Weld pool shape prediction in plasma augmented laser welded steel

Vitek, J.M.; David, S. A.; Richey, M.W.; Biffin, J.; Blundell, N.; Page, Colin

doi:10.1179/136217101101538929

Cited by 27 publications

(10 citation statements)

References 4 publications

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“…[4][5][6][7] However, welding operations are very sensitive and welding conditions, skill levels, weld defects, and heataffected zones (HAZ) created by the heat generated during welding cause changes in the mechanical properties. [8][9][10] Since such weld defects and HAZ may change the microstructures of metals and consequently affect the material properties considerably, the fatigue strength of weld zones should be evaluated before the parts are applied to the actual products. [11][12][13][14][15] In particular, the weld zone strength highly relies on the welding method.…”

Section: Introductionmentioning

confidence: 99%

The effects of robot welding and manual welding on the low- and high-cycle fatigue lives of SM50A carbon steel weld zones

Han

Yang

Kim

et al. 2019

Advances in Mechanical Engineering

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The purpose of this study is to analyze the differences between the effects of robot welding and manual welding on the low- and high-cycle fatigue lives of the weld zones for T-shaped weld structures fabricated from SM50A carbon steel using a CO2 gas arc welding method. Fatigue tests were conducted using a three-point bending method, and the S-N curves of the manual welding and robot welding crossed each other at approximately 3 × 104 cycles. The robot welding weld zone had better high-cycle fatigue lives than the manual welding. The results are attributable to the fact that the more uniform and higher welding speed of the robot welding leads to smaller weld zone area (i.e. ~12% smaller than the manual welding) and also smaller grain size than the manual welding. Because a smaller grain size in the robot welding weld zone results in a higher hardness than the manual welding and material brittleness increases with increasing hardness, the robot welding weld zone shows better high-cycle fatigue lives but poorer low-cycle fatigue lives than the manual welding.

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Section: Introductionmentioning

confidence: 99%

The effects of robot welding and manual welding on the low- and high-cycle fatigue lives of SM50A carbon steel weld zones

Han

Yang

Kim

et al. 2019

Advances in Mechanical Engineering

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show abstract

“…Vitek et al 5 employed ANNs to predict weld pool shape as a function of welding parameters for arc welding processes. Eguchi et al 6 proposed the switchback method to achieve stable back-bead geometry, and an arc sensor to estimate wire extension and arc length using arc voltage and welding current.…”

Section: Introductionmentioning

confidence: 99%

Prediction model for bead reinforcement area in automatic gas metal arc welding

Shim

Zhang

Yoon

et al. 2018

Advances in Mechanical Engineering

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Automatic welding systems are widely used for high-volume production industries, where the cost of related equipment is justified by the large number of pieces to be made. Detailed movement devices are required, including predetermined welding parameter sequences and timers, to form the weld joints. Automatic gas metal arc welding processes require new mathematical models to predict optimal welding parameters for a given bead geometry to accomplish the desired mechanical properties of the weldment. The developed algorithm should be able to be employed across a wide range of material thicknesses and all welding positions, and available in analytical form to be easily applied to the welding robot with high degree of confidence in predicting bead dimensions. Therefore, this study investigated welding voltage, arc current, welding speed, contact tube weld distance, and welding angle on bead reinforcement area for automated gas metal arc welding processes using a central composite design to generate response surface methodology and artificial neural network models. Average absolute deviation was used to compare accuracy between the two models. Analysis of variance showed coefficients of determination of 0.894 and 0.948 with average absolute deviation 4.01% and 3.11% for the response surface methodology and artificial neural network models, respectively. This suggests that artificial neural network is a better modeling technique for predicting bead reinforcement area compared to response surface methodology.

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“…Recently, researchers all over the world have been paying attention to the hybrid welding process and have developed hybrid welding with more hybrid modes such as laser-metal inert gas (MIG), 4,5 laser-plasma 6 and laser-double arc 7 hybrid welding processes. The investigations show that the hybrid welding…”

Section: Introductionmentioning

confidence: 99%

Hybrid laser-arc welding of magnesium alloys

Song

2010

Welding and Joining of Magnesium Alloys

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Weld pool shape prediction in plasma augmented laser welded steel

Cited by 27 publications

References 4 publications

The effects of robot welding and manual welding on the low- and high-cycle fatigue lives of SM50A carbon steel weld zones

The effects of robot welding and manual welding on the low- and high-cycle fatigue lives of SM50A carbon steel weld zones

Prediction model for bead reinforcement area in automatic gas metal arc welding

Hybrid laser-arc welding of magnesium alloys

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