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表面粗糙度趋势分析及预测技术是计算机集成制造系统故障诊断技术发展的迫切需要。本文在讨论神经网络非线性、多因素预测原理及其拓扑结构的基础上,基于神经网络方法设计了智能型的工件表面粗糙度监测预测系统,将非线性预测和多因素预测引入表面粗糙度预测模型中,即在进行工件表面租糙度预测时兼顾了刀具磨损,从而使本系统拥有可靠和高精度的预测效果。
Surface roughness trend analysis and prediction technology is an urgent need for the development of computer integrated manufacturing system fault diagnosis technology. Based on the theory of nonlinear and multi-factor prediction of neural network and its topological structure, this paper designs an intelligent system for monitoring and forecasting the surface roughness of workpieces based on the neural network method. The nonlinear and multi-factor prediction are introduced into the prediction of surface roughness In the model, tool wear is taken into account when predicting the roughness of the surface of the workpiece, so that the system has reliable and high-precision prediction results.