Abstract
To target the accurate and fast joint quality identification, this work presents a method based on the particle swarm optimization (PSO) and the kernel extreme learning machine (KELM) in resistance spot welding (RSW). We perform welding and tensile tests to determine the related information and extract features from signals combined with the welding mechanism. Afterward, we optimize the parameters in the KELM with the PSO and fivefold cross validation (CV) and establish an identification model based on the KELM to classify the joint quality. The comparison results show that the joint quality identification model has a good generalization performance with an accuracy of up to 97.83%. Moreover, the feature extraction is reasonable, providing insight into the RSW process. The quality identification method based on the PSO and KELM is effective in RSW.
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Sun, H., Yang, J. & Wang, L. Resistance spot welding quality identification with particle swarm optimization and a kernel extreme learning machine model. Int J Adv Manuf Technol 91, 1879–1887 (2017). https://doi.org/10.1007/s00170-016-9944-y
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DOI: https://doi.org/10.1007/s00170-016-9944-y