Neural Network-Based Prediction and Optimization of Air Bubble Defects in Glass FiberReinforced Nylon 6 Injection Molding

Neural Network-Based Prediction and Optimization of Air Bubble Defects in Glass FiberReinforced Nylon 6 Injection Molding | 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 Neural Network-Based Prediction and Optimization of Air Bubble Defects in Glass FiberReinforced Nylon 6 Injection Molding Pratap Ramesh Sonawane, Neeraj B. Dole This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7271545/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Injection molding is a widely used manufacturing process, but defects such as air bubbles significantly impact product quality and mechanical properties. Traditional optimization techniques rely on trial-and-error methods, which are time-consuming and inefficient. This study presents a neural network-based approach for predicting and optimizing air bubble defects in glass fiber-reinforced Nylon 6 injection molding. A dataset of key process parameters—including nozzle temperature, barrel temperature, injection speed, pressure, cycle time, and cushion size— was collected through a Design of Experiments (DOE) approach. A feed forward neural network was trained to predict defect size based on these parameters, achieving an R-squared accuracy of 0.92. To further refine process conditions, an optimization algorithm was applied to minimize air bubble formation. The optimized parameters resulted in an 80% reduction in defect size, demonstrating the effectiveness of neural network-based process control. This approach provides a data-driven framework for improving product quality and reducing waste in injection molding. Process Optimization Neural Networks Manufacturing Quality Control DOE Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 21 Aug, 2025 Reviewers invited by journal 16 Aug, 2025 Editor invited by journal 07 Aug, 2025 Editor assigned by journal 04 Aug, 2025 First submitted to journal 01 Aug, 2025 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-7271545","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501226372,"identity":"a6abb755-bb5c-41be-bd0f-2bb2fdb86d45","order_by":0,"name":"Pratap Ramesh Sonawane","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYCgAZn4GBjZCihgbkHnskg2kauE3OEBAi7n04ecPfu5gsOeXbn4m8XOHjbTxjeRnDz5UMMjzix3AqsWyL82wsfcMQ+LMOcfMJHvPpBmb3UgzN5xxhsFw5uwErFoMgHINvG0MCQY3Esxu8LYdTjYDMqTBIrdxaWH/2Pi3jcHe4Eb6t5t/2/7Xb56R/o2AFh7DZqACxg03csxu87YdYDaQyMFvi2UPT+Fs2TMSiTNn5JT/lm1LZpY486ZMcsYZCZx+Medh3/Dx7Q4be36J9M2Gb9vsmPnb07dJfKiwkeeXxuEwEMHYIIEkJABWKYFNNbIWZCH+AzhVj4JRMApGwcgEAK/FXWEAVFdFAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-6681-5208","institution":"Matoshri College of Engineering and Research Center","correspondingAuthor":true,"prefix":"","firstName":"Pratap","middleName":"Ramesh","lastName":"Sonawane","suffix":""},{"id":501226373,"identity":"a5e22264-4091-41a2-a377-89daa26a2726","order_by":1,"name":"Neeraj B. 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