基于BP神经网络的电解加工精度预测模型

为精确地预测电解加工精度，采用了BP神经网络的方法进行建模．在分析影响加工精度主要因素的基础上，确定了BP神经网络模型的特征参数，并根据实际情况，确定了输入层和中间隐层的维数，从而确定了模型的结构．用试验参数对模型结构进行训练，最终建立了一个用于电解加工精度预测的BP神经网络模型．利用该模型进行的精度预测结果表明，该模型的预测误差可以控制在10％以内，具有很高的精度预测能力．

Prediction of the Workpiece Accuracy During the Electrochemical Machining Based on BP Neural Network

In order to correctly predict the workpiece accuracy during the electrochemical machining(ECM), the method of BP neural network was used to establish a prediction model. Feature parameters of the BP neural network model were determined by analyzing the main factors affecting the machined accuracy, and the dimensional numbers of the input layer and the middle hidden layer were confirmed according to the practical conditions, thus obtaining the model structure which was then trained by using the experimental results. Finally, a BP neural network model to predict the accuracy of the workpiece during the ECM was established. The prediction results show that the error of accuracy prediction can be controlled within 10% by using the proposed model, which is of excellent capability of accuracy prediction.