Application of Evolutionary Neural Networks in Prediction of Tool Wear in Machining Process
进化神经网络在机床工具损耗预测中的应用

An improved evolutionary method based on real-number encoding is presented to optimize the connection weights and the topology of neural networks. The algorithm could adaptively adjust magnitude of mutation according to individual fitness, and mutation rate will increase with evolving generations as soon as evolution gets into stagnancy. Experiments show that the evolutionary artificial neural network is efficient to predict tool wear in electrical discharge milling machining and the prediction results are better than the standard BP neural networks. The proposed prediction model can be used for tool compensation on-line in electrical discharge milling machining.