A method for temperature sensor selection and model selection for machine tool thermal error modelling using ANFIS and ANN

A method for temperature sensor selection and model selection for machine tool thermal error modelling using ANFIS and ANN | 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 A method for temperature sensor selection and model selection for machine tool thermal error modelling using ANFIS and ANN Nemwel Ariaga, Andrew Longstaff, Simon Fletcher This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4235130/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Oct, 2024 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 5 You are reading this latest preprint version Abstract Thermal errors account for a significant part of the dimensional errors of components produced by precision machine tools. These errors are commonly compensated using predictions from temperature-based empirical models. The accuracy and robustness of these predictions are affected by the locations of temperature sensors used to obtain the model’s input data. Methods for sensor selection found in literature are often difficult to replicate and automate because they require tuning of several hyperparameters. This work presents a sensor and model selection approach that uses Proper Orthogonal Decomposition (POD) and QR-pivoting to select a subset of sensors that have been preinstalled in the machine tool as possible model inputs. These sensors are then sorted according to their correlation with the thermally error being modelled. The final set of inputs and thermal error model structure is chosen using Bayesian Information Criterion (BIC) to limit model overfitting. The approach is tested on modelling of Y-axis thermal errors of machining data from different air-cutting heating cycles performed using different spindle speeds and feed rates using Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) models. The accuracy of these models was compared to that of models trained using inputs selected by conventional approaches: the least absolute shrinkage and selection operator (LASSO), fuzzy c-means clustering (FCM), and principal component regression (PCR). The presented approach had better or comparable results to the conventional approaches while using fewer inputs. The presented approach is also well suited for automation compared to conventional approaches that require expert input. CNC machine tool Thermal error model Sensor selection Adaptive Neuro-fuzzy Inference System (ANFIS) Artificial Neural Network (ANN) Full Text Cite Share Download PDF Status: Published Journal Publication published 03 Oct, 2024 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Major Revisions Needed 03 Jun, 2024 Reviewers agreed at journal 18 Apr, 2024 Reviewers invited by journal 18 Apr, 2024 Editor assigned by journal 12 Apr, 2024 First submitted to journal 10 Apr, 2024 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-4235130","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":292872031,"identity":"4f264cc5-7b65-46bb-9ded-9d6463b5f467","order_by":0,"name":"Nemwel Ariaga","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYLCCBCDmZ+9hYOABc9mI1CLZc4YULSBgcCOHSC3mDMzPPjyoqZVnuPn2mMQbBjt5Bom0BLxaLBvYjGckHDtu2Dg7L01yDkOyYYNE2gH87jnAYMyQwHaMsVk6x0yah4E5gUEivYGAFvbPDAn/jtm3SZ4BaaknRguPMUNiW01ijwQPSMthoBZCDjvMU8yQ2HcgeQZPjrHlHIPjhm08zxLwaznevpnxx7c62/3HzxjeeFNRLc/PnmaAVwsDM5g8DDOB+IisI1LdKBgFo2AUjEgAAL4HPlPh6hNEAAAAAElFTkSuQmCC","orcid":"","institution":"University of Huddersfield","correspondingAuthor":true,"prefix":"","firstName":"Nemwel","middleName":"","lastName":"Ariaga","suffix":""},{"id":292872038,"identity":"0ced66a4-9e09-40dc-a5db-b6640d662e8d","order_by":1,"name":"Andrew Longstaff","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Longstaff","suffix":""},{"id":292872039,"identity":"ece03ada-8399-4222-a281-4e9b93be4038","order_by":2,"name":"Simon Fletcher","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Simon","middleName":"","lastName":"Fletcher","suffix":""}],"badges":[],"createdAt":"2024-04-08 08:44:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4235130/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4235130/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00170-024-14443-5","type":"published","date":"2024-10-03T15:57:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66096732,"identity":"9043e273-018f-4196-9d04-037fe0a3c7a9","added_by":"auto","created_at":"2024-10-07 16:08:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2383782,"visible":true,"origin":"","legend":"","description":"","filename":"SensorselectionjournalRevisedUpdated.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4235130/v1_covered_bbdf9cef-7291-42e9-990a-b24e9db4e68e.pdf"}],"financialInterests":"","formattedTitle":"A method for temperature sensor selection and model selection for machine tool thermal error modelling using ANFIS and ANN","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":true,"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":"CNC machine tool, Thermal error model, Sensor selection, Adaptive Neuro-fuzzy Inference System (ANFIS), Artificial Neural Network (ANN)","lastPublishedDoi":"10.21203/rs.3.rs-4235130/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4235130/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThermal errors account for a significant part of the dimensional errors of components produced by precision machine tools. 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