Optimized Milling Processes for Carbon Emissions Based on Hybrid Multi-Objective Algorithms

Optimized Milling Processes for Carbon Emissions Based on Hybrid Multi-Objective Algorithms | 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 Optimized Milling Processes for Carbon Emissions Based on Hybrid Multi-Objective Algorithms Wen-Yang Chang, Yu-Hsiang Lin, Zheng-Xun Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8062067/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 To address the accelerated implementation of global carbon neutrality policies, this study proposes a milling process parameter control system that is predictive, optimizable, and low-carbon oriented. The system employs a non-destructive clamp-type ammeter to measure the instantaneous current of the machine tool in real time during machining, enabling accurate estimation of energy consumption and carbon emissions, and providing high-resolution data for subsequent modeling. Based on these data, a continuous prediction model is constructed using multiple polynomial regression combined with cubic spline interpolation. This model is then integrated into a hybrid multi-objective optimization framework that combines Multi-Objective Particle Swarm Optimization (MOPSO) and the Nondominated Sorting Genetic Algorithm III (NSGA-III) to optimize carbon emissions, machining time, and surface roughness. Experimental results demonstrate that, under both fixed cutting depth and full decision space scenarios, the maximum carbon emission reduction rates reached 19.12% and 28.49%, respectively, while the best verification error rates in actual machining were −0.66% and −6.82%. Furthermore, an MVC architecture, combined with an MSSQL database and the MQTT protocol, was employed to develop a responsive web interface, enabling real-time data transmission, visualization, and interactive decision support. Full Text Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major Revisions Needed 12 Dec, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviewers invited by journal 11 Nov, 2025 Editor assigned by journal 11 Nov, 2025 First submitted to journal 07 Nov, 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-8062067","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":543501684,"identity":"a132cac1-7fe8-4c90-bbb9-28028754d922","order_by":0,"name":"Wen-Yang 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Algorithms","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":"","lastPublishedDoi":"10.21203/rs.3.rs-8062067/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8062067/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"To address the accelerated implementation of global carbon neutrality policies, this study proposes a milling process parameter control system that is predictive, optimizable, and low-carbon oriented. The system employs a non-destructive clamp-type ammeter to measure the instantaneous current of the machine tool in real time during machining, enabling accurate estimation of energy consumption and carbon emissions, and providing high-resolution data for subsequent modeling. Based on these data, a continuous prediction model is constructed using multiple polynomial regression combined with cubic spline interpolation. This model is then integrated into a hybrid multi-objective optimization framework that combines Multi-Objective Particle Swarm Optimization (MOPSO) and the Nondominated Sorting Genetic Algorithm III (NSGA-III) to optimize carbon emissions, machining time, and surface roughness. Experimental results demonstrate that, under both fixed cutting depth and full decision space scenarios, the maximum carbon emission reduction rates reached 19.12% and 28.49%, respectively, while the best verification error rates in actual machining were −0.66% and −6.82%. Furthermore, an MVC architecture, combined with an MSSQL database and the MQTT protocol, was employed to develop a responsive web interface, enabling real-time data transmission, visualization, and interactive decision support.","manuscriptTitle":"Optimized Milling Processes for Carbon Emissions Based on Hybrid Multi-Objective Algorithms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-21 05:36:07","doi":"10.21203/rs.3.rs-8062067/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revisions Needed","date":"2025-12-12T08:20:53+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-11-25T16:04:25+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-11T18:12:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-11T14:44:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"The International Journal of Advanced Manufacturing Technology","date":"2025-11-08T02:08:41+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"19cf1746-58be-4eed-9c78-0d279f509a65","owner":[],"postedDate":"November 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-13T14:20:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-21 05:36:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8062067","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8062067","identity":"rs-8062067","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}