Advancing Low-Carbon Additive Manufacturing: An Integrated Deep Learning Approach for Optimal Resource and Capacity Management

Advancing Low-Carbon Additive Manufacturing: An Integrated Deep Learning Approach for Optimal Resource and Capacity Management | 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 Advancing Low-Carbon Additive Manufacturing: An Integrated Deep Learning Approach for Optimal Resource and Capacity Management Bangtong Huang, Qi Xu, Tianchen Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8999503/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract As Additive Manufacturing (AM) shifts towards Make-to-Order (MTO) models, synchronizing raw material inventory with machine capacity becomes critical for handling stochastic demand. However, existing literature often treats inventory control and capacity planning as decoupled problems and frequently overlooks the carbon emissions associated with machine rental logistics. To address these gaps, this study proposes a data-driven integrated decision-making framework for the joint optimization of raw material replenishment and machine leasing. Specifically, we develop an end-to-end hybrid CNN-LSTM deep learning model, where Convolutional Neural Networks (CNN) extract local demand fluctuation patterns and Long Short-Term Memory (LSTM) networks capture long-term temporal dependencies. The model explicitly incorporates carbon emission costs from both material transport and equipment logistics into the objective function. Numerical experiments based on a 550-day simulation using real industrial data demonstrate the superiority of the proposed approach. The results indicate that the CNN-LSTM strategy achieves a total cost gap of only 12.53% relative to the theoretical optimal solution, significantly outperforming traditional Base Stock (+29.69%) and Constant Order (+34.35%) policies. Furthermore, while incurring a increase in carbon emissions (+7.76% over optimal), the proposed model achieves a 100% on-time delivery rate, offering a robust trade-off between economic efficiency, service levels, and environmental sustainability. Physical sciences/Energy science and technology Physical sciences/Engineering Earth and environmental sciences/Environmental sciences Physical sciences/Mathematics and computing Additive Manufacturing Inventory Management CNN-LSTM Carbon Emission Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 01 Apr, 2026 Reviews received at journal 30 Mar, 2026 Reviews received at journal 28 Mar, 2026 Reviewers agreed at journal 20 Mar, 2026 Reviewers agreed at journal 20 Mar, 2026 Reviewers invited by journal 18 Mar, 2026 Editor assigned by journal 18 Mar, 2026 Editor invited by journal 16 Mar, 2026 Submission checks completed at journal 13 Mar, 2026 First submitted to journal 13 Mar, 2026 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-8999503","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":609968883,"identity":"e64e09c6-c667-44d0-8bc5-962301409cff","order_by":0,"name":"Bangtong Huang","email":"","orcid":"","institution":"Donghua University","correspondingAuthor":false,"prefix":"","firstName":"Bangtong","middleName":"","lastName":"Huang","suffix":""},{"id":609968884,"identity":"0aca4371-23b1-4c5b-898a-e2ab761dff58","order_by":1,"name":"Qi Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYJCCAwwVEnLy7I0PQBwePuK0nLExNuw5bADWwkaUNYxtaYkNN5LBWhgIajG4kbzxAAPbYcbGmY8ZH/Pm2MmwMTA/fHQDjxbJGWkFBxh4DjOzSyczG87clgx0GJuxcQ4eLfwSOQYHGCQOszHOzj8m8XEbM1ALD5s0Pi1sYC0Gh3kYbh5mk0jcVk9YC8SWhDQJhhvMbEBbDhPWItnzDOiXAzYGhj1gvxznYWMm4BeD48mbPzD+k6ifz34YGGLbqu352ZsfPsanBaSL+Q8Knxm/crAWwkpGwSgYBaNgZAMAqTBCw9RO83IAAAAASUVORK5CYII=","orcid":"","institution":"Donghua University","correspondingAuthor":true,"prefix":"","firstName":"Qi","middleName":"","lastName":"Xu","suffix":""},{"id":609968886,"identity":"3a3a2852-68ea-478e-8d37-712f5a02d951","order_by":2,"name":"Tianchen Yang","email":"","orcid":"","institution":"Shanghai University of Engineering Sciences","correspondingAuthor":false,"prefix":"","firstName":"Tianchen","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2026-03-01 06:08:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8999503/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8999503/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105563514,"identity":"fac974b7-c3e5-4c2c-8b95-6df86e4ef249","added_by":"auto","created_at":"2026-03-27 12:47:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10230877,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptlatex.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8999503/v1_covered_da450ceb-71ce-4cb6-92b9-a13a0257e17e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Advancing Low-Carbon Additive Manufacturing: An Integrated Deep Learning Approach for Optimal Resource and Capacity Management","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Additive Manufacturing, Inventory Management, CNN-LSTM, Carbon Emission","lastPublishedDoi":"10.21203/rs.3.rs-8999503/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8999503/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"As Additive Manufacturing (AM) shifts towards Make-to-Order (MTO) models, synchronizing raw material inventory with machine capacity becomes critical for handling stochastic demand. 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