A weld point cloud recognition method based on an improved Light Gradient Boosting Machine

A weld point cloud recognition method based on an improved Light Gradient Boosting Machine | 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 Article A weld point cloud recognition method based on an improved Light Gradient Boosting Machine Hongtao Yang, Ziqiang Bi, Xiulan Li, Qingan Yao, Peng Zhang, Haotian Bai, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8644300/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Accurate identification of weld regions is a critical step in weld quality inspection and automated grinding operations. However, weld point cloud data is typically characterized by high irregularity and lack of topological structure, making its efficient processing and accurate recognition a persistent research challenge. In recent years, advanced machine learning methods have shown significant potential for weld identification, yet research on 3D vision-based recognition of three-dimensional weld point clouds remains in its early stages. This study focuses on three typical weld seam trajectories—linear, curved, and S-shaped—conducting classification and recognition experiments based on point cloud data. Using Overall Accuracy, Precision, Recall, and F1-score as evaluation metrics, a systematic comparison was performed on the classification performance of three models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM)—across different neighborhood scales. Experimental results indicate that LightGBM achieved optimal classification performance within a 1.5mm neighborhood range. To further enhance the classification accuracy, this study introduced the Artificial Lemming Algorithm (LAL), Alpha Evolution Algorithm (AE), and Starfish Optimization Algorithm (SFOA) to improve the LightGBM model. The optimized LAL-LightGBM, AE-LightGBM, and SFOA-LightGBM models attained accuracy rates on linear, curved, and S-shaped test sets of (98.02%, 96.07%, 93.87%), (98.83%, 96.36%, 94.18%), and (98.00%, 96.12%, 93.91%), respectively. Comprehensive results demonstrate that the AE-LightGBM model performed best in overall evaluation, verifying the effectiveness of intelligent optimization algorithms in improving classification model performance. Furthermore, this study employed the SHAP interpretability framework to analyze the feature contributions of the AE-LightGBM model, effectively revealing its decision-making mechanism and enhancing model interpretability. This research provides a feasible technical pathway for robot-based weld grinding recognition tasks utilizing 3D vision and supervised machine learning. Physical sciences/Engineering Physical sciences/Mathematics and computing 3D vision weld seam recognition point cloud processing LightGBM intelligent optimization algorithms explainable artificial intelligence Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 02 Mar, 2026 Reviews received at journal 21 Feb, 2026 Reviewers agreed at journal 15 Feb, 2026 Reviewers invited by journal 28 Jan, 2026 Editor assigned by journal 28 Jan, 2026 Editor invited by journal 28 Jan, 2026 Submission checks completed at journal 23 Jan, 2026 First submitted to journal 23 Jan, 2026 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-8644300","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":582370237,"identity":"27fc280d-35d8-4857-8f7a-ff98ff7c066e","order_by":0,"name":"Hongtao Yang","email":"","orcid":"","institution":"Changchun University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Hongtao","middleName":"","lastName":"Yang","suffix":""},{"id":582370238,"identity":"358c6869-a82d-4af7-81e2-0ca753ce9c2e","order_by":1,"name":"Ziqiang Bi","email":"","orcid":"","institution":"Changchun University of 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Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"3D vision, weld seam recognition, point cloud processing, LightGBM, intelligent optimization algorithms, explainable artificial intelligence","lastPublishedDoi":"10.21203/rs.3.rs-8644300/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8644300/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate identification of weld regions is a critical step in weld quality inspection and automated grinding operations. However, weld point cloud data is typically characterized by high irregularity and lack of topological structure, making its efficient processing and accurate recognition a persistent research challenge. In recent years, advanced machine learning methods have shown significant potential for weld identification, yet research on 3D vision-based recognition of three-dimensional weld point clouds remains in its early stages. This study focuses on three typical weld seam trajectories\u0026mdash;linear, curved, and S-shaped\u0026mdash;conducting classification and recognition experiments based on point cloud data. Using Overall Accuracy, Precision, Recall, and F1-score as evaluation metrics, a systematic comparison was performed on the classification performance of three models\u0026mdash;Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM)\u0026mdash;across different neighborhood scales. Experimental results indicate that LightGBM achieved optimal classification performance within a 1.5mm neighborhood range. To further enhance the classification accuracy, this study introduced the Artificial Lemming Algorithm (LAL), Alpha Evolution Algorithm (AE), and Starfish Optimization Algorithm (SFOA) to improve the LightGBM model. The optimized LAL-LightGBM, AE-LightGBM, and SFOA-LightGBM models attained accuracy rates on linear, curved, and S-shaped test sets of (98.02%, 96.07%, 93.87%), (98.83%, 96.36%, 94.18%), and (98.00%, 96.12%, 93.91%), respectively. Comprehensive results demonstrate that the AE-LightGBM model performed best in overall evaluation, verifying the effectiveness of intelligent optimization algorithms in improving classification model performance. Furthermore, this study employed the SHAP interpretability framework to analyze the feature contributions of the AE-LightGBM model, effectively revealing its decision-making mechanism and enhancing model interpretability. This research provides a feasible technical pathway for robot-based weld grinding recognition tasks utilizing 3D vision and supervised machine learning.\u003c/p\u003e","manuscriptTitle":"A weld point cloud recognition method based on an improved Light Gradient Boosting Machine","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-30 10:24:34","doi":"10.21203/rs.3.rs-8644300/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-02T09:29:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-21T13:57:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"27364760325781426068849850139657801933","date":"2026-02-15T18:39:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-29T00:28:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-29T00:23:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-28T17:48:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-24T00:57:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-01-24T00:51:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3b4d35b9-ad92-473e-9fdb-c2ba39b0439d","owner":[],"postedDate":"January 30th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":61957868,"name":"Physical sciences/Engineering"},{"id":61957869,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-05-18T14:23:57+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-30 10:24:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8644300","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8644300","identity":"rs-8644300","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}