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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
JWJ. 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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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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Welding New Generation Steel PB2 Using Conventional Welding with and without PWHT with Use of Temper Bead Technique
Design, Fabrication and Economy of Metal Structures. 2013;495-500   Crossref logo
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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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PULSED FM-GMA WELDING
Welding for Challenging Environments. 1986;21-29   Crossref logo
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Thin sheet welding of Al 6082 alloy by AC pulse-GMA and AC wave pulse-GMA welding
Materials & Design. 2009;30(2):306-313   Crossref logo
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A Non-Intrusive GMA Welding Process Quality Monitoring System Using Acoustic Sensing
Sensors. 2009;9(9):7150-7166   Crossref logo
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Study of technological opportunities of GMA welding and surfacing with pulse electrode wire feed
Welding in the World. 2016;60(3):525-533   Crossref logo
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Phenomenological model-based study on electron beam welding process, and input-output modeling using neural networks trained by back-propagation algorithm, genetic algorithms, particle swarm optimization algorithm and bat algorithm
Applied Intelligence. 2017;   Crossref logo
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Deep Learning in Natural Language Generation from Images
Deep Learning in Natural Language Processing. 2018;289-307   Crossref logo
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This metadata service is kindly provided by CrossRef from May 29, 2014. Journal of Welding and Joining has participated in CrossRef Text and Data Mining service since October 29, 2014.