Comparison of RSM, ANN, and ANFIS in predicting the weld strength of Laser transmission welds of oak wood powder-reinforced polypropylene absorbent part.

Comparison of RSM, ANN, and ANFIS in predicting the weld strength of Laser transmission welds of oak wood powder-reinforced polypropylene absorbent part. | 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 Comparison of RSM, ANN, and ANFIS in predicting the weld strength of Laser transmission welds of oak wood powder-reinforced polypropylene absorbent part. Munyaradzi Kapuyanyika, Albert Uchenna Ude, Vivekanandhan Chinnasamy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3948196/v2 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Dec, 2024 Read the published version in NED University Journal of Research → Version 2 posted You are reading this latest preprint version Show more versions Abstract This study examines the feasibility of Laser transmission welding(LTW) of a 100% homo-polypropylene transparent part joined to an absorbent part composed of 15% wt white oak wood fiber reinforced homo-polypropylene doped with 0.2% carbon black in lap-joint configuration. Focus is given to investigating the influence of process parameters, namely laser power, welding speed, stand-off distance, and clamp pressure on the laser welded joints of polypropylene joined to plant-based polypropylene composite and the feasibility of weld strength prediction using Response Surface Method (RSM), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). Results showed that stand-off distance was the most crucial factor affecting weld strength, followed by welding speed. Laser power and clamp pressure had insignificant effects on weld strength in the design space studied in this paper. The coefficient of determination (R2) was (0.90), (0.93), and (0.99) for the RSM model, the ANN model, and the ANFIS model, respectively. All the prediction models exhibited acceptable mean absolute error percentages and root-mean-square errors. The results suggested a satisfactory performance in predicting weld strength for the specified materials in this study's specified parameter design space. The ANFIS model showed the best predictions, followed by the ANN and RSM models. Mechanical Engineering Materials Engineering Laser transmission welding Response Surface method Artificial Neural Network Adaptive Neuro-Fuzzy Inference System Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Published Journal Publication published 25 Dec, 2024 Read the published version in NED University Journal of Research → Version 2 posted You are reading this latest preprint version Show more versions 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. 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