Smart manufacturing towards cyber-physical resilience in 3D printing process monitoring and anomaly detection

Smart manufacturing towards cyber-physical resilience in 3D printing process monitoring and anomaly detection | 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 Smart manufacturing towards cyber-physical resilience in 3D printing process monitoring and anomaly detection Abdullah Al Mamun, Murat Kuzlu, Vukica Jovanović, Winston Sealy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7368558/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Oct, 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 The digital threads of additive manufacturing (AM), originating from smart manufacturing, leverage Cyber-Physical Systems (CPS) that integrate interconnected cyber and physical domains, wherein the cyber domain encompasses product design processes (e.g., CAD modeling, .STL file creation, G-code generation, and cloud-based data sharing), while the physical domain employs the G-code to drive 3D printing and enable post-process monitoring. G-code modifications are a common vector for malicious cyberattacks on AM systems, enabling alterations to part designs and print parameters that disrupt the printing process and degrade the mechanical properties and functionality of mission-critical parts. To detect cyber-attacks in AM systems, this study employs National Institute of Standards and Technology (NIST) cybersecurity frameworks to systematically detect cyber-attacks in AM systems and uses a fused filament fabrication (FFF) 3D printer testbed to simulate raster angle-based alteration classification (RAAC) for sensitivity analysis. This work focuses on G-code alteration detection using Euclidean distances along the x, y, and z axes, and employs layer-by-layer in-situ video monitoring of the FFF process via a digital microscope camera mounted on the extruder head to identify fabrication defects and potential cyber-physical attacks. Thus, layer-wise image frames are processed using adaptive region-of-interests (ROIs) to adjust spatiotemporal image fames’ raster angle and RGB-to-grayscale conversion to extract rasterized surface texture features (STFs), which capture surface patterns at varying raster angles and are refined using principal component analysis (PCA) for dimensionality reduction and feature extraction. This study evaluates a multiclass support vector machine (SVM) to detect RAAC attacks on AM systems. The model’s effectiveness is measured by using confusion matrices to analyze classification accuracy. The RAAC approach proved more effective than benchmark methods like Independent Component Analysis (ICA) and Convolutional Neural Networks (CNNs). Anomaly Detection Cyber-Physical Systems Raster Angle Smart Manufacturing Surface Texture Features Full Text Cite Share Download PDF Status: Published Journal Publication published 22 Oct, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Minor Revisions Needed 28 Sep, 2025 Reviewers agreed at journal 22 Aug, 2025 Reviewers invited by journal 15 Aug, 2025 Editor assigned by journal 15 Aug, 2025 First submitted to journal 13 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. 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-7368558","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501022393,"identity":"9597ac36-ea9b-4db7-a6d4-799207ddce62","order_by":0,"name":"Abdullah Al Mamun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYDACZgglx8CQAKTYiNHCDqGMSdDCD6ESG4jWwt/MnfjpZtud9H72HAOGD2WHCWuROMy7WTq37VnuzJ43BowzzhGhheEw7waglsO5G27kGDDzthGhRR5oy2+glnQDkJa/xGgxOMy7DWRLAlgLIzFaDIFarHPOHTac2fOs4GDPuXTCWuTOn918O6fssDw/e/LGBz/KrAlrQQEHSFQ/CkbBKBgFowAXAAAunDwos+tRkgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-9199-8974","institution":"Old Dominion University","correspondingAuthor":true,"prefix":"","firstName":"Abdullah","middleName":"Al","lastName":"Mamun","suffix":""},{"id":501022394,"identity":"8aa8873b-905b-4e0b-93da-89f713e31ac3","order_by":1,"name":"Murat Kuzlu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Murat","middleName":"","lastName":"Kuzlu","suffix":""},{"id":501022395,"identity":"656531b7-0cc2-4b4b-a04b-ecf451cc7463","order_by":2,"name":"Vukica Jovanović","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Vukica","middleName":"","lastName":"Jovanović","suffix":""},{"id":501022396,"identity":"7477be31-6abf-4e58-b143-11c31597f933","order_by":3,"name":"Winston Sealy","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Winston","middleName":"","lastName":"Sealy","suffix":""}],"badges":[],"createdAt":"2025-08-14 00:30:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7368558/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7368558/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00170-025-16795-y","type":"published","date":"2025-10-22T16:16:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":94490119,"identity":"09c4892b-d2fe-4bd8-b0cd-9647feb14934","added_by":"auto","created_at":"2025-10-27 17:07:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1556980,"visible":true,"origin":"","legend":"","description":"","filename":"SmartmanufacturingAM.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7368558/v1_covered_7a1c7ebc-1c21-4a2b-9b90-ba8aeca635e6.pdf"}],"financialInterests":"","formattedTitle":"Smart manufacturing towards cyber-physical resilience in 3D printing process monitoring and anomaly detection","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":"Anomaly Detection, Cyber-Physical Systems, Raster Angle, Smart Manufacturing, Surface Texture Features","lastPublishedDoi":"10.21203/rs.3.rs-7368558/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7368558/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The digital threads of additive manufacturing (AM), originating from smart manufacturing, leverage Cyber-Physical Systems (CPS) that integrate interconnected cyber and physical domains, wherein the cyber domain encompasses product design processes (e.g., CAD modeling, .STL file creation, G-code generation, and cloud-based data sharing), while the physical domain employs the G-code to drive 3D printing and enable post-process monitoring. 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