• Title/Summary/Keyword: MIG 용접

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The Waveform Control and Blowhole Generation in the Wave Pulse MIG Welding for Galvanized Steel Sheets (아연도금강판에 대한 중첩펄스 MIG 용접에서의 파형제어와 기공 발생 특성)

  • Cho Sang-Myung;Kim Ki-Jung;Lee Byung-Woo
    • Journal of Welding and Joining
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    • v.23 no.1
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    • pp.69-76
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    • 2005
  • Recently, application of arc welding to galvanized carbon steel sheet is on the increasing Ould in the fields of automobile and construction industries. In arc welding process, zinc is evaporated in weld pool, even under the appropriate welding condition and produce blowhole and/or pit. Zinc gas cause instability of arc and increase spatter and fume. This research is purposed to minimize the heat-input and the formation of porosities in the welded joint of the galvanized carbon steel sheet using variable polarity AC wave pulse MIG welding system. An appropriate welding condition which showed low spatter and good bead appearance was acquired by applying the AC pulse MIG welding machine to DC duplicated MIG welding with the solid wire. When oxygen gas was added to shield gas of MIG welding for galvanized steel sheet, arc length was increased and arc stability was improved. In the AC duplicated welding, the loss of galvanized layer was decreased as the amount of heat-input was decreased when the EN ratio was increased under the condition that average welding current was evenly set.

Effects of laser and arc power on the penetration depth in $CO_2$ laser-MIG hybrid welding ($CO_2$ 레이저-MIG 하이브리드 용접부 용입깊이에 미치는 레이저 및 아크 출력의 영향)

  • 홍승갑;이종봉
    • Proceedings of the KWS Conference
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    • 2003.05a
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    • pp.81-83
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    • 2003
  • The potential advantages of the hybrid welding process are improved weld penetration, enhanced gap tolerance, control of weld metal composition, and improved weld quality in comparison to laser or arc welding. Especially, the deep penetration of hybrid welding is very attractive in welding of thick plates. In this study, therefore, the influence of arc power in hybrid welding on detailed bead dimensions at different laser power levels was investigated.

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Characteristic study of mechanical properties of Structural rolled steel and Stainless steel for MIG welding (구조용 압연강재와 스테인리스 강재의 MIG 용접에 대한 기계적 특성연구)

  • Lim, J.Y.;Yoon, M.J.;Kim, S.Y.;Kim, T.G.
    • Journal of the Korean Society of Manufacturing Process Engineers
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    • v.13 no.1
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    • pp.100-106
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    • 2014
  • It is well known that fatigue failures occur on welded structures in industrial application due to repetitive load force. In order to decrease the incidence of fatigue failure, we analyzed the mechanical properties based on structural aspects in rolled steel(SS 400) welded onto stainless steel (STS 304) by the MIG welding method as well as the structure of rolled steel welded onto itself. We compared the hardness, tensile and fatigue properties with two types of samples which had no defects on the welding parts as observed by X-ray topographic analysis. It was found that the tensile and fatigue strength levels of SS 400 welded onto STS 304 by the MIG welding method were higher than those of STS 304 welded onto itself.

Prediction of Tensile Strength for Plasma-MIG Hybrid Welding Using Statistical Regression Model and Neural Network Algorithm (통계적 회귀 모형과 인공 신경망을 이용한 Plasma-MIG 하이브리드 용접의 인장강도 예측)

  • Jung, Jin Soo;Lee, Hee Keun;Park, Young Whan
    • Journal of Welding and Joining
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    • v.34 no.2
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    • pp.67-72
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    • 2016
  • Aluminum alloy is one of light weight material and it is used to make LNG tank and ship. However, in order to weld aluminum alloy high density heat source is needed. In this paper, I-butt welding of Al 5083 with 6mm thickness using Plasma-MIG welding was carried out. The experiment was performed to investigate the influence of plasma-MIG welding parameters such as plasma current, wire feeding rate, MIG-welding voltage and welding speed on the tensile strength of weld. In addition we suggested 3 strength estimation models which are second order polynomial regression model, multiple nonlinear regression model and neural network model. The estimation performance of 3 models was evaluated in terms of average error rate (AER) and their values were 0.125, 0.238, and 0.021 respectively. Neural network model which has training concept and reflects non -linearity was best estimation performance.