An Artificial Intelligence Approach to Predicting Processing Parameters for Liquid Composite Molded (LCM) Carbon Fibre-Reinforced Plastics (CFRPs)

An Artificial Intelligence Approach to Predicting Processing Parameters for Liquid Composite Molded (LCM) Carbon Fibre-Reinforced Plastics (CFRPs) | 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 An Artificial Intelligence Approach to Predicting Processing Parameters for Liquid Composite Molded (LCM) Carbon Fibre-Reinforced Plastics (CFRPs) Jennifer L Sears, Jennifer Johrendt This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5634297/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 This study developed a comparative machine learning (ML) framework to predict optimal processing parameters and mechanical properties for automotive-grade carbon fibre-reinforced plastics (CFRPs) produced via Liquid Composite Molding (LCM). Multi-linear- (MLR), support vector- (SVR), random forest- (RFR), and gradient boosting regression (GBR) algorithms were coupled with artificial neural network (ANNs) using multi-layer perceptron (MLP), and single and dual-path functional application programming interface (FAPIs) models to increase the prediction outcomes. The models developed herein can be tailored to the case-specific needs of the application. The results demonstrated that the RFR model, enhanced with hyperparameter tuning, achieved the highest predictive accuracy, explaining 45% of the variability in the data for the linear regression case. The FAPI model using Keras® and Tensorflow® exhibited superior performance for the non-linear case, with test predictions of 78.20% for flexural strength, 73.8% for flexural modulus and 99.97% for binder use. Additionally, thermal and demolding analysis expanded the complexity of the predictions by enhancing the correlation to part quality, ultimately accelerating product development and providing industry with accurate predictive modelling tools. machine learning (ML) predictive modelling processing parameters carbon fibre-reinforced plastics (CFRPs) Full Text Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major Revisions Needed 01 May, 2025 Reviewers agreed at journal 12 Feb, 2025 Reviewers invited by journal 18 Dec, 2024 Editor assigned by journal 18 Dec, 2024 First submitted to journal 16 Dec, 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. 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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-5634297","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":392008825,"identity":"47cb7cc1-16cb-422b-84f2-ff38495fe389","order_by":0,"name":"Jennifer L Sears","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYLACxn82DAzMDAwSJOhhSyNdy2EwRZwW+fbmZx9/8JxP3HCc9+DNHxX3GPjbD+DXYnDmmPFsHonbiRsO8yVb85wpZpA4k0BAi0SCMTODwe3EbYd5zKQZ2xIYDBgIaJGf//wz44+Ec2Atkj9BWvgfEPDMDR5jBp4DB8BaJHhBWiQIOexMTjEzb0Oy8f7DPMZAvyTwSNwgYIt8+/HNjD8b7GRn9p8xBIZYghx/PwFbMAAPiepHwSgYBaNgFGADADHXP0pH/1ESAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0003-9487-9254","institution":"University of Windsor","correspondingAuthor":true,"prefix":"","firstName":"Jennifer","middleName":"L","lastName":"Sears","suffix":""},{"id":392008826,"identity":"f220248e-c600-4241-9e07-547e29e27457","order_by":1,"name":"Jennifer Johrendt","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jennifer","middleName":"","lastName":"Johrendt","suffix":""}],"badges":[],"createdAt":"2024-12-12 22:37:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5634297/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5634297/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71997331,"identity":"e9b5d4c6-9628-4eb8-9009-defe40fd2578","added_by":"auto","created_at":"2024-12-20 12:42:20","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2036308,"visible":true,"origin":"","legend":"","description":"","filename":"IJAMTSearsJohrendt.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5634297/v1_covered_1ed3d507-3311-4401-bb0c-ee0c722bb952.pdf"}],"financialInterests":"","formattedTitle":"An Artificial Intelligence Approach to Predicting Processing Parameters for Liquid Composite Molded (LCM) Carbon Fibre-Reinforced Plastics (CFRPs)","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":"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":"machine learning (ML), predictive modelling, processing parameters, carbon fibre-reinforced plastics (CFRPs)","lastPublishedDoi":"10.21203/rs.3.rs-5634297/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5634297/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study developed a comparative machine learning (ML) framework to predict optimal processing parameters and mechanical properties for automotive-grade carbon fibre-reinforced plastics (CFRPs) produced via Liquid Composite Molding (LCM). Multi-linear- (MLR), support vector- (SVR), random forest- (RFR), and gradient boosting regression (GBR) algorithms were coupled with artificial neural network (ANNs) using multi-layer perceptron (MLP), and single and dual-path functional application programming interface (FAPIs) models to increase the prediction outcomes. The models developed herein can be tailored to the case-specific needs of the application. The results demonstrated that the RFR model, enhanced with hyperparameter tuning, achieved the highest predictive accuracy, explaining 45% of the variability in the data for the linear regression case. The FAPI model using Keras\u0026reg; and Tensorflow\u0026reg; exhibited superior performance for the non-linear case, with test predictions of 78.20% for flexural strength, 73.8% for flexural modulus and 99.97% for binder use. 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