Automating the Art of Jalebi Manufacturing: A Machine Learning-Assisted Inverse Kinematics Approach for Precision Path Planning and Scalable Production of a Traditional Indian Sweet

Automating the Art of Jalebi Manufacturing: A Machine Learning-Assisted Inverse Kinematics Approach for Precision Path Planning and Scalable Production of a Traditional Indian Sweet | 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 Automating the Art of Jalebi Manufacturing: A Machine Learning-Assisted Inverse Kinematics Approach for Precision Path Planning and Scalable Production of a Traditional Indian Sweet Mahesh A. Makwana, Samit Dutta, Kalpesh Parmar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8383903/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 Jalebi is a popular deep-fried Indian dessert made from fermented batter, typically consisting of refined flour and yogurt. The automation of jalebi making is essential to improve efficiency, consistency, and hygiene in large-scale production. Traditional manual preparation lacks hygiene, requires intensive labor, consumes time, and results in inconsistencies in shape, size, and texture. An automation in jalebi-making ensures uniform batter dispensing, precise frying, and controlled sugar syrup absorption, reducing human effort and production costs. Additionally, automation enhances food safety by minimizing direct human contact, making it an ideal solution for commercial kitchens, sweet shops, and industrial-scale food production. The automation of Jalebi production presents a significant challenge due to intricate motion patterns traditionally performed by skilled artisans. Conventional inverse kinematics approaches struggle to replicate these patterns due to the absence of a mechanistic motion model. This study proposes a novel machine learning-based path planning method for the automated production of Jalebi, ensuring consistent shape, quality, and texture while preserving traditional craftsmanship. Motion data was collected from skilled artisans to capture path patterns, size variations, and production speeds. A parametric equation was developed to represent the Jalebi formation process, enabling the formulation of an inverse kinematics solution for an RUU-parallel robot configuration. To the best of our knowledge, this is the first attempt at automating Jalebi making using an RUU-parallel manipulator, a low cost solution. A supervised learning model was trained on these motion patterns, achieving a mean squared error of 1.005 × 10−12 after 1000 iterations and an R-squared value of 1. The trained network was validated which shows the mean square error of 0.0988 and an optimized R-squared value 0.8742. The approach was validated through simulations, demonstrating accuracy, consistency, and adaptability across different Jalebi designs and production scales. This framework enables scalable, precise, and efficient Jalebi manufacturing, with potential applications in automating other intricate food production processes, such as Chakari and Imarti, thereby bridging cultural heritage with modern automation technologies. In addition, the conceptualized Automatic Jalebi Making Machine not only optimizes the automation process but also provides a cost-effective and scalable solution for similar food preparation industries. Inverse Kinematics Jalebi Path Planning Automation Parallel Manipulator Full Text Additional Declarations No competing interests reported. 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-8383903","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":561820922,"identity":"495958fa-702d-4fde-80de-7f5a823a6a9b","order_by":0,"name":"Mahesh A. 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Sweet","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"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},"keywords":"Inverse Kinematics, Jalebi, Path Planning, Automation, Parallel Manipulator","lastPublishedDoi":"10.21203/rs.3.rs-8383903/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8383903/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eJalebi is a popular deep-fried Indian dessert made from fermented batter, typically consisting of refined flour and yogurt. The automation of jalebi making is essential to improve efficiency, consistency, and hygiene in large-scale production. Traditional manual preparation lacks hygiene, requires intensive labor, consumes time, and results in inconsistencies in shape, size, and texture. An automation in jalebi-making ensures uniform batter dispensing, precise frying, and controlled sugar syrup absorption, reducing human effort and production costs. Additionally, automation enhances food safety by minimizing direct human contact, making it an ideal solution for commercial kitchens, sweet shops, and industrial-scale food production. The automation of Jalebi production presents a significant challenge due to intricate motion patterns traditionally performed by skilled artisans. Conventional inverse kinematics approaches struggle to replicate these patterns due to the absence of a mechanistic motion model. This study proposes a novel machine learning-based path planning method for the automated production of Jalebi, ensuring consistent shape, quality, and texture while preserving traditional craftsmanship. Motion data was collected from skilled artisans to capture path patterns, size variations, and production speeds. A parametric equation was developed to represent the Jalebi formation process, enabling the formulation of an inverse kinematics solution for an RUU-parallel robot configuration. To the best of our knowledge, this is the first attempt at automating Jalebi making using an RUU-parallel manipulator, a low cost solution. A supervised learning model was trained on these motion patterns, achieving a mean squared error of 1.005 × 10−12 after 1000 iterations and an R-squared value of 1. The trained network was validated which shows the mean square error of 0.0988 and an optimized R-squared value 0.8742. The approach was validated through simulations, demonstrating accuracy, consistency, and adaptability across different Jalebi designs and production scales. This framework enables scalable, precise, and efficient Jalebi manufacturing, with potential applications in automating other intricate food production processes, such as Chakari and Imarti, thereby bridging cultural heritage with modern automation technologies. In addition, the conceptualized Automatic Jalebi Making Machine not only optimizes the automation process but also provides a cost-effective and scalable solution for similar food preparation industries.\u003c/p\u003e","manuscriptTitle":"Automating the Art of Jalebi Manufacturing: A Machine Learning-Assisted Inverse Kinematics Approach for Precision Path Planning and Scalable Production of a Traditional Indian Sweet","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-18 05:29:58","doi":"10.21203/rs.3.rs-8383903/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":"7a8eb58b-3df5-4505-beef-5c01b85098a6","owner":[],"postedDate":"December 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-18T05:29:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-18 05:29:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8383903","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8383903","identity":"rs-8383903","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}