Knowledge Mapping of Artificial Intelligence and Industry 4.0 Enabling Technologies in Fault Prediction of 3D Printed Implant Process

Knowledge Mapping of Artificial Intelligence and Industry 4.0 Enabling Technologies in Fault Prediction of 3D Printed Implant Process | 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 Knowledge Mapping of Artificial Intelligence and Industry 4.0 Enabling Technologies in Fault Prediction of 3D Printed Implant Process keshav Rawat, Sandeep Sahoo, Girish Sharma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5581060/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The integration of Artificial intelligence(AI) and industry 4.0 technologies has transformed the production of bio implants through additive manufacturing (AM) and enhanced automation, streamlining all tasks across manufacturing line. AI, data science techniques and digital twin simulations has a vital role in defect monitoring and fault mitigation methods for 3D printed bio plants. Therefore, to assess the current state-of-the-art of the domain, the present study employs scientometric methods to systematically analyze the current state, research patterns, and future directions in the 3D printed implant process. The research literature is extracted from Scopus, covering the period of 2013 to 2023. The study explores country trends, keyword co-occurrence and document co-citation networks to identify the key technologies for integration in to 3D printed implant process such as porosity, biocompatibility, mechanical properties, and tissue engineering. The study findings reveal the growing significance of AI and Industry 4.0 technologies in improving design, customisation and functionality of implants, especially the areas like tissue scaffolds, tensile strength, and learning systems. AI and industry 4.0 food technology telecommunications, environmental applications, aerospace, tensile strength and AI. The present study provides a succinct overview of the developments in 3D printed implant process, enabling the data scientists, researchers, practitioners and business managers to exercise precise control, optimized customized designs, and informed decision making. Physical sciences/Materials science/Biomaterials/Implants Physical sciences/Materials science/Biomaterials/Tissues Bio-based 3D printing Industry 4.0 IoT Tissue scaffold Additive manufacturing Scientometric Analysis Document Co-citation Keyword co-occurrence Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-5581060","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":389105543,"identity":"8b3f5d69-a56e-452c-8c7a-2e9e65a6b237","order_by":0,"name":"keshav Rawat","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYBACCQYGNiBlA2YxwEhitKSRruUwUYohQLK9/dmDn23n5Q1uNzB++MFgkUdQizTPGXPD3rbbhhvuHGCW7GGQKCaoRU4ih02C58ztBIMbCQzSQOclNhDUIv/8meSfM+dAWph/E6VFWoLBTJqn4gBICxtxtkj25JhJy1QkG868kdhm2WNAhBaJ48efSb4xsJPnu5F8+MaPijrCWpAAI1CxAQnqR8EoGAWjYBTgBgBcwDZoDCws9wAAAABJRU5ErkJggg==","orcid":"","institution":"Central University of Haryana","correspondingAuthor":true,"prefix":"","firstName":"keshav","middleName":"","lastName":"Rawat","suffix":""},{"id":389105544,"identity":"a4748751-1f68-4f77-a050-fee8955bab2c","order_by":1,"name":"Sandeep Sahoo","email":"","orcid":"","institution":"National Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Sandeep","middleName":"","lastName":"Sahoo","suffix":""},{"id":389105545,"identity":"c5559bb6-68c3-457d-8005-3a31b1232269","order_by":2,"name":"Girish Sharma","email":"","orcid":"","institution":"Central University of Himachal Pradesh","correspondingAuthor":false,"prefix":"","firstName":"Girish","middleName":"","lastName":"Sharma","suffix":""}],"badges":[],"createdAt":"2024-12-04 15:25:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5581060/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5581060/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78867219,"identity":"c69b4711-6a2d-4a15-bc48-abc2aaf2d63d","added_by":"auto","created_at":"2025-03-20 04:37:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":615330,"visible":true,"origin":"","legend":"Article File","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5581060/v1_covered_59705ef0-6f2f-45f8-b187-b85547497a39.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Knowledge Mapping of Artificial Intelligence and Industry 4.0 Enabling Technologies in Fault Prediction of 3D Printed Implant Process","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bio-based 3D printing, Industry 4.0, IoT, Tissue scaffold, Additive manufacturing, Scientometric Analysis, Document Co-citation, Keyword co-occurrence","lastPublishedDoi":"10.21203/rs.3.rs-5581060/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5581060/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The integration of Artificial intelligence(AI) and industry 4.0 technologies has transformed the production of bio implants through additive manufacturing (AM) and enhanced automation, streamlining all tasks across manufacturing line.\r\n\tAI, data science techniques and digital twin simulations has a vital role in defect monitoring and fault mitigation methods for 3D printed bio plants. 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