Optimization of lapping process parameters of CP-Ti based on PSO with mutation and BPNN

Optimization of lapping process parameters of CP-Ti based on PSO with mutation and BPNN | 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 lapping process parameters of CP-Ti based on PSO with mutation and BPNN Kaiqiang Ye, Jianbin Wang, Hong Gao, Liu Yang, Ping Xiao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-364458/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Aug, 2021 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 4 You are reading this latest preprint version Abstract This work aims to improve the surface quality of commercially pure titanium (CP-Ti) with free alumina lapping fluid and establish the relationship between the main process parameters of lapping and roughness. On this basis, the optimal process parameters were searched by performing particle swarm optimization with mutation. First, free alumina lapping fluid was used to perform an L 9 (3 3 ) orthogonal experiment on CP-Ti to acquire data samples to train the neural network. At the same time, a BP neural network was created to fit the nonlinear functional relation among the lapping pressure P , spindle speed n , slurry flow Q and roughness Ra . Then, the range of the node numbers in the hidden layer of the neural network was determined by empirical formulas and the Kolmogorov theorem. On this basis, particle swarm optimization with mutation was used to search for the optimal process parameter configurations for lapping CP-Ti. The optimal process parameter configurations were used in the neural network to calculate the prediction value. Finally, the accuracy of the prediction was verified experimentally. The optimum process parameter configurations found by particle swarm optimization were as follows: the lapping pressure was 5 kPa, spindle speed was 60 r·min − 1 and slurry flow was 50 ml·min − 1 . Then, the configurations were applied to a neural network to simulate prediction: the roughness was 0.1127 µm. The roughness obtained by experiments was 0.1134 µm. The error was 0.62%, which indicates that the well-trained neural network can achieve a good prediction when experimental data are missing. Applying the particle swarm optimization (PSO) algorithm with mutation to a neural network will obtain the optimal process parameter configurations, which can effectively improve the surface quality of CP-Ti lapped with free abrasive. Mechanical Engineering Systems and Networking Orthopedics Particle swarm optimization(PSO) BP neural network(BPNN) Parameter optimization Commercial pure titanium(CP-Ti) Free abrasive lapping K-fold cross validation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Full Text Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF. Supplementary Files MATLABCODE.rtf Ra1.tif Ra2.tif Ra3.tif Ra4.tif Ra5.tif Cite Share Download PDF Status: Published Journal Publication published 19 Aug, 2021 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Reviewers invited by journal 25 Mar, 2021 Reviews received at journal 25 Mar, 2021 Editor assigned by journal 24 Mar, 2021 First submitted to journal 24 Mar, 2021 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-364458","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":18744602,"identity":"38509913-df34-4bc5-b6d1-18e8c20c4a3f","order_by":0,"name":"Kaiqiang 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However, the manuscript can be downloaded and accessed as a PDF.\u003c/p\u003e"}],"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":false,"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":"Particle swarm optimization(PSO), BP neural network(BPNN), Parameter optimization, Commercial pure titanium(CP-Ti), Free abrasive lapping, K-fold cross validation ","lastPublishedDoi":"10.21203/rs.3.rs-364458/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-364458/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis work aims to improve the surface quality of commercially pure titanium (CP-Ti) with free alumina lapping fluid and establish the relationship between the main process parameters of lapping and roughness. On this basis, the optimal process parameters were searched by performing particle swarm optimization with mutation. First, free alumina lapping fluid was used to perform an \u003cem\u003eL\u003c/em\u003e\u003csub\u003e9\u003c/sub\u003e(3\u003csup\u003e3\u003c/sup\u003e) orthogonal experiment on CP-Ti to acquire data samples to train the neural network. At the same time, a BP neural network was created to fit the nonlinear functional relation among the lapping pressure \u003cem\u003eP\u003c/em\u003e, spindle speed \u003cem\u003en\u003c/em\u003e, slurry flow \u003cem\u003eQ\u003c/em\u003e and roughness \u003cem\u003eRa\u003c/em\u003e. Then, the range of the node numbers in the hidden layer of the neural network was determined by empirical formulas and the Kolmogorov theorem. On this basis, particle swarm optimization with mutation was used to search for the optimal process parameter configurations for lapping CP-Ti. The optimal process parameter configurations were used in the neural network to calculate the prediction value. Finally, the accuracy of the prediction was verified experimentally. The optimum process parameter configurations found by particle swarm optimization were as follows: the lapping pressure was 5 kPa, spindle speed was 60 r\u0026middot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and slurry flow was 50 ml\u0026middot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Then, the configurations were applied to a neural network to simulate prediction: the roughness was 0.1127 \u0026micro;m. The roughness obtained by experiments was 0.1134 \u0026micro;m. The error was 0.62%, which indicates that the well-trained neural network can achieve a good prediction when experimental data are missing. Applying the particle swarm optimization (PSO) algorithm with mutation to a neural network will obtain the optimal process parameter configurations, which can effectively improve the surface quality of CP-Ti lapped with free abrasive.\u003c/p\u003e","manuscriptTitle":"Optimization of lapping process parameters of CP-Ti based on PSO with mutation and BPNN","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-03-30 22:45:43","doi":"10.21203/rs.3.rs-364458/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2021-03-26T00:00:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-03-26T00:00:00+00:00","index":0,"fulltext":""},{"type":"editorAssigned","content":"","date":"2021-03-25T00:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"The International Journal of Advanced Manufacturing Technology","date":"2021-03-24T21:37:23+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":"a556659c-8ab5-4e3e-996f-6af289429139","owner":[],"postedDate":"March 30th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":3307329,"name":"Mechanical Engineering"},{"id":3307330,"name":"Systems and Networking"},{"id":3307331,"name":"Orthopedics"}],"tags":[],"updatedAt":"2021-08-29T15:10:15+00:00","versionOfRecord":{"articleIdentity":"rs-364458","link":"https://doi.org/10.1007/s00170-021-07862-1","journal":{"identity":"the-international-journal-of-advanced-manufacturing-technology","isVorOnly":false,"title":"The International Journal of Advanced Manufacturing Technology"},"publishedOn":"2021-08-19 15:03:22","publishedOnDateReadable":"August 19th, 2021"},"versionCreatedAt":"2021-03-30 22:45:43","video":"","vorDoi":"10.1007/s00170-021-07862-1","vorDoiUrl":"https://doi.org/10.1007/s00170-021-07862-1","workflowStages":[]},"version":"v1","identity":"rs-364458","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-364458","identity":"rs-364458","version":["v1"]},"buildId":"2u56kwukJI3zHK-uzyFNs","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}