Integrating Machine Learning with Multimodal Monitoring System Utilizing Acoustic and Vision Sensing to Evaluate Geometric Variations in Laser Directed Energy Deposition

Integrating Machine Learning with Multimodal Monitoring System Utilizing Acoustic and Vision Sensing to Evaluate Geometric Variations in Laser Directed Energy Deposition | 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 Integrating Machine Learning with Multimodal Monitoring System Utilizing Acoustic and Vision Sensing to Evaluate Geometric Variations in Laser Directed Energy Deposition ke xu, Chaitanya Krishna Vallabh, Souran Manoochehri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7435959/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 Laser directed energy deposition (DED) additive manufacturing faces significant challenges in maintaining consistent part quality due to complex melt pool dynamics and process variations. While extensive research has focused on part defect detection, limited work has systematically validated the capability of utilizing process monitoring systems to evaluate melt pool dynamics, and process quality. This study presents a novel multimodal monitoring framework that synergistically integrates contact-based acoustic emission (AE) sensing with vision-based coaxial camera monitoring to enable layer-wise identification and evaluation of geometric variations in DED fabricated parts. The experimental study employs three distinct part configurations: a baseline part without any holes, the part with 3mm diameter through-hole, and the part with 5mm diameter through-hole. These specimens are served as test cases to evaluate the monitoring system's discerning capabilities. Raw sensor data undergoes preprocessing, where acoustic signals are subjected to filtering and feature extraction using time-domain and frequency-domain analysis, while coaxial camera data undergoes melt pool segmentation followed by morphological feature extraction. Multiple machine learning algorithms including support vector machines (SVM), neural networks, random forest, gradient boosting, XGBoost, and logistic regression are evaluated to identify optimal models for layer-wise geometric variations classification. The integrated multimodal strategy demonstrates superior classification performance of 94.4% as compared to individual sensing modalities of 87.8% (AE only) and 86.7% (Camera only). Experimental validation reveals that the integrated multimodal monitoring system effectively captures both structural vibration signatures and surface morphological changes associated with studied geometric variations. While this study focuses on specified part geometric variations, the demonstrated capability to discriminate between different geometric features establishes the technical foundation for future applications in characterizing any part variations such as geometric accuracies and manufacturing-induced defects. Laser Directed Energy Deposition Multimodal Monitoring Acoustic Emission Machine Learning Geometric Variation Classification Additive Manufacturing Quality Control 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 19 Oct, 2025 Reviewers agreed at journal 27 Aug, 2025 Reviewers invited by journal 27 Aug, 2025 Editor assigned by journal 27 Aug, 2025 First submitted to journal 25 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. 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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-7435959","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":506385134,"identity":"ed8f7c50-08a6-450b-9649-06725a572988","order_by":0,"name":"ke xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYBACCQYG9h8fEHxmorQwSM5gMCBRizQPSVokZ+QYGNv8+SNncLz38AeGCuvEBkJapHnOGCTnthkYG5w5lybBcCadsBY59h6Dw7kNBokzZ+SYMTC2HSZCCzOPYbPFH4P6mfPfGH9g/EeEFmn2HmNmBjaDBH4JHgMJxgYitEj2HCtj7G0zNuznyTGTSDiWbkxQi8SN5G0MP/7IybOxnzH+8KHGWpagFlSQQJryUTAKRsEoGAW4AACIUTb+rte75QAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-2439-1942","institution":"Stevens Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"ke","middleName":"","lastName":"xu","suffix":""},{"id":506385135,"identity":"60f0bf1b-ce83-480c-9460-9429e1cb5dd2","order_by":1,"name":"Chaitanya Krishna Vallabh","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Chaitanya","middleName":"Krishna","lastName":"Vallabh","suffix":""},{"id":506385136,"identity":"1d4ff9e6-3f58-4314-8b58-58b4a1c2e9d4","order_by":2,"name":"Souran Manoochehri","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Souran","middleName":"","lastName":"Manoochehri","suffix":""}],"badges":[],"createdAt":"2025-08-22 15:47:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7435959/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7435959/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00170-025-16946-1","type":"published","date":"2025-11-12T15:57:26+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":96104985,"identity":"11918058-f13e-46f3-9018-38c4d3548f72","added_by":"auto","created_at":"2025-11-17 16:06:17","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2343834,"visible":true,"origin":"","legend":"","description":"","filename":"KeXuSubmissionWordVersion.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7435959/v1_covered_a9cfb630-42fe-4101-bff6-a0dcf203ef0d.pdf"}],"financialInterests":"","formattedTitle":"Integrating Machine Learning with Multimodal Monitoring System Utilizing Acoustic and Vision Sensing to Evaluate Geometric Variations in Laser Directed Energy Deposition","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":"Laser Directed Energy Deposition, Multimodal Monitoring, Acoustic Emission, Machine Learning, Geometric Variation Classification, Additive Manufacturing, Quality Control","lastPublishedDoi":"10.21203/rs.3.rs-7435959/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7435959/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLaser directed energy deposition (DED) additive manufacturing faces significant challenges in maintaining consistent part quality due to complex melt pool dynamics and process variations. 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