Optimization of Enhanced TIG Welding Process Using Artificial Neural Network and Heuristic Algorithms

Optimization of Enhanced TIG Welding Process Using Artificial Neural Network and Heuristic Algorithms | 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 Original Article Optimization of Enhanced TIG Welding Process Using Artificial Neural Network and Heuristic Algorithms Masoud Azadi Moghaddam, Farhad Kolahan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-680478/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 Using conventional gas tungsten arc welding (C-GTAW) process includes some demerits, shallow penetration has been considered as the most important ones. Recently, in order to cope with the mentioned disadvantage (low penetration), using a paste like coating of activating flux during welding process known as activated GTAW (AGTAW) has been proposed. In this paper, effect of A-GTAW process input adjusting parameters including welding speed (S), welding current (C) and percentage of activating fluxes (TiO2 and SiO2) combination (F) on weld bead width (WBW), depth of penetration (DOP), and consequently aspect ratio (ASR) (the most important quality characteristics) in welding of AISI316L parts have been studied. Box-behnken design (BBD) of experiments has been used to prepare the required experimental matrix for modeling and optimization objectives. Back propagation neural network (BPNN), architecture (hidden layers number and their corresponding neurons/nodes) of which has been determined using heuristic algorithm employed to model the process outputs, the most fitted ones have been optimized using simulated annealing (SA), and particle swarm optimization (PSO) algorithms in order to obtain the desired aspect ratio, maximum depth of penetration, and minimum weld bead width. Finally, confirmation experimental tests have been carried out to evaluate the performance of the proposed method. Due to the obtained results, the suggested method for modeling and optimization of A-GTAW process is quite efficient (with less than 4% error). Mechanical Engineering Activated gas tungsten arc welding (A-GTAW) Box-behnken design (BBD) back propagation neural network (BPNN) and heuristic algorithms 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-680478","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Original Article","associatedPublications":[],"authors":[{"id":37517556,"identity":"2f3d467c-5e3a-453e-bb6e-2a681fcccd34","order_by":0,"name":"Masoud Azadi Moghaddam","email":"","orcid":"","institution":"Ferdowsi University of Mashhad","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Masoud","middleName":"Azadi","lastName":"Moghaddam","suffix":""},{"id":37517557,"identity":"2e70d217-8ad3-4717-bf85-bdf9cff087c8","order_by":1,"name":"Farhad Kolahan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYBACCQbmBmYgLQfnE6GFEazFmHQtiQ1EO0yyvbHxc0HNnfQN1w4wfvjBYJFPUIs0z8Fm6RnHnuVuuJ3ALNnDIGFJ0Do5icQGaR62wyAtDNJAhxoQtAWopfk3z7/D6QZAW34TpUVaIrFNmrftcAJQCxtxtkj2HGyz5u07bDjzdmKbZY8BEVokjjcfvs3z7bA83+3kwzd+VNQR1oIEGBsYGEjSMApGwSgYBaMAJwAA8y820eogtesAAAAASUVORK5CYII=","orcid":"","institution":"Ferdowsi University of Mashhad","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Farhad","middleName":"","lastName":"Kolahan","suffix":""}],"badges":[],"createdAt":"2021-07-03 03:53:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-680478/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-680478/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":13656346,"identity":"f395c238-eeb4-4aed-b9ce-61735680e2f2","added_by":"auto","created_at":"2021-09-17 10:05:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":795770,"visible":true,"origin":"","legend":"","description":"","filename":"mainfullpaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-680478/v1_covered.pdf"},{"id":11260842,"identity":"bb47c4fe-40d5-4d86-aa7e-931819bab3b6","added_by":"auto","created_at":"2021-07-08 15:11:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":792222,"visible":true,"origin":"","legend":"","description":"","filename":"mainfullpaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-680478/v1_covered.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eOptimization of Enhanced TIG Welding Process Using Artificial Neural Network and Heuristic Algorithms\u003c/p\u003e","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-680478/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"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":false,"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":"Activated gas tungsten arc welding (A-GTAW), Box-behnken design (BBD), back propagation neural network (BPNN), and heuristic algorithms","lastPublishedDoi":"10.21203/rs.3.rs-680478/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-680478/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Using conventional gas tungsten arc welding (C-GTAW) process includes some demerits, shallow penetration has been considered as the most important ones. Recently, in order to cope with the mentioned disadvantage (low penetration), using a paste like coating of activating flux during welding process known as activated GTAW (AGTAW) has been proposed. In this paper, effect of A-GTAW process input adjusting parameters including welding speed (S), welding current (C) and percentage of activating fluxes (TiO2 and SiO2) combination (F) on weld bead width (WBW), depth of penetration (DOP), and consequently aspect ratio (ASR) (the most important quality characteristics) in welding of AISI316L parts have been studied. Box-behnken design (BBD) of experiments has been used to prepare the required experimental matrix for modeling and optimization objectives. 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