Optimization of Surface Roughness for Titanium Alloy Based on Multi- strategy Fusion Snake Algorithm

Optimization of Surface Roughness for Titanium Alloy Based on Multi- strategy Fusion Snake Algorithm | 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 Article Optimization of Surface Roughness for Titanium Alloy Based on Multi- strategy Fusion Snake Algorithm Guochao Zhao, Nanqi Q. Li, Yang Zhao, Hui Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3815092/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 Titanium alloy has the characteristics of low thermal conductivity, small elastic modulus, and serious work hardening, which makes it difficult to predict the surface quality after high-speed milling. Surface quality is known to significantly impact the wear resistance, fatigue strength, and corrosion resistance of parts. To improve the service performance of titanium alloy parts, it is of great significance to optimize the milling parameters based on the improvement of surface quality. Therefore, this paper proposes a milling parameter optimization method based on the snake algorithm based on multi-strategy fusion. The surface roughness was used as the optimization goal to optimize the parameters. Firstly, the response surface method was used to establish a prediction model of titanium alloy milling surface roughness to realize the prediction of surface roughness and make it continuous. Then, the snake algorithm with multi-strategy fusion was proposed, which initialized the population based on the orthogonal matrix initialization strategy, so that the population individuals were more evenly distributed in space, increased the diversity of the population, improved the model of food quantity and temperature in the algorithm, optimized the change mechanism of food quantity and temperature in the original algorithm into a dynamic adaptive mechanism, accelerated the convergence speed, used the joint reverse strategy to select and generate individuals with higher fitness, and strengthened the ability of the algorithm to escape the local optimal solution. Experimental results on five benchmarks with multiple comparative optimization algorithms show that the MSSO algorithm has faster convergence speed and higher convergence accuracy. Finally, the multi-strategy snake algorithm was used to optimize the optimization objective equation, and the milling parameter experiment shows that the surface roughness of Ti64 is increased by 55.7 percent compared with that before optimization, and the surface roughness of the specimen optimized by the multi-strategy fusion snake algorithm is significantly reduced, the surface toolpath row spacing is reduced, and the average height of the texture is reduced. This method can reduce the optimization time and ensure better optimization results than the classical optimization algorithm. Physical sciences/Engineering/Aerospace engineering Physical sciences/Engineering/Mechanical engineering Ti64 Response surface method Snake algorithm Orthogonal matrix initialization Dynamic adaptive parameters 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-3815092","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":265698829,"identity":"981d0625-a392-4c50-8fd3-597241026acb","order_by":0,"name":"Guochao Zhao","email":"","orcid":"","institution":"Liaoning Technical University","correspondingAuthor":false,"prefix":"","firstName":"Guochao","middleName":"","lastName":"Zhao","suffix":""},{"id":265698830,"identity":"82ed46c3-9c34-433f-ad92-8678ca5abb5f","order_by":1,"name":"Nanqi Q. Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYBACPmYwJSHHz9588EFCRQ1hLWxQLcaSPceSDR6cOUaEFiiduOFGjpnkwxZmIrSw8x78XFBmwbjhQI5ZRWIDGwN/e3cCAYfxJUvPOCfBLHngWNmNxB0yDBJnzm4goIXHQJq3TYKN72DzthuJZ9gYDCRyCWox/g3UwsNwmMGsILGNmSgtZiBbJASOsZgxEK3FmuechIFkD1uyRMKZYzwE/cLPf8b4Nk9ZXX2//OODH39U1Mjxt/fi1wK1C8HkIUI5mpZRMApGwSgYBRgAAC/zP8Nkke0CAAAAAElFTkSuQmCC","orcid":"","institution":"Liaoning Technical University","correspondingAuthor":true,"prefix":"","firstName":"Nanqi","middleName":"Q.","lastName":"Li","suffix":""},{"id":265698831,"identity":"a1ad3eda-cd47-4c62-953c-b91127d8f5bf","order_by":2,"name":"Yang Zhao","email":"","orcid":"","institution":"Liaoning Technical University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Zhao","suffix":""},{"id":265698832,"identity":"0aca19bc-2942-4769-9502-4bc13cbe4df7","order_by":3,"name":"Hui Wang","email":"","orcid":"","institution":"Liaoning Technical University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2023-12-28 06:14:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3815092/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3815092/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52952879,"identity":"f143a9c6-9afc-4646-ad6e-ba601ca10a11","added_by":"auto","created_at":"2024-03-19 04:30:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":814944,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3815092/v1_covered_5af73db5-1994-45fa-9ccc-af375b81fcc9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimization of Surface Roughness for Titanium Alloy Based on Multi- strategy Fusion Snake Algorithm","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Ti64, Response surface method, Snake algorithm, Orthogonal matrix initialization, Dynamic adaptive parameters","lastPublishedDoi":"10.21203/rs.3.rs-3815092/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3815092/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTitanium alloy has the characteristics of low thermal conductivity, small elastic modulus, and serious work hardening, which makes it difficult to predict the surface quality after high-speed milling. Surface quality is known to significantly impact the wear resistance, fatigue strength, and corrosion resistance of parts. To improve the service performance of titanium alloy parts, it is of great significance to optimize the milling parameters based on the improvement of surface quality. Therefore, this paper proposes a milling parameter optimization method based on the snake algorithm based on multi-strategy fusion. The surface roughness was used as the optimization goal to optimize the parameters. Firstly, the response surface method was used to establish a prediction model of titanium alloy milling surface roughness to realize the prediction of surface roughness and make it continuous. Then, the snake algorithm with multi-strategy fusion was proposed, which initialized the population based on the orthogonal matrix initialization strategy, so that the population individuals were more evenly distributed in space, increased the diversity of the population, improved the model of food quantity and temperature in the algorithm, optimized the change mechanism of food quantity and temperature in the original algorithm into a dynamic adaptive mechanism, accelerated the convergence speed, used the joint reverse strategy to select and generate individuals with higher fitness, and strengthened the ability of the algorithm to escape the local optimal solution. Experimental results on five benchmarks with multiple comparative optimization algorithms show that the MSSO algorithm has faster convergence speed and higher convergence accuracy. Finally, the multi-strategy snake algorithm was used to optimize the optimization objective equation, and the milling parameter experiment shows that the surface roughness of Ti64 is increased by 55.7 percent compared with that before optimization, and the surface roughness of the specimen optimized by the multi-strategy fusion snake algorithm is significantly reduced, the surface toolpath row spacing is reduced, and the average height of the texture is reduced. This method can reduce the optimization time and ensure better optimization results than the classical optimization algorithm.\u003c/p\u003e","manuscriptTitle":"Optimization of Surface Roughness for Titanium Alloy Based on Multi- strategy Fusion Snake Algorithm","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-08 18:11:57","doi":"10.21203/rs.3.rs-3815092/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":"4dbd542f-b20f-4698-ad50-97e6d9616836","owner":[],"postedDate":"January 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":27993387,"name":"Physical sciences/Engineering/Aerospace engineering"},{"id":27993388,"name":"Physical sciences/Engineering/Mechanical engineering"}],"tags":[],"updatedAt":"2024-03-19T04:29:58+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-08 18:11:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3815092","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3815092","identity":"rs-3815092","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}