Weld fatigue behavior of gas metal arc welded steel sheets based on porosity and gap size-Reference-Cited by-同舟云学术

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Weld fatigue behavior of gas metal arc welded steel sheets based on porosity and gap size

Published:2022-11-28 Issue:3-4 Volume:124 Page:1141-1153
ISSN:0268-3768
Container-title:The International Journal of Advanced Manufacturing Technology
language:en
Short-container-title:Int J Adv Manuf Technol

Author:

Kim Dong-Yoon,Kim Gwang-Gook,Yu Jiyoung,Kim Dong Cheol,Kim Young-Min,Park Junhong

Funder

Korea Institute of Industrial Technology

Publisher

Springer Science and Business Media LLC

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Mechanical Engineering,Software,Control and Systems Engineering

Link

https://link.springer.com/content/pdf/10.1007/s00170-022-10567-8.pdf

Reference29 articles.

1. Yang J, Oliveira JP, Li Y, Tan C, Gao C, Zhao Y, Yu Z (2022) Laser techniques for dissimilar joining of aluminum alloys to steels: a critical review. J Mater Process Tech 301:117443. https://doi.org/10.1016/j.jmatprotec.2021.117443

2. Xu W, Li H, Yang J, Zhao Y, Oliveira JP, Liu H, Zhang Z, Tan C (2022) Dynamic wetting and spreading mechanisms regulated by elemental Ni in a Mg/steel immiscible system during laser processing. J Manuf Process 80:600–611. https://doi.org/10.1016/j.jmapro.2022.06.042

3. Bannantine JA, Comer JJ, Handrock JL (1990) Fundamentals of metal fatigue analysis. Prentice Hall, New Jersey

4. Ahiale GK, Oh YJ, Choi WD, Lee KB, Jung JG, Nam SW (2013) Microstructure and fatigue resistance of high strength dual phase steel welded with gas metal arc welding and plasma arc welding processes. Met Mater Int 19:933–939. https://doi.org/10.1007/s12540-013-5005-3

5. Duchet M, Haouas J, Gibeau E, Pechenot F, Honecker C, Munier R, Weber B (2019) Improvement of the fatigue strength of welds for lightweight chassis application made of advanced high strength steels. Procedia Structural Integrity 19:585–594. https://doi.org/10.1016/j.prostr.2019.12.063

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1. Application of Convolutional Neural Networks for Classifying Penetration Conditions in GMAW Processes Using STFT of Welding Data;Applied Sciences;2024-06-04

2. Deep convolutional neural network for weld defect classification in radiographic images;Heliyon;2024-05

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