Stochastic Response Analysis for Multivariate Manufacturing Processes

Stochastic Response Analysis for Multivariate Manufacturing Processes | 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 Stochastic Response Analysis for Multivariate Manufacturing Processes Jaeman Kim, Sang Ki Kim, Seung-Hwan Park, Jihoon Kang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9326628/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract In modern manufacturing, artificial intelligence and data analytics techniques are frequently used and developed for various industrial applications. The final purpose of the smart-factory problem is to automatically control the process recipe so that the manufacturer can obtain the desired standard or yield of the production processes. A datadriven model predictive control (D-MPC) strategy is widely used to quantitatively improve control systems. In this study, we introduce a novel modeling and control method termed stochastic response modeling (SRM). The method performs properly when a target variable has zero-inflated, intermittent, and time-invariant patterns. The proposed method comprises two main concepts: stochastic transformation of the response variable and coefficient adjustment algorithms that can address current limitations in the manufacturing field. Results of industrial case studies demonstrate the efficacy of SRM, especially in terms of the robustness and usability of model-based control. We believe that the proposed method can optimize the overall manufacturing process, such that high-yield production is always possible. stochastic representation autocorrelation beta regression sequential optimization Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 16 Apr, 2026 Reviewers invited by journal 13 Apr, 2026 Editor assigned by journal 07 Apr, 2026 First submitted to journal 05 Apr, 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9326628","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":622546957,"identity":"0e4cc6f2-2baa-4e8f-ba8b-11eb91bf27c4","order_by":0,"name":"Jaeman Kim","email":"","orcid":"","institution":"Tech University of Korea","correspondingAuthor":false,"prefix":"","firstName":"Jaeman","middleName":"","lastName":"Kim","suffix":""},{"id":622546958,"identity":"6e44c83c-2ea5-4308-9132-ff6058048515","order_by":1,"name":"Sang Ki Kim","email":"","orcid":"","institution":"Tech University of Korea","correspondingAuthor":false,"prefix":"","firstName":"Sang","middleName":"Ki","lastName":"Kim","suffix":""},{"id":622546959,"identity":"b877215b-1ae5-4778-a0af-ac4ad7a30bc3","order_by":2,"name":"Seung-Hwan Park","email":"","orcid":"","institution":"Chungnam National University","correspondingAuthor":false,"prefix":"","firstName":"Seung-Hwan","middleName":"","lastName":"Park","suffix":""},{"id":622546960,"identity":"459049a8-3976-44a0-b567-b120546fd1d3","order_by":3,"name":"Jihoon Kang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYBACxmYeMG0AYj/4UAGkmJkbiNbCbDjjDIhixK+FgQGhhU2atw1sDH4tzO28Bx983MFgzD+7/bIB77zaaP52oJYfFdvwOIwv2XDmGQYziTtnCh9IbjueO+MwYwNjz5nb+PxiBnKPDcONnGQDw23HchuAWpgZ24jQIn8jJ00icc6x3PnEajEzuJF+TOJgQ03uBiK0GBvObJMwNryRw2zYcOxA7kagloP4/GLYf8bwwcc2G8N5N9IfPv5TU5c77/zhgw9+VODR0gCmJICYBxQ3h8HcAzjVA4E8gsn+AEjU4VM8CkbBKBgFIxQAAL0aWicdlDCaAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-3784-5958","institution":"Tech University of Korea","correspondingAuthor":true,"prefix":"","firstName":"Jihoon","middleName":"","lastName":"Kang","suffix":""}],"badges":[],"createdAt":"2026-04-05 13:52:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9326628/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9326628/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107424345,"identity":"e6ed510f-f780-4c3e-9044-f49ee80433c5","added_by":"auto","created_at":"2026-04-21 10:57:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1432425,"visible":true,"origin":"","legend":"","description":"","filename":"SRTManufacturingProcessesManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9326628/v1_covered_28f6baf4-75fd-4362-8a3f-95690dd80aa0.pdf"}],"financialInterests":"","formattedTitle":"Stochastic Response Analysis for Multivariate Manufacturing Processes","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"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":"stochastic representation, autocorrelation, beta regression, sequential optimization","lastPublishedDoi":"10.21203/rs.3.rs-9326628/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9326628/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In modern manufacturing, artificial intelligence and data analytics techniques are frequently used and developed for various industrial applications. The final purpose of the smart-factory problem is to automatically control the process recipe so that the manufacturer can obtain the desired standard or yield of the production processes. A datadriven model predictive control (D-MPC) strategy is widely used to quantitatively improve control systems. In this study, we introduce a novel modeling and control method termed stochastic response modeling (SRM). The method performs properly when a target variable has zero-inflated, intermittent, and time-invariant patterns. The proposed method comprises two main concepts: stochastic transformation of the response variable and coefficient adjustment algorithms that can address current limitations in the manufacturing field. Results of industrial case studies demonstrate the efficacy of SRM, especially in terms of the robustness and usability of model-based control. We believe that the proposed method can optimize the overall manufacturing process, such that high-yield production is always possible.","manuscriptTitle":"Stochastic Response Analysis for Multivariate Manufacturing Processes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-21 10:55:49","doi":"10.21203/rs.3.rs-9326628/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-04-16T08:08:32+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-13T20:27:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-07T08:05:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"The International Journal of Advanced Manufacturing Technology","date":"2026-04-05T09:51:00+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":"f41dcb60-e90a-456b-8fc7-8fafe04a8639","owner":[],"postedDate":"April 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T10:55:49+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-21 10:55:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9326628","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9326628","identity":"rs-9326628","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}