摘要
表面粗糙度的预测是切削加工质量分析的重要研究方向,为了在保证铣削的同时预测加工表面的粗糙度、提高生产率,将人工神经网络技术应用于铣削加工领域。应用BP神经网络建立高速铣削加工表面粗糙度预测模型,将预报结果与试验真值进行对比验证,结果表明该方法能够得到较好的预测精度,对高速铣削参数的选择和表面质量的控制具有指导意义。
The prediction of surface roughness is an important research for machining quality analysis. In order to predict machining surface roughness under ensuring milling and increase productivity, artificial neural network is used to milling area. Build high-speed milling surface roughness prediction model using BP artificial neural network. Prediction results are compared with experimental value, and the resulhs show that this method can achieve better prediction accuracy. It has a guiding significance for parameters＇ selection of high-speed milling and quality control of the surface.
出处
《装备制造技术》
2012年第6期237-238,241,共3页
Equipment Manufacturing Technology
基金
黑龙江省留学归国科学基金资助(LC2011C35)
黑龙江省教育厅科研项目(11551429)
关键词
高速铣削
表面粗糙度
BP神经网络
铝合金
high-speed milling
surface roughness
BP artificial neural network
aluminum alloy
