Multi optimization of laser drilling of GFRP composites via TOPSIS approach

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Abstract Composite materials are very promising for use in a variety of applications. However, due to their anisotropy, they are challenging to cut using traditional machining. A desirable option for cutting composites is laser beam machining (LBM). In the present study, laser beam drilling (LBD) experiments with a CO2 laser on glass fibre reinforced polyester (GFRP) composites were conducted based on Taguchi’s L9 orthogonal array to decide on a parametric optimization of multiple responses, such as hole taper angle (θ), heat affected zone (HAZ), and material removal rate (MRR), using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach. The input process parameters are cutting speed (S), laser power (P), and diameter of laser drilled hole (D). Based on closeness coefficient (Si*) of TOPSIS, the optimum levels of parameters were identified and analysis of variance (ANOVA) was used to quantitatively assess the influence of the input parameters on the output responses. The results demonstrated that all the selected cutting parameters have a significant effect on all the measured responses. From the confirmation experiment carried out at the optimum LBD conditions, there has been an improvement of Si* by 12%.
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Multi optimization of laser drilling of GFRP composites via TOPSIS approach | 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 Multi optimization of laser drilling of GFRP composites via TOPSIS approach R. A. Elsad, Ahmed Bahei El-Deen Mahrous This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4774848/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 Composite materials are very promising for use in a variety of applications. However, due to their anisotropy, they are challenging to cut using traditional machining. A desirable option for cutting composites is laser beam machining (LBM). In the present study, laser beam drilling (LBD) experiments with a CO 2 laser on glass fibre reinforced polyester (GFRP) composites were conducted based on Taguchi’s L9 orthogonal array to decide on a parametric optimization of multiple responses, such as hole taper angle (θ), heat affected zone (HAZ), and material removal rate (MRR), using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach. The input process parameters are cutting speed (S), laser power (P), and diameter of laser drilled hole (D). Based on closeness coefficient (S i *) of TOPSIS, the optimum levels of parameters were identified and analysis of variance (ANOVA) was used to quantitatively assess the influence of the input parameters on the output responses. The results demonstrated that all the selected cutting parameters have a significant effect on all the measured responses. From the confirmation experiment carried out at the optimum LBD conditions, there has been an improvement of S i * by 12%. Composites Laser drilling HAZ Hole taper angle TOPSIS and ANOVA 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-4774848","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":343150939,"identity":"fce58403-0d88-4429-b6b3-e26101de62c0","order_by":0,"name":"R. A. 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