Diagnosis of Mechanoluminescent Crack Based on Double Deep Learning in Al 7075
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Published:2023-12-05
Issue:12
Volume:61
Page:958-964
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ISSN:1738-8228
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Container-title:Korean Journal of Metals and Materials
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language:en
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Short-container-title:Korean J. Met. Mater.
Author:
Park Tae O,Shin Youn Woo,Lee Seung Hwan,Jwa Pius,Kwon Yong Nam,Timilsina Suman,Jang Seong Min,Jo Chul Woo,Kim Ji Sik
Abstract
The phenomenon of mechanoluminescence (ML) refers to the emission of light induced by mechanical stimulation applied to mechano-optical materials for example SrAl<sub>2</sub>O<sub>3</sub>:Eu,Dy (SAO). Numerous technologies on the basis of ML have been presented to visualize the stress or strain in various structures for the applications including structural health monitoring. As a result, extensive attention has been devoted to the design, synthesis, characteristics, optimizations, and applications of ML materials. However, challenges still remain in the standardization of ML measurement and evaluation, thereby commercially viable ML applications are currently unavailable. To overcome these difficulties, present study proposes ML measurement and evaluation techniques employing the ML fracture mechanics, finite element method, and dual deep learnings. For the effective normalization of visualized ML images under fixed initial ML intensity condition, continuous UV irradiation above the critical ML power density has been subjected to tensile and compact tension (CT) specimens. Therefore, Plastic Stress Intensity Factor (SIF) as well as crack tip stress field have been extracted successfully from normalized ML images based on ML fracture mechanics. To complement and verify the ML analysis, numerical FEM simulation and analytical ASTM calculation have been also provided. Finally, a double deep learning consists of Generative Adversarial Networks (GAN) and Convolutional Neural Networks (CNN) has been trained and tested for the standard evaluation of in-situ ML images.
Funder
National Research Foundation of Korea
Ministry of Science and ICT
National Research Council of Science and Technology
Publisher
The Korean Institute of Metals and Materials
Subject
Metals and Alloys,Surfaces, Coatings and Films,Modeling and Simulation,Electronic, Optical and Magnetic Materials