Prediction and Optimization Method for Welding Quality of Components in Ship Construction

Prediction and Optimization Method for Welding Quality of Components in Ship Construction | 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 Prediction and Optimization Method for Welding Quality of Components in Ship Construction Jinfeng Liu, Yifa Cheng, Xuwen Jing, Xiaojun Liu, Yu Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3849855/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Apr, 2024 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract Welding process, as one of the crucial industrial technologies in ship construction, accounts for approximately 70% of the workload and costs account for approximately 40% of the total cost. The existing welding quality prediction methods have hypothetical premises and subjective factors, which cannot meet the dynamic control requirements of intelligent welding for processing quality. Aiming at the low efficiency of quality prediction problems poor timeliness and unpredictability of quality control in ship assembly-welding process, a data and model driven welding quality prediction method is proposed. Firstly, the influence factors of welding quality are analyzed and the correlation mechanism between process parameters and quality is determined. According to the analysis results, a stable and reliable data collection architecture is established. And the welding process monitoring elements are determined based on the feature dimensionality reduction methods. To improve the accuracy of welding quality prediction, the prediction model is constructed by fusing the adaptive simulated annealing, the particle swarm optimization, and the back propagation neural network algorithms. Finally, the effectiveness of the prediction method is verified through 24 sets of plate welding experiments, the prediction accuracy reaches over 90%. Physical sciences/Engineering/Mechanical engineering Physical sciences/Mathematics and computing/Software Quality prediction Components welding Welding quality Process parameter optimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Apr, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 08 Feb, 2024 Reviews received at journal 01 Feb, 2024 Reviewers agreed at journal 31 Jan, 2024 Reviewers invited by journal 30 Jan, 2024 Editor assigned by journal 30 Jan, 2024 Editor invited by journal 18 Jan, 2024 Submission checks completed at journal 17 Jan, 2024 First submitted to journal 10 Jan, 2024 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. 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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-3849855","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":267559611,"identity":"54f43bd6-6e3a-4729-b1b1-b08ca4db6b2c","order_by":0,"name":"Jinfeng Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIie3SoY7CMBwG8D9ZMky52p0Zr/AtFZdT9yolZxFLeABQxYDH3BucGIYgd8Hugp3cGRSXYJdU0FIUoptE9DP90vSXf5OWKCTkCfMSEUFSampUwu1JP4kdEbZKQ9CDuMUSZo/3IUOG/G+P9I1/X2ZMa+LDKajd+y7GgEkF8b75L8RIgV5XZwzWVRdRelLUv4YsQKiniAaqk2Be1FUjmAZ99CUSxxUJFpspSSeJc0uyoo6RfSnBkuqU/6w9hPPDLmsVxjgeTjjrNOXLz23Teogd5B49kbfCbC+9wHyUxs0r7yUkJCQk5CFXxAZIPeKykssAAAAASUVORK5CYII=","orcid":"","institution":"Jiangsu University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Jinfeng","middleName":"","lastName":"Liu","suffix":""},{"id":267559612,"identity":"b1ccb405-eda3-48a9-8a32-8b3646153384","order_by":1,"name":"Yifa Cheng","email":"","orcid":"","institution":"Jiangsu University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yifa","middleName":"","lastName":"Cheng","suffix":""},{"id":267559613,"identity":"9abba9ae-7f3f-4220-b999-0ac0325a1bdb","order_by":2,"name":"Xuwen Jing","email":"","orcid":"","institution":"Jiangsu University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xuwen","middleName":"","lastName":"Jing","suffix":""},{"id":267559614,"identity":"3aeffea9-c166-47e7-9ecb-18d3cf96f5c3","order_by":3,"name":"Xiaojun Liu","email":"","orcid":"","institution":"Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Xiaojun","middleName":"","lastName":"Liu","suffix":""},{"id":267559615,"identity":"8da09cc5-99b9-4155-82e7-a74876d5bb0d","order_by":4,"name":"Yu Chen","email":"","orcid":"","institution":"Jiangsu University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-01-10 09:30:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3849855/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3849855/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-59490-w","type":"published","date":"2024-04-23T18:05:51+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":55398962,"identity":"702cd787-66f1-417a-acc2-3071e9c01224","added_by":"auto","created_at":"2024-04-26 18:06:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1366205,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscriptv3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3849855/v1_covered_9a18c261-b514-4f43-ae09-e59a48ac9daa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prediction and Optimization Method for Welding Quality of Components in Ship Construction","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"Quality prediction, Components welding, Welding quality, Process parameter optimization","lastPublishedDoi":"10.21203/rs.3.rs-3849855/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3849855/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWelding process, as one of the crucial industrial technologies in ship construction, accounts for approximately 70% of the workload and costs account for approximately 40% of the total cost. The existing welding quality prediction methods have hypothetical premises and subjective factors, which cannot meet the dynamic control requirements of intelligent welding for processing quality. Aiming at the low efficiency of quality prediction problems poor timeliness and unpredictability of quality control in ship assembly-welding process, a data and model driven welding quality prediction method is proposed. Firstly, the influence factors of welding quality are analyzed and the correlation mechanism between process parameters and quality is determined. According to the analysis results, a stable and reliable data collection architecture is established. And the welding process monitoring elements are determined based on the feature dimensionality reduction methods. 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