@inproceedings{f7012384e02c42388e0f66d9b02ee58f,
title = "Effect analysis and ANN prediction of surface roughness in end milling AISI H13 steel",
abstract = "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.",
keywords = "Artificial neural network (ANN), End milling, S/N ratio, Surface roughness",
author = "Qing Zhang and Song Zhang and Jia Man and Bin Zhao",
year = "2014",
doi = "10.4028/www.scientific.net/MSF.800-801.590",
language = "English",
isbn = "9783038351429",
series = "Materials Science Forum",
publisher = "Trans Tech Publications Ltd.",
pages = "590--595",
editor = "Xianli Liu and Bin Jiang and Xuefeng Wu and Caixu Yue",
booktitle = "High Speed Machining VI",
address = "Switzerland",
note = "6th International Conference on High Speed Machining, ICHSM 2014 ; Conference date: 24-07-2014 Through 25-07-2014",
}