Research on Surface Roughness Prediction of Turning Parts Based on BP Artificial Neural Network

DOI

10.2991/ifmeita-17.2018.23How to use a DOI?

Keywords

BP Artificial Neural Network,Surface Roughness Prediction,Levenberg-Marquardt algorithm

Abstract

Surface roughness of parts is an important index for the quality of processing. An accurate and efficient model for surface roughness prediction can provide a reliable constraint or objective function for the processing parameter optimization. In the part design and actual processing, the surface roughness value (Ra) is taken from the national standard specification series, these series can be regarded as the corresponding category. Therefore, in this research, a new method is presented to predict the surface roughness by classifying the Ra under different cutting conditions. The effects of network structure and learning algorithm on the prediction results were discussed. Finally, a 5-3-3-3 BP Artificial Neural Network (ANN) structure with Levenberg-Marquardt (LM) algorithm were used to build the model. The results show that the prediction accuracy of the model was as high as 97.44%, and the surface roughness value of the turning parts can be predicted well.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Ping Wang
AU  - Hui Zhang
AU  - Peiqing Ye
AU  - Tong Zhao
AU  - Qi Sun
PY  - 2018/02
DA  - 2018/02
TI  - Research on Surface Roughness Prediction of Turning Parts Based on BP Artificial Neural Network
BT  - Proceedings of the 2nd International Forum on Management, Education and Information Technology Application (IFMEITA 2017)
PB  - Atlantis Press
SP  - 133
EP  - 138
SN  - 2352-5398
UR  - https://doi.org/10.2991/ifmeita-17.2018.23
DO  - 10.2991/ifmeita-17.2018.23
ID  - Wang2018/02
ER  -