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

Author

Listed:
  • Kuanfang He

    (Hunan University of Science and Technology)

  • Xuejun Li

    (Hunan University of Science and Technology)

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.

Suggested Citation

  • Kuanfang He & Xuejun Li, 2016. "A quantitative estimation technique for welding quality using local mean decomposition and support vector machine," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 525-533, June.
    • Handle: RePEc:spr:joinma:v:27:y:2016:i:3:d:10.1007_s10845-014-0885-8
    DOI: 10.1007/s10845-014-0885-8
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    Cited by:

    1. Lei Zhou & Tianjian Li & Wenjia Zheng & Zhongdian Zhang & Zhenglong Lei & Laijun Wu & Shiliang Zhu & Wenming Wang, 2022. "Online monitoring of resistance spot welding electrode wear state based on dynamic resistance," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 91-101, January.
    2. Guiqian Liu & Xiangdong Gao & Deyong You & Nanfeng Zhang, 2019. "Prediction of high power laser welding status based on PCA and SVM classification of multiple sensors," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 821-832, February.
    3. Christian Kubik & Sebastian Michael Knauer & Peter Groche, 2022. "Smart sheet metal forming: importance of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine for predicting wear states during blanking," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 259-282, January.
    4. Liang Tian & Yu Luo, 2020. "A study on the prediction of inherent deformation in fillet-welded joint using support vector machine and genetic optimization algorithm," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 575-596, March.

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