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The application of abductive networks and FEM to predict the limiting drawing ratio in sheet metal forming processes

机译:绑架网络和有限元法在钣金成形过程中极限拉深比预测中的应用

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摘要

The deep drawing process, one of the sheet metal forming methods, is very useful in the industrial field because of its efficiency. The limiting drawing ratio (LDR) is affected by many material and process parameters, such as the strain-hardening exponent, the plastic strain ratio, friction and lubrication, the blank holder force, the presence of drawbeads, the profile radius of the die and punch, etc. In order to verify the finite element method (FEM) simulation results of the LDR, the experimental data are compared with the results of the current simulation. The influences of the process parameters such as the blank holder force, the profile radius of the die, the clearance between the punch and the die, and the friction coefficient on the LDR are also examined. The abductive network was then applied to synthesize the data sets obtained from the numerical simulation. The predicted results of the LDR from the prediction model are in good agreement with the results obtained from the FEM simulation. By employing the predictive model, it can provide valuable references to the prediction of the LDR under a suitable range of process parameters.
机译:作为金属板成型方法之一的深冲工艺由于其效率高而在工业领域中非常有用。极限拉伸比(LDR)受许多材料和工艺参数的影响,例如应变硬化指数,塑性应变比,摩擦和润滑,毛坯夹持力,拉延筋的存在,模具的轮廓半径和为了验证LDR的有限元方法(FEM)仿真结果,将实验数据与当前仿真结果进行了比较。还检查了诸如坯料夹持器力,模具轮廓半径,冲头与模具之间的间隙以及LDR的摩擦系数等工艺参数的影响。然后将外展网络应用于从数值模拟中获得的数据集。来自预测模型的LDR预测结果与从FEM仿真获得的结果非常吻合。通过使用预测模型,它可以在适当范围的过程参数下为LDR的预测提供有价值的参考。

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