An optimization method for radial forging process using ANN and Taguchi method
An optimization method for radial forging process using ANN and Taguchi method
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DOI：
10.1007/s00170-008-1371-2
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发表时间：
2009-02
期刊：
影响因子：
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通讯作者：
M. Sanjari;A. Karimi Taheri;M. R. Movahedi
中科院分区：
文献类型：
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作者：
M. Sanjari;A. Karimi Taheri;M. R. Movahedi
采用人工神经网络和田口法对径向锻造过程的径向力和应变非均匀性进行了优化。通过显微硬度试验验证的工艺有限元分析（以确认预测的应变分布）和先前研究者发表的实验锻造载荷来预测最终产品中的应变分布和径向力。首先采用正交阵列法选择工艺参数组合进行田口法数值试验，然后采用有限元法进行数值模拟。然后通过田口法预测了最佳工艺条件。在此基础上，利用有限元方法训练了人工神经网络模型（以遗传算法为全局优化过程），预测了最优条件，并与田口法的结果进行了比较。用有限元法对优化条件进行了验证，两组结果吻合较好。
In this study, the artificial neural network (ANN) and the Taguchi method are employed to optimize the radial force and strain inhomogeneity in radial forging process. The finite element analysis of the process verified by the microhardness test (to confirm the predicted strain distribution) and the experimental forging load published by the previous researcher are used to predict the strain distribution in the final product and the radial force. At first, a combination of process parameters are selected by orthogonal array for numerical experimenting by Taguchi method and then simulated by FEM. Then the optimum conditions are predicted via the Taguchi method. After that, by using the FEM results, an ANN model was trained and the optimum conditions are predicted by means of ANN (using genetic algorithm as global optimization procedure) and compared with those achieved by the Taguchi method. The optimum conditions are verified by FEM, and good agreement is found between the two sets of results.