• Journal of Welding and Joining
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• v.31 no.3
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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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• v.32 no.2
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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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.7
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• pp.707-712
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• 2015

During the welding process, welding distortion is caused by the non-uniformity of the temperature distribution in the weldment. Welding distortion is redistributed because the residual stress and rigidity change according to the removal of the welded structure. In shipbuilding in particular, this phenomenon may be observed during the cutting process of lugs that are attached to blocks for transfer. The redistribution of welding distortion also causes problems, such as damage to the cutting tool. The aim of this study is to experimentally analyze the redistribution of welding distortion because of the partial removal of the welded structure. In the experiments conducted in this study, fillet welding and cutting were performed, and longitudinal bending and angular distortion in the welded structures were then investigated and analyzed.

• Journal of the Korean Society of Safety
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• v.28 no.3
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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.