FEM and ANN Analysis in Fine-Blanking Process
FEM and ANN Analysis in Fine-Blanking Process
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精冲过程中的 FEM 和 ANN 分析
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
E. Derakhshani
中科院分区：
文献类型：
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作者：
F. Djavanroodi;A. Pirgholi;E. Derakhshani
精冲（FB）是一种有效且经济的剪切工艺，可提供精确且干净的切割边缘，消除不必要的二次操作，并提高质量。在传统的冲裁产品开发模式中，成型产品和模具的设计通常基于专业知识和经验，这些知识和经验通常是通过多年的学徒和熟练的工艺获得的。本研究探讨将有限元素法与人工神经网路结合应用于精密冲裁制程分析之可行性。采用各向同性的弹塑性材料模型，利用有限元分析方法对该过程进行了模拟。结果与文献中的实验结果进行了比较;在用实验数据验证模型的有效性后，将V形圈高度效应、凸模和模辊上的保持器力、静压状态作为增加抛光区的重要因素，零件精度和径向应力状态作为增加模具侵蚀的因素，也用于训练神经网络，被考虑。最后，数值数据被用来训练神经网络。Levenberg-Marquardt（LM）算法在隐藏层（LM-3）中具有三个神经元，似乎是最优拓扑，并且给出了最佳结果。结果表明，对于模辊尺寸，有限元法与人工神经网络预测值的多重决定系数（R2值）约为0.999，表明有限元法与人工神经网络相结合的方法可作为精冲工艺设计的有力工具。
Fine-blanking (FB) is an effective and economical shearing process that offers a precise and clean cutting-edge finish, eliminates unnecessary secondary operations, and increases quality. In the traditional blanking product development paradigm, the design of the formed product and tooling is usually based on know-how and experience, which are generally obtained through long years of apprenticeship and skilled craftsmanship. In this study, the possibility of using finite element method (FEM) together with artificial neural networks (ANN) was investigated to analysis the fine-blanking process. Finite element analysis was used to simulate the process with an isotropic elastic–plastic material model. The results compare well with experimental results available in the literature; after confirming the validity of the model with experimental data, a number of parameters such as V-ring height effect, punch and holder force on die-roll, hydrostatic pressure status as an important factor in increasing burnish zone, and accuracy of part and radial stress status as a factor in increasing die erosion, which were also used for training the ANN, were considered. Finally, numerical data were used to train neural networks. The Levenberg–Marquardt (LM) algorithm with three neurons in the hidden layer (LM-3) appeared to be the most optimal topology and gives the best results. It was found that the coefficient of multiple determinations (R 2 value) between the FEM and ANN predicted data is equal to about 0.999 for the size of die-roll, therefore indicating the possibility of FEM and ANN as a powerful design tool for the fine-blanking process.