• Journal of Welding and Joining
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• 제31권3호
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• pp.44-48
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• 2013

Artificial neural network (ANN) model is applied to predict arc welding process window for automotive steel plate. Target weldment was various automotive steel plate combination with lap fillet joint. The accuracy of prediction was evaluated through comparison experimental result to ANN simulation. The effect of ANN variables on the accuracy is investigated such as number of hidden layers, perceptrons and transfer function type. A static back propagation model is established and tested. The result shows comparatively accurate predictability of the suggested ANN model. However, it restricts to use nonlinear transfer function instead of linear type and suggests only one single hidden layer rather than multiple ones to get better accuracy. In addition to this, obvious fact is affirmed again that the more perceptrons guarantee the better accuracy under the precondition that there are enough experimental database to train the neural network.

• Journal of Welding and Joining
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• 제32권2호
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• pp.43-47
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• 2014

Famous artificial neural network (ANN) is applied to predict proper process window of arc welding. Target weldment is variously combined lap joint fillet welding of automotive steel plates. ANN's system variable such as number of hidden layers, perceptrons and transfer function are carefully selected through case by case test. Input variables are welding condition and steel plate combination, for example, welding machine type, shield gas composition, current, speed and strength, thickness of base material. The number of each input variable referred in welding experiment is counted and provided to make it possible to presume the qualitative precision and limit of prediction. One of experimental process windows is excluded for predictability estimation and the rest are applied for neural network training. As expected from basic ANN theory, experimental condition composed of frequently referred input variables showed relatively more precise prediction while rarely referred set showed poorer result. As conclusion, application of ANN to arc welding process window derivation showed comparatively practical feasibility while it still needs more training for higher precision.

• 대한기계학회논문집A
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• 제39권7호
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• pp.707-712
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• 2015

용접변형은 용접 시 구조물 내에서의 불균일한 온도분포특성으로 인하여 필연적으로 유발되는 현상이다. 또한 용접변형이 발생된 용접구조물의 일부를 제거하는 과정에서 구조물내의 용접잔류응력과 강성의 연속적인 변화에 따라 추가적인 변형이 발생하여 변형의 재분포가 이루어진다. 특히, 이러한 현상은 선박의 제조과정 중 대형블럭을 옮기기 위해 설치된 러그의 절단과정에서 살펴볼 수 있다. 용접구조물의 부분 제거 시 발생되는 변형의 재분포는 절단공구의 파손 등의 문제를 야기하기도 한다. 본 논문은 실험을 통하여 용접구조물의 부분 제거에 따른 용접변형의 재분포가 어떠한 양상으로 발생되는지 연구하기 위한 것이다. 실험을 위해 필릿용접을 실시하였고, 용접된 리브의 일부를 제거함에 따라 발생되는 종굽힘과 각변형을 측정하여 비교 및 분석하였다.

• 한국안전학회지
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• 제28권3호
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• pp.1-5
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• 2013

In order to evaluate fatigue endurance for an MTB(mountain bike) frame, FEM(finite element method) analysis was performed. For evaluating the fatigue endurance of the MTB frame, the S-N data for Al-6061 fillet weldment were compared with the stress analysis results through FEM analysis of the frame. Three loading condition, pedalling, horizontal and vertical loading conditions were considered for fatigue endurance evaluation. Horizontal loading(+1200 N) condition was found to be the most severe to the frame. The maximum von Mises stress of the frame under horizontal loading(+1200 N) condition was determined 294 MPa through FEM analysis of the frame. Conclusively, on the basis of fatigue strength of 200 MPa at the number of cycles of 50,000, the MTB frame has an improper safety factor of approximately 0.25, suggesting that this frame needs reinforcement.