental results are provided to confirm the effectiveness of this approach to estimate the rationality of welding parameters and welding formation quality.

Keywords: Welding quality; Welding current; LMD; Energy entropy; SVM; Quantitative estimation (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s10845-014-0885-8

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