Investigation of failures in rotational moulding using historical production dataset and machine learning

Investigation of failures in rotational moulding using historical production dataset and machine learning | 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 Investigation of failures in rotational moulding using historical production dataset and machine learning Baris Ördek, James Mcgree, Paul Corry, Christian Spreafico This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7067025/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Nov, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 5 You are reading this latest preprint version Abstract Rotational moulding (RM) is a versatile manufacturing process widely used for producing lightweight, seamless plastic components, but its potential is often constrained by challenges in optimizing production parameters for diverse product geometries and simultaneous batch production. This study addresses the pressing need for a data-driven approach to enhance RM efficiency and reduce defects under non-optimal process conditions. Leveraging historical production data from a medium-sized RM enterprise, an Ensemble Learning-based machine learning (ML) model was developed to predict failure probabilities across 390 product-process combinations. Input parameters are heating temperature, speed, mould volume, product mass. The model achieved an accuracy of 97.17%, identifying optimal parameter ranges for minimizing defects. The results revealed that deviations between machine and product-specific conditions, particularly in heating temperature and rotational speed, significantly increased failure probabilities. Products with intermediate sizes and masses were most susceptible to failures, while extreme values of mould volume occupancy showed a lower likelihood of failures. Notably, the study highlighted the critical importance of maintaining minimal delta heating temperature and speed ratio disparities to ensure product quality. This approach offers a robust framework for optimizing RM processes without costly sensorization, making it especially beneficial for small- and medium-sized enterprises. Rotational moulding Machine learning Failure Prediction Design for manufacturing Full Text Cite Share Download PDF Status: Published Journal Publication published 12 Nov, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Major Revisions Needed 02 Oct, 2025 Reviewers agreed at journal 07 Sep, 2025 Reviewers invited by journal 10 Jul, 2025 Editor assigned by journal 09 Jul, 2025 First submitted to journal 07 Jul, 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. 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-7067025","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":483714905,"identity":"74ef275c-7f79-4655-9bae-0eee47218272","order_by":0,"name":"Baris Ördek","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Baris","middleName":"","lastName":"Ördek","suffix":""},{"id":483714906,"identity":"e743cb71-5034-4805-99f9-581c042d9216","order_by":1,"name":"James Mcgree","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"James","middleName":"","lastName":"Mcgree","suffix":""},{"id":483714907,"identity":"8e4bd8af-f8b8-4b0d-829b-4d59b4ed6285","order_by":2,"name":"Paul Corry","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"","lastName":"Corry","suffix":""},{"id":483714908,"identity":"83ca2cfe-f185-496d-bbc8-a19e272ad296","order_by":3,"name":"Christian Spreafico","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-3442-2063","institution":"University of Bergamo: Universita degli Studi di Bergamo","correspondingAuthor":true,"prefix":"","firstName":"Christian","middleName":"","lastName":"Spreafico","suffix":""}],"badges":[],"createdAt":"2025-07-07 15:27:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7067025/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7067025/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00170-025-16925-6","type":"published","date":"2025-11-12T15:57:42+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":96105053,"identity":"a7356e3a-30e1-40aa-ba9b-a467768c3a40","added_by":"auto","created_at":"2025-11-17 16:07:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":936167,"visible":true,"origin":"","legend":"","description":"","filename":"Rotationalsyntheticdata9.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7067025/v1_covered_e365d08a-d01c-4cdf-bc45-ed0e3174ffde.pdf"}],"financialInterests":"","formattedTitle":"Investigation of failures in rotational moulding using historical production dataset and machine learning","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":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"Rotational moulding, Machine learning, Failure Prediction, Design for manufacturing","lastPublishedDoi":"10.21203/rs.3.rs-7067025/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7067025/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Rotational moulding (RM) is a versatile manufacturing process widely used for producing lightweight, seamless plastic components, but its potential is often constrained by challenges in optimizing production parameters for diverse product geometries and simultaneous batch production. 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