Morphological Analysis and Intelligent Optimization of the Fusion Zone in Electrofusion Welding of PE Pipes

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Morphological Analysis and Intelligent Optimization of the Fusion Zone in Electrofusion Welding of PE Pipes | 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 Morphological Analysis and Intelligent Optimization of the Fusion Zone in Electrofusion Welding of PE Pipes Tao Shen, Yuan Chen, Weiyu Liu, Jingtai Liu, Xiaogang Wang, Bingqing Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7357134/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Taking dn400 large-diameter PE electrofusion fittings as the research object, a theoretical model of electrofusion welding was established and experimental verification was completed. Based on this, the influence of welding parameters and wire feeding methods on the morphology of the fusion zone and welding performance was analyzed. A welding database was established using finite element simulation calculations and Latin hypercube sampling (LHS). Random forest (RF) was used to distinguish the fusion state under different welding parameters and wire feeding methods, and BP neural network (BPNN) was used to predict welding performance. The particle swarm (PSO) algorithm was used to optimize the morphology of the fusion zone and welding performance. The results showed: compared with uniform wire distribution, non-uniform wire distribution has a better effect on controlling and improving the morphology and performance indicators of the fusion zone. The RF classification model and BPNN both achieved an accuracy of over 96% in distinguishing the fusion state and predicting welding performance. The PSO optimization algorithm can significantly improve the morphology of the fusion zone, with all welding performance indicators improving by more than 8%. PE electrofusion fittings fusion zone morphology machine learning non-uniform wire distribution heuristic algorithms parameter optimization Full Text Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Minor Revisions Needed 28 Apr, 2026 Reviewers agreed at journal 07 Oct, 2025 Reviewers invited by journal 30 Sep, 2025 Editor assigned by journal 14 Aug, 2025 First submitted to journal 12 Aug, 2025 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. 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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-7357134","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":523157295,"identity":"f22f5e0b-9e53-4ba2-853c-e78b85595a09","order_by":0,"name":"Tao 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Based on this, the influence of welding parameters and wire feeding methods on the morphology of the fusion zone and welding performance was analyzed. A welding database was established using finite element simulation calculations and Latin hypercube sampling (LHS). Random forest (RF) was used to distinguish the fusion state under different welding parameters and wire feeding methods, and BP neural network (BPNN) was used to predict welding performance. The particle swarm (PSO) algorithm was used to optimize the morphology of the fusion zone and welding performance. The results showed: compared with uniform wire distribution, non-uniform wire distribution has a better effect on controlling and improving the morphology and performance indicators of the fusion zone. The RF classification model and BPNN both achieved an accuracy of over 96% in distinguishing the fusion state and predicting welding performance. The PSO optimization algorithm can significantly improve the morphology of the fusion zone, with all welding performance indicators improving by more than 8%.\u003c/p\u003e","manuscriptTitle":"Morphological Analysis and Intelligent Optimization of the Fusion Zone in Electrofusion Welding of PE Pipes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-14 14:18:25","doi":"10.21203/rs.3.rs-7357134/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor Revisions Needed","date":"2026-04-29T01:14:34+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-10-07T11:13:25+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-01T03:40:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-14T08:49:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"The International Journal of Advanced Manufacturing Technology","date":"2025-08-12T11:05:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-international-journal-of-advanced-manufacturing-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jamt","sideBox":"Learn more about [The International Journal of Advanced Manufacturing Technology](https://www.springer.com/journal/170)","snPcode":"170","submissionUrl":"https://submission.nature.com/new-submission/170/3","title":"The International Journal of Advanced Manufacturing Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a9c0ae99-f1c8-4e43-ba87-fac295779047","owner":[],"postedDate":"October 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-05T16:39:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-14 14:18:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7357134","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7357134","identity":"rs-7357134","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}

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