The Effect of Welding Dimensional Variability on the Fatigue Life of Gas Metal Arc Welded Joints

Kang, Hong Tae; Khosrovaneh, Abolhassan; Link, Todd M.; Bonnen, John; Amaya, Mark A.; Shih, Hua Chu

doi:10.4271/2011-01-0196

Cited by 8 publications

(1 citation statement)

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“…Therefore, the weld lines in the chassis parts affect the overall performances of the chassis parts. Additionally, the geometry of the weld bead has a dominant influence on the mechanical performance of the chassis parts, that is, on their tensile strengths and fatigue characteristics [5][6][7][8][9]. The bead geometry is composed of five parts, including the weld face, root, toe, leg, and throat.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Weld Quality Prediction Using a Laser Vision Sensor in a Lap Fillet Joint during Gas Metal Arc Welding

Lee

Hwang

Kim

et al. 2020

Sensors

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Nondestructive test (NDT) technology is required in the gas metal arc (GMA) welding process to secure weld robustness and to monitor the welding quality in real-time. In this study, a laser vision sensor (LVS) is designed and fabricated, and an image processing algorithm is developed and implemented to extract precise laser lines on tested welds. A camera calibration method based on a gyro sensor is used to cope with the complex motion of the welding robot. Data are obtained based on GMA welding experiments at various welding conditions for the estimation of quality prediction models. Deep neural network (DNN) models are developed based on external bead shapes and welding conditions to predict the internal bead shapes and the tensile strengths of welded joints.

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Section: Introductionmentioning

confidence: 99%