标题：Effect analysis and ANN prediction of surface roughness in end milling AISI H13 steel
作者：Zhang, Qing ;Zhang, Song ;Man, Jia ;Zhao, Bin
作者机构：[Zhang, Qing ;Zhang, Song ;Man, Jia ;Zhao, Bin ] Key Laboratory of High Efficiency and Clean Mechanical Manufacture (Ministry of Education), School of 更多
会议名称：6th International Conference on High Speed Machining, ICHSM 2014
会议日期：24 July 2014 through 25 July 2014
来源：Materials Science Forum
关键词：Artificial neural network (ANN); End milling; S/N ratio; Surface roughness
摘要：Surface roughness has a significant effect on the performance of machined components. In the present study, a total of 49 end milling experiments on AISI H13 steel are conducted. Based on the experimental results, the signal-to-noise (S/N) ratio is employed to study the effects of cutting parameters (axial depth of cut, cutting speed, feed per tooth and radial depth of cut) on surface roughness. An ANN predicting model for surface roughness versus cutting parameters is developed based on the experimental results. The testing results show that the proposed model can be used as a satisfactory prediction for surface roughness. © (2014) Trans Tech Publications, Switzerland.