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A Study on the Algorithm for Determining Back Bead Generation in GMA Welding Using Deep Learning
Min Seok Kim, Seung Min Shin, Dong Hyun Kim, Sehun Rhee
J Weld Join. 2018;36(2):74-81.   Published online 2018 April 13    DOI: https://doi.org/10.5781/JWJ.2018.36.2.11

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A Study on the Algorithm for Determining Back Bead Generation in GMA Welding Using Deep Learning
Journal of Welding and Joining. 2018;36(2):74-81   Crossref logo
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Development of an Artificial Intelligence Powered TIG Welding Algorithm for the Prediction of Bead Geometry for TIG Welding Processes using Hybrid Deep Learning
Metals. 2020;10(4):451   Crossref logo
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Prediction of bead geometry in pulsed GMA welding using back propagation neural network
Journal of Materials Processing Technology. 2008;200(1-3):300-305   Crossref logo
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Spectrogram Based Detection Algorithm for Back-Bead in Gas Metal Arc Welding Process using Convolution Neural Network
Journal of Welding and Joining. 2021;39(2):198-205   Crossref logo
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Prediction Model for Back-Bead Monitoring During Gas Metal Arc Welding Using Supervised Deep Learning
IEEE Access. 2020;8:224044-224058   Crossref logo
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Convolutional Neural Network Model for the Prediction of Back-Bead Occurrence in GMA Root Pass Welding of V-groove Butt Joint
Journal of Welding and Joining. 2021;39(5):463-470   Crossref logo
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A Study on Spatter Tracking Algorithm for a Vertical GMA Welding Process
Journal of Welding and Joining. 2019;37(4):347-356   Crossref logo
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Welding New Generation Steel PB2 Using Conventional Welding with and without PWHT with Use of Temper Bead Technique
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DNA Cryptography and Deep Learning using Genetic Algorithm with NW algorithm for Key Generation
Journal of Medical Systems. 2017;42(1):   Crossref logo
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An investigation into an intelligent system for predicting bead geometry in GMA welding process
Journal of Materials Processing Technology. 2005;159(1):113-118   Crossref logo
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