A quantitative estimation technique for welding quality using local mean decomposition and support vector machine
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DOI: 10.1007/s10845-014-0885-8
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Cited by:
- 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.
- 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.
- 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.
- 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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Keywords
Welding quality; Welding current; LMD; Energy entropy; SVM; Quantitative estimation;All these keywords.
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