摘要
磨削温度是评价磨削过程的一个重要指标,利用BP神经网络良好的非线性映射功能,以磨削用量(砂轮线速度、工作太速度和磨削深度)为输入,以磨削温度为输出,建立了磨削温度的BP神经网络预测模型。并通过仿真验证了模型的正确性,为磨削温度的预测提供了一个简单可行的方法。
Grinding temperature is an important indicator to evaluate the grinding process. In this paper, BP neural network model of the grinding temperature was built by using the nonlinear mapping ability of BP neural network. Grinding parameters （wheel speed, workbench speed and grinding depth） are the model＇ s input, and the output is grinding temperature. The simulation is conducted to verify the correctness of the model. A simple and feasible method for grinding temperature forecast is provided.
出处
《机械管理开发》
2013年第1期145-146,共2页
Mechanical Management and Development
关键词
磨削
BP神经网络
磨削温度
预测
grinding
BP neural network
grinding temperature
forecast
