Advancing One-Sided Non-Destructive Testing for Denoising: Integrating Deep Learning for Enhanced Defect Detection and Quality Assurance

Advancing One-Sided Non-Destructive Testing for Denoising: Integrating Deep Learning for Enhanced Defect Detection and Quality Assurance | 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 Advancing One-Sided Non-Destructive Testing for Denoising: Integrating Deep Learning for Enhanced Defect Detection and Quality Assurance Abdulrahman M. Alanazi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5823897/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 May, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 5 You are reading this latest preprint version Abstract This paper presents a comprehensive study on the enhancement of one-sided Non-Destructive Testing (NDT) methods—Ultrasonic Testing (UT), Eddy Current Testing (ECT), and Radiographic Testing (RT)—through the application of deep learning models. The primary focus is on improving defect detection accuracy, signal-to-noise ratio (SNR), and reducing the minimum detectable defect size. For UT, a Convolutional Neural Network (CNN) was employed to process deconvolved ultrasonic signals, achieving a defect size detection of 0.8 mm and improving SNR to 35 dB with an accuracy rate of 98%. ECT was enhanced using a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) layers, reducing the defect size detection to 0.3 mm, increasing SNR to 45 dB, and achieving a detection accuracy of 99%. In RT, a Deep Convolutional Generative Adversarial Network (DCGAN) was used to enhance X-ray image quality, enabling the detection of defects as small as 0.7 mm with an SNR of 40 dB and a detection probability of 97%. These results highlight the potential of deep learning models to significantly improve the efficiency, accuracy, and reliability of NDT processes. Despite these advancements, challenges such as the need for extensive datasets and high computational power remain, signaling areas for future research. Keywords: Non-Destructive Testing (NDT), Ultrasonic Testing (UT), Eddy Current Testing (ECT), Radiographic Testing (RT), Deep Learning, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Generative Adversarial Networks (GAN), Defect Detection, Signal Processing, Quality Assurance Non-Destructive Testing One-Sided Inspection Deep Learning Defect Detection Quality Assurance Ultrasonic Testing Full Text Cite Share Download PDF Status: Published Journal Publication published 14 May, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Minor Revisions Needed 16 Apr, 2025 Reviewers agreed at journal 21 Mar, 2025 Reviewers invited by journal 21 Mar, 2025 Editor assigned by journal 14 Jan, 2025 First submitted to journal 13 Jan, 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. 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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-5823897","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":432131114,"identity":"fab238f0-093a-4fa4-8677-a11eb952d81d","order_by":0,"name":"Abdulrahman M. 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Despite these advancements, challenges such as the need for extensive datasets and high computational power remain, signaling areas for future research. 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