工具阴极的精确设计与修正是电解加工研究难点之一。采用人工神经网络技术，建立了基于改进BP神经网络的数字化阴极修正模型。通过模型对阴极型面进行了数字化修正，改变传统人工修正的方法，提高阴极修正效率。以多次阴极修正数据为基础，对型面修正量进行预测。结果表明：网络模型预测的阴极修正量与试验修正量比较接近，最大绝对误差在0.015mm左右，证明网络模型具有较好的预测效果。该网络模型能广泛应用于航空发动机叶片等复杂型面阴极的数字化修正，减少修正次数，缩短阴极修正周期，提高叶片电解加工精度。

The accurate design and modification of cathode is one of the key problems in electrochemical machining （ECM）. In this paper, a digital cathode modification model is established based on the improved BP neural network, which substitutes a digital modification, changes the conventional manual correction method and remarkably increases the efficiency of the cathode modification process. In this study, the correction value of the cathode surface is predicted according to the data of multiple cathode modifications. The results show that the proposed model is effective in cathode modification, with a maximum absolute error between the predicted correction value and the actual one of only about 0. 015 mm, and that it can be widely used to the digital cathode modification of complex surfaces such as aeroengine blade, with greatly-reduced modification time and modification period as well as an improved electrochemical machining accuracy.