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A Study on the Development of Deep Learning Algorithm for Determining External Quality of Welded Parts Using Transfer Learning: Bo Wook Seo, Sung Bin Han, Jun Young Heo, Seok Kim, Young Tae Cho; J Weld Join. 2023;41(2):132-139. Published online April 30, 2023; DOI: https://doi.org/10.5781/JWJ.2023.41.2.7
Cited By 1; Full text PubReader ePub PDF

Study of Convolution Neural Network Based Deep Learning to Classify the Quality of Self-Piercing Riveting Joint: Min Gyu Kim, Tae Hyun Lee, Seung Hwan Lee, Cheol Hee Kim, Dong Hyuck Kam; J Weld Join. 2022;40(6):502-511. Published online December 30, 2022; DOI: https://doi.org/10.5781/JWJ.2022.40.6.6; Full text PubReader ePub PDF

Study on Nugget Diameter Prediction of Resistance Spot Welding Using an Artificial Neural Network: Jongkyu Kim, JaHun Ku, Yeongdo Park et al.; J Weld Join. 2021;39(6):649-657. Published online December 6, 2021; DOI: https://doi.org/10.5781/JWJ.2021.39.6.10
Cited By 2; Full text PubReader ePub PDF

Convolutional Neural Network Model for the Prediction of Back-Bead Occurrence in GMA Root Pass Welding of V-groove Butt Joint: Hyung Won Lee, Jiyoung Yu, Gwang-Gook Kim et al.; J Weld Join. 2021;39(5):463-470. Published online October 19, 2021; DOI: https://doi.org/10.5781/JWJ.2021.39.5.1
Cited By 3; Full text PubReader ePub PDF

Study on Real-Time Porosity Defect Detection Through Neural Network Structure Optimization using Genetic Algorithm in GMAW: Dongwook Lee, Chengnan Jin, Sehun Rhee; J Weld Join. 2021;39(5):542-551. Published online September 17, 2021; DOI: https://doi.org/10.5781/JWJ.2021.39.5.11
Cited By 2; Full text PubReader ePub PDF

Spectrogram Based Detection Algorithm for Back-Bead in Gas Metal Arc Welding Process using Convolution Neural Network: Chengnan Jin, Sangrin Park, Sehun Rhee; J Weld Join. 2021;39(2):198-205. Published online April 1, 2021; DOI: https://doi.org/10.5781/JWJ.2021.39.2.9
Cited By 1; Full text PubReader ePub PDF

Review on the Recent Welding Research with Application of CNN-Based Deep Learning Part II: Model Evaluation and Visualizations: Kidong Lee, Sung Yi, Soongkeun Hyun, Cheolhee Kim; J Weld Join. 2021;39(1):20-26. Published online February 8, 2021; DOI: https://doi.org/10.5781/JWJ.2021.39.1.2
Cited By 11; Full text PubReader ePub PDF

Review on the Recent Welding Research with Application of CNN-Based Deep Learning Part I: Models and Applications: Kidong Lee, Sung Yi, Soongkeun Hyun, Cheolhee Kim; J Weld Join. 2021;39(1):10-19. Published online February 8, 2021; DOI: https://doi.org/10.5781/JWJ.2021.39.1.1
Cited By 19; Full text PubReader ePub PDF

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 April 13, 2018; DOI: https://doi.org/10.5781/JWJ.2018.36.2.11
Cited By 11; Full text PubReader ePub PDF

Prediction of Tensile Strength for Plasma-MIG Hybrid Welding Using Statistical Regression Model and Neural Network Algorithm: Jin Soo Jung, Hee Keun Lee, Young Whan Park; J Weld Join. 2016;34(2):67-72. Published online April 30, 2016; DOI: https://doi.org/10.5781/JWJ.2016.34.2.67
Cited By 4; Full text PubReader ePub PDF

A Study on Development of STACO Model to Predict Bead Height in Tandem GMA Welding Process: Jongpyo Lee, IllSoo Kim, Minho Park, Cheolkyun Park, Bongyong Kang, Jiyeon Shim; J Weld Join. 2014;32(6):544-549. Published online December 31, 2014; DOI: https://doi.org/10.5781/JWJ.2014.32.6.8
Cited By 2; PDF

Proper Arc Welding Condition Derivation of Auto-body Steel by Artificial Neural Network: Jungho Cho; J Weld Join. 2014;32(2):169-173. Published online May 14, 2014; DOI: https://doi.org/10.5781/JWJ.2014.32.2.43
Cited By 2; PDF

Using Neural Network Algorithm for Bead Visualization: Chang-Dae Koo, Hyeong-Seok Yang, Jung-Yeong Kim, Sang-Ho Shin; J Weld Join. 2013;31(5):35-40. Published online November 5, 2013; DOI: https://doi.org/10.5781/KWJS.2013.31.5.35; PDF

Prediction of Arc Welding Quality through Artificial Neural Network: Jungho Cho; J Weld Join. 2013;31(3):44-48. Published online June 30, 2013; DOI: https://doi.org/10.5781/KWJS.2013.31.3.44
Cited By 5; PDF

Development of Intelligent Monitoring System for Welding Process Faults Detection in Auto Body Assembly: TaeHyung Kim, Jiyoung Yu, Sehun Rhee, YoungWhan Park; J Weld Join. 2010;28(4):81-86. Published online September 8, 2010; PDF

Volume 42, No. 1

Journal of
Welding and
Joining

Print ISSN: 2466-2232
Online ISSN: 2466-2100

MOST VIEWED MOST CITED

7,544	Review on Vacuum Laser Beam Welding
4,017	A Study on Welding-induced Temperature Distribution and Deformation of IMO Type C LNG Fuel Tank Support Structures
3,778	Optimization of FC-CSP and MLCC Soldering Process Using Type 7 Solder Paste
3,743	Study on Laser Welding of Dissimilar Materials between Aluminum and Copper (Ⅰ) - Research Trend in Laser Welding Technology and Materials Testing Methods -
3,647	Recent Studies of Transient Liquid Phase Bonding Technology for Electric Vehicles

Cited By 9	Changes in the Mechanical Properties and Microstructure of High Manganese Steel by High Heat Input Welding and General Welding Processes
Cited By 7	Review on Vacuum Laser Beam Welding
Cited By 6	A Study on Welding-induced Temperature Distribution and Deformation of IMO Type C LNG Fuel Tank Support Structures
Cited By 6	Comparison of Welding Deformation Characteristics by Circumferential Welding for Austenitic and Duplex Stainless Steel Pipe : Experimental Study
Cited By 6	Development and Evaluation of Stainless Steel-Base Flux-Cored Wires for 9% Nickel Steel

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