应用神经网络优化压边力

Using neural network to optimize blank holder force

以汽车行李箱盖冲压成形过程为例,提出一种利用人工神经网络技术对压边力控制曲线进行优化的方法,将压边力优化理论与数值仿真技术相结合,建立了压边力优化RBF神经网络模型。仿真结果证明,采用优化后的变压边力控制曲线能有效改善板料的成形性能和成形质量。

An optimization method of blank holder force （BHF） curve using artificial neural network （ANN） was proposed for the stamping forming of a type of automobile lift-gate. The BHF optimization RBF NN was built up based on BHF optimization theory and numerical simulation technology. The result of finite element simulation proves that the formability and quality of sheet blank are improved under the optimal curve.