Expulsion of Resistance Spot Welding Recognition and Diagnosis Based on PSO-SVM-SHAP

Expulsion of Resistance Spot Welding Recognition and Diagnosis Based on PSO-SVM-SHAP | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Expulsion of Resistance Spot Welding Recognition and Diagnosis Based on PSO-SVM-SHAP Zhijian Jiang, Lipeng Deng, De Wang, Hanfeng Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8510508/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Expulsion is a common defect in resistance spot welding, and expulsion occurring along the interface of joined plates can significantly compromise the quality of the weld spot. To address welding expulsion issues caused by factors such as fluctuating network voltage, unstable air pressure, and non-standard worker operations during production, this paper proposes a solution based on the Shapley additive explanation (SHAP) and particle swarm optimization support vector machine (PSO-SVM) classification method. The aim is to construct expulsion cause identification system for resistance spot welding using electrode displacement signals. By simulating three typical scenarios that lead to expulsion—excessive current, insufficient pressure, and unclean surfaces—the corresponding electrode displacement data were collected. Based on the electrode displacement data under abnormal conditions, seven electrode displacement signal features were manually extracted, and a feature set of electrode displacement signals was constructed. Subsequently, SHAP values were employed to select the optimal feature subset, and the model was trained and tested using a SVM optimized by the PSO algorithm. Experimental results indicate that the classification accuracy of the test set reaches 92.85%. Furthermore, this paper employs macro-average and micro-average precision, recall, and F1 scores as model evaluation criteria and conducts a comparative analysis with other common classification models. The findings reveal that only the PSO-SVM model surpasses 90% in both macro-average and micro-average metrics, with minimal differences in various indicators between the training and test sets, thus demonstrating superior generalization ability. Spot welding Mecision learning Shapley additive explanation Full Text Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8510508","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":574064247,"identity":"2871b15a-9526-4841-9275-cbbf43ad8b1c","order_by":0,"name":"Zhijian 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To address welding expulsion issues caused by factors such as fluctuating network voltage, unstable air pressure, and non-standard worker operations during production, this paper proposes a solution based on the Shapley additive explanation (SHAP) and particle swarm optimization support vector machine (PSO-SVM) classification method. The aim is to construct expulsion cause identification system for resistance spot welding using electrode displacement signals. By simulating three typical scenarios that lead to expulsion\u0026mdash;excessive current, insufficient pressure, and unclean surfaces\u0026mdash;the corresponding electrode displacement data were collected. Based on the electrode displacement data under abnormal conditions, seven electrode displacement signal features were manually extracted, and a feature set of electrode displacement signals was constructed. Subsequently, SHAP values were employed to select the optimal feature subset, and the model was trained and tested using a SVM optimized by the PSO algorithm. Experimental results indicate that the classification accuracy of the test set reaches 92.85%. Furthermore, this paper employs macro-average and micro-average precision, recall, and F1 scores as model evaluation criteria and conducts a comparative analysis with other common classification models. 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