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Prediction of Weld Tensile-Shear Strength using ANN Based on the Weld Shape in Aluminum Alloy GMAW
Dong-Yoon Kim, June Hyung Hwang, Gwang-Gook Kim, Young-Min Kim, Jiyoung Yu, Junhong Park
J Weld Join. 2023;41(1):17-27.   Published online 2023 February 17    DOI: https://doi.org/10.5781/JWJ.2023.41.1.2

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Prediction of Weld Tensile-Shear Strength using ANN Based on the Weld Shape in Aluminum Alloy GMAW
Journal of Welding and Joining. 2023;41(1):17-27   Crossref logo
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Study on prediction of tensile-shear strength of weld spot based on an improved neural network algorithm
Welding in the World. 2022;67(3):741-752   Crossref logo
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Prediction of Tensile-Shear Strength of Spot Welds Based on Fracture Modes
Welding in the World. 2004;48(5-6):39-45   Crossref logo
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Study of the weld ability of aluminum alloy 5083 H116 with pulsed arc GMAW (GMAW-P)
Ciencia y tecnología de buques. 2012;6(11):43   Crossref logo
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Development of response surface model for tensile shear strength of weld-bonds of aluminium alloy 6061 T651
Materials & Design. 2012;34:673-678   Crossref logo
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Correlation between weld nugget grain size, weld nugget hardness and tensile strength of friction stir welded commercial grade aluminium alloy joints
Materials & Design. 2012;34:242-251   Crossref logo
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Prediction of Optimum Weld Tensile Strength Using Response Surface Methodology
European Journal of Engineering and Technology Research. 2021;6(3):81-84   Crossref logo
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Prediction of Weld Bead Dilution in GMAW Process Using Fuzzy Logic
Applied Mechanics and Materials. 2011;110-116:3171-3175   Crossref logo
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Numerical Prediction of Shrinkage Defect in Aluminum Alloy Electron Beam Weld Based on Niyama Criterion Program
Advanced Materials Research. 2014;1030-1032:130-133   Crossref logo
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Prediction of weld formation in 5083 aluminum alloy by twin-wire CMT welding based on deep learning
Welding in the World. 2019;63(4):947-955   Crossref logo
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