Experimental Investigations of Optimum Sheet Metal Blanking Clearance for IS2062 HR Steel Using Artificial Neural Network(ANN)

Vijaya P.Patil, Pradip P.Patil, Nilesh E. Ingale
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引用次数: 1

Abstract

In sheet metal forming, particularly in sheet metal blanking, an unsuitable value of clearance may lead to secondary crack formation. Cracks lead to uneven edges leading to loss of productivity in terms of quality of surface finish and dimensional accuracy. In the present work, experiments are carried out on the power press with the uni-punch tool as a cutting tool and IS2062HR material. In blanking, punch penetration is varied to find a depth at which crack initiates in sheet metal. After punching, shear angle and fracture angle and punch penetration considered as input parameters from the available ranges. Experimentally, the optimum value of clearance obtained by plotting angles versus per cent clearance. The input values fed to train the Neural Network (NN) which predicts clearance for different unknown input parameters. The results of predictions are well within the range of experimental values. However, the material ductility influences the clearance selection for blanking.
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应用人工神经网络(ANN)优化IS2062 HR钢板料冲裁间隙的实验研究
在板料成形,特别是板料下料过程中,间隙值不合适可能导致二次裂纹的形成。裂纹导致边缘不均匀,从而导致表面光洁度和尺寸精度方面的生产力损失。在本工作中,以单孔刀具为切削刀具,采用IS2062HR材料在动力压力机上进行了实验。在落料过程中,要改变冲床的侵彻,以确定板料中裂纹产生的深度。冲压后,从可用范围内考虑剪切角、断裂角和冲孔深度作为输入参数。实验上,通过绘制角度与百分比间隙的关系得到最佳间隙值。输入值用于训练神经网络(NN),该网络预测不同未知输入参数的间隙。预测的结果完全在实验值的范围内。然而,材料的延展性影响了冲裁间隙的选择。
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