Proceedings of the 7th International Conference on Education, Management, Information and Computer Science (ICEMC 2017)

Prediction of Surface Roughness for HSM Based on BP Neural Network

Authors
Chen Ying, Sun Yanhong, Yang Zhengwen, Wu Guangdong
Corresponding Author
Chen Ying
Available Online June 2016.
DOI
https://doi.org/10.2991/icemc-17.2017.83How to use a DOI?
Keywords
Surface roughness; BP neural network; Cutting parametres; 5-axis machine; Toroidal cutter
Abstract
A predictive model is presented for the surface roughness in high-speed milling of P1.2738 (plastic die steel)based on BP Neural network. The data for establishing the model is derived from the experiment conducted on a high-speed 5-axis machining center by factorial design of experiments. Compared with measured data and data from regression analysis, the result of prediction using BP neural network indicates its feasibility, which provides reference for the optimization of cutting parameters.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Cite this article

TY  - CONF
AU  - Chen Ying
AU  - Sun Yanhong
AU  - Yang Zhengwen
AU  - Wu Guangdong
PY  - 2016/06
DA  - 2016/06
TI  - Prediction of Surface Roughness for HSM Based on BP Neural Network
BT  - Proceedings of the 7th International Conference on Education, Management, Information and Computer Science (ICEMC 2017)
PB  - Atlantis Press
SP  - 421
EP  - 424
SN  - 2352-538X
UR  - https://doi.org/10.2991/icemc-17.2017.83
DO  - https://doi.org/10.2991/icemc-17.2017.83
ID  - Ying2016/06
ER  -