ANN and Machine Learning based predictions of MRR in AWSJ Machining of CFRP composites

ANN and Machine Learning based predictions of MRR in AWSJ Machining of CFRP composites | 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 ANN and Machine Learning based predictions of MRR in AWSJ Machining of CFRP composites K Ramesha, N Santhosh, B A Praveena, C Manjunath, Banakara Nagaraj, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4264339/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 The study assesses the efficacy of Abrasive Water Suspension Jet (AWSJ) Machining, a non-conventional technique based on erosion principles, with a specific emphasis on its use in machining carbon fiber-reinforced plastics (CFRP) composites. The analysis examines critical process variables, including Speed, Feed, and Standoff distance, to evaluate their influence on Material Removal Rate (MRR), during underwater cutting operations. The results unambiguously support the superiority of underwater cutting. Expanding the diameter of the jet in underwater cutting improves both the width of the cut and the roughness of the surface. This also helps reduce vibrations in the nozzle when operating at high pressures, resulting in a smaller cut and a smoother surface. This highlights the effectiveness of underwater cutting in generating accurate machining results. In addition, the study utilizes machine learning (ML) models such as Random Forest and XGBoost to enhance the optimization of MRR, a crucial parameter in composite machining. The results demonstrate exceptional performance across all models, with XGBoost exhibiting outstanding accuracy and efficiency on both the training and test datasets. The comparative analysis reveals the competitive performance of Random Forest XGBoost and Artificial Neural Network (ANN) in optimizing MRR. These models achieve notable accuracy scores in both training and test sets, surpassing the regular statistical methods such as the Response Surface Methodology (RSM). ANN ML Models XGBoost Random Forest RSM AWSJ CFRP Composites 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-4264339","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":308189596,"identity":"0eb7618d-0940-4bbf-a99e-efe7c42477f8","order_by":0,"name":"K Ramesha","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"K","middleName":"","lastName":"Ramesha","suffix":""},{"id":308189597,"identity":"e657a419-3bf6-4ac8-bdb7-097d28a8ec43","order_by":1,"name":"N Santhosh","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"N","middleName":"","lastName":"Santhosh","suffix":""},{"id":308189598,"identity":"05072c94-2c5e-48c6-a5ed-ca9ec3eecdfd","order_by":2,"name":"B A Praveena","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"B","middleName":"A","lastName":"Praveena","suffix":""},{"id":308189599,"identity":"89e176f0-4f93-4b34-93d6-07f49cf3f8a7","order_by":3,"name":"C Manjunath","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"C","middleName":"","lastName":"Manjunath","suffix":""},{"id":308189600,"identity":"d449c7af-7e58-43d4-bdbf-529451a0c3e9","order_by":4,"name":"Banakara Nagaraj","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Banakara","middleName":"","lastName":"Nagaraj","suffix":""},{"id":308189601,"identity":"353fbcb3-12a9-4fad-9402-6221b8914306","order_by":5,"name":"MGM Johar","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"MGM","middleName":"","lastName":"Johar","suffix":""},{"id":308189602,"identity":"c1b69cab-3833-4af9-b879-52e825e028a6","order_by":6,"name":"Adam Amril Jaharadak","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Adam","middleName":"Amril","lastName":"Jaharadak","suffix":""},{"id":308189603,"identity":"2946f273-7e02-439d-9e67-5cf29232517d","order_by":7,"name":"Ali A. Rajhi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"A.","lastName":"Rajhi","suffix":""},{"id":308189604,"identity":"663b771c-18ad-4e19-9cfc-9020feadd683","order_by":8,"name":"Sagr Alamri","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sagr","middleName":"","lastName":"Alamri","suffix":""},{"id":308189605,"identity":"bc41e02a-9b59-4162-ade2-f50c45a6e438","order_by":9,"name":"Alaauldeen A. Duhduh","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Alaauldeen","middleName":"A.","lastName":"Duhduh","suffix":""},{"id":308189606,"identity":"24e9c5d3-6474-4eb4-8663-2b831f33f7fb","order_by":10,"name":"CHANNA KESHAVA NAIK N","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIie3RsQrCMBCA4QsHcYl2Var2FQoZRAR9FnHo4iJO4qAQqIvg7IsERyWgi+AquBh9AgdBNxNFx7ZugvmnHuSjFwLgcv1kRAAZIwACOZqRFb4giKElNMNv8EUAadGOqaSWU4L3Fg0IJt56cO02yxRQn/YJpD5ti858G0GokB4qsmMWo5x3E0i4bAuVjxWEaEhJoiGM+olkp18kEEj7JTnKQPZmMUvALEYuUmUhWvB5HDFzF+4TuWEU0+6yi85+L25Ug9lKX+5y2PJyQp+TiM0+Cnt+sPeY1ucMuWU47XK5XP/XA8QsQASWscLaAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-6253-8573","institution":"BGS College of Engineering and Technology , Bangalore","correspondingAuthor":true,"prefix":"","firstName":"CHANNA","middleName":"KESHAVA NAIK","lastName":"N","suffix":""}],"badges":[],"createdAt":"2024-04-14 09:18:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4264339/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4264339/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61519728,"identity":"c6e6dd02-781a-4873-adad-097b3ac209aa","added_by":"auto","created_at":"2024-07-31 17:00:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1219048,"visible":true,"origin":"","legend":"","description":"","filename":"FinalManuscriptIJAMT.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4264339/v1_covered_3c960390-e18d-42f6-99b6-a72d8bd54a62.pdf"}],"financialInterests":"","formattedTitle":"ANN and Machine Learning based predictions of MRR in AWSJ Machining of CFRP composites","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":"ANN, ML Models, XGBoost, Random Forest, RSM, AWSJ, CFRP Composites","lastPublishedDoi":"10.21203/rs.3.rs-4264339/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4264339/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study assesses the efficacy of Abrasive Water Suspension Jet (AWSJ) Machining, a non-conventional technique based on erosion principles, with a specific emphasis on its use in machining carbon fiber-reinforced plastics (CFRP) composites. The analysis examines critical process variables, including Speed, Feed, and Standoff distance, to evaluate their influence on Material Removal Rate (MRR), during underwater cutting operations. The results unambiguously support the superiority of underwater cutting. Expanding the diameter of the jet in underwater cutting improves both the width of the cut and the roughness of the surface. This also helps reduce vibrations in the nozzle when operating at high pressures, resulting in a smaller cut and a smoother surface. This highlights the effectiveness of underwater cutting in generating accurate machining results. In addition, the study utilizes machine learning (ML) models such as Random Forest and XGBoost to enhance the optimization of MRR, a crucial parameter in composite machining. The results demonstrate exceptional performance across all models, with XGBoost exhibiting outstanding accuracy and efficiency on both the training and test datasets. The comparative analysis reveals the competitive performance of Random Forest XGBoost and Artificial Neural Network (ANN) in optimizing MRR. These models achieve notable accuracy scores in both training and test sets, surpassing the regular statistical methods such as the Response Surface Methodology (RSM).\u003c/p\u003e","manuscriptTitle":"ANN and Machine Learning based predictions of MRR in AWSJ Machining of CFRP composites","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-11 10:57:00","doi":"10.21203/rs.3.rs-4264339/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":"5a08ea8b-1146-4ebc-8273-aadb8cb84ffa","owner":[],"postedDate":"June 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-31T16:52:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-11 10:57:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4264339","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4264339","identity":"rs-4264339","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}