Optimization of the robot positioner performance in a CNC robot manufacturing cell

Optimization of the robot positioner performance in a CNC robot manufacturing cell | 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 Optimization of the robot positioner performance in a CNC robot manufacturing cell Saravanan Arunachalam This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8269445/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In this study, the operational performance of the CNC robot manufacturing cell is optimized using an artificial neural network integrated with a genetic algorithm to determine the optimal process parameters. The CNC robot manufacturing cell consists of three HAAS-made CNC machines, a KUKA robot positioner, and a KUKA robot. The robot was placed on a robot positioner that provides an additional seventh axis for the robot. Real-world industrial data was collected from the physical workstation through Gembawalk and replicated on the Visual components - commercial factory simulation software. Design of experiments was used to determine the significant factors and their levels that affect the performance of the robot positioning system. The identified factors were Speed, payload, Path precision, and number of picks, each with three levels. A L27 orthogonal array was developed with two responses, and the gathered data were used to train a feed-forward back propagation network with appropriate learning parameters. The best network model was selected as the fitness function for the genetic algorithm optimization problem, providing optimal trade-offs to minimize cycle time and energy consumption. The optimization results reveal that the optimal parameter set significantly improves overall throughput and operating efficiency relative to baseline settings. These results collectively highlight the ability of metaheuristic optimization strategies to address nonlinear multi-parameter interactions inherent in robotic manufacturing systems. Based on the ANN modeling integrated with GA optimization of the KUKA robot positioner, the following recommendations are provided for the efficient operation of the CNC robot manufacturing cell, utilizing a KUKA robot. Robot positioner Robot linear floor track Design of Experiments Artificial neural network Genetic algorithm and Industrial robotics. Full Text Cite Share Download PDF Status: Posted Version 1 posted 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-8269445","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":558153257,"identity":"e5853e8b-7128-4708-8ae5-17c1ba3cdbce","order_by":0,"name":"Saravanan 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algorithm and Industrial robotics.","lastPublishedDoi":"10.21203/rs.3.rs-8269445/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8269445/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this study, the operational performance of the CNC robot manufacturing cell is optimized using an artificial neural network integrated with a genetic algorithm to determine the optimal process parameters. The CNC robot manufacturing cell consists of three HAAS-made CNC machines, a KUKA robot positioner, and a KUKA robot. The robot was placed on a robot positioner that provides an additional seventh axis for the robot. Real-world industrial data was collected from the physical workstation through Gembawalk and replicated on the Visual components - commercial factory simulation software. Design of experiments was used to determine the significant factors and their levels that affect the performance of the robot positioning system. The identified factors were Speed, payload, Path precision, and number of picks, each with three levels. A L27 orthogonal array was developed with two responses, and the gathered data were used to train a feed-forward back propagation network with appropriate learning parameters. The best network model was selected as the fitness function for the genetic algorithm optimization problem, providing optimal trade-offs to minimize cycle time and energy consumption. The optimization results reveal that the optimal parameter set significantly improves overall throughput and operating efficiency relative to baseline settings. These results collectively highlight the ability of metaheuristic optimization strategies to address nonlinear multi-parameter interactions inherent in robotic manufacturing systems. Based on the ANN modeling integrated with GA optimization of the KUKA robot positioner, the following recommendations are provided for the efficient operation of the CNC robot manufacturing cell, utilizing a KUKA robot.\u003c/p\u003e","manuscriptTitle":"Optimization of the robot positioner performance in a CNC robot manufacturing cell","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-15 09:34:18","doi":"10.21203/rs.3.rs-8269445/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6d3d1482-3a20-47ef-a62a-6435eaea0fa0","owner":[],"postedDate":"December 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T08:12:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-15 09:34:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8269445","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8269445","identity":"rs-8269445","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}