Prediction of Surface Roughness of U71Mn Steel Milling Based on RBF Neural Network
Vol. 18 (2022), Chemical Engineering
Vol. 18 (2022)

Prediction of Surface Roughness of U71Mn Steel Milling Based on RBF Neural Network

Chemical Engineering
Published 2022-10-17
Zhuang Shudong
College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, China
Yu Hang
College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, China
Liu Xiyu
College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, China
Jenny Sama Kevin
College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, China
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Keywords

U71Mn high manganese steel
orthogonal test
Surface Roughness
RBF Neural Network

How to Cite

Shudong, Z., Hang, Y., Xiyu, L., & Kevin, J. S. (2022). Prediction of Surface Roughness of U71Mn Steel Milling Based on RBF Neural Network. Journal of Basic & Applied Sciences, 18, 65–71. Retrieved from http://set-publisher.com/index.php/jbas/article/view/2404
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Abstract

In order to predict the surface roughness of U71Mn high manganese steel before actual milling operation, an orthogonal experiment was designed. Based on the intelligent algorithm of Radial Basis Functions (RBF) neural network, an accurate prediction model of surface roughness is done with MATLAB. By comparing the predicted data of RBF neural network model with the actual measured data, it is proven that the model is accurate and effective.

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References

Chuangbo M. The effect of rail grinding on wheel-rail interaction[J]. Railway Standard Design 2002; (07): 31-32.

Chunlin W, Beibei H, Yiming F, et al. Multi-objective optimization of double vane pump based on radial basis function neural network and particle swarm optimization [J]. Journal of Agricultural Engineering 2019; 35(02): 25-32.

Shuaizhe W, Jinmei W, Yongqi W, et al. Short-term wind power prediction based on PSO-ICA-BP neural network [J]. Electrical and Electrical 2019; (02): 7-11.

Moody JE, Darken CJ. Fast learning in networks of locally-tuned processing units. Neural Computation 1991; 3(4): 579-581.

Junjun C, Qinghua C. Application of Radial Basis Function Neural Network in Surface Roughness Prediction of High Speed Milling [J]. Combination Machine Tool and Automation Machining Technology 2013; (06): 6-8.

Xianjin T, Zhangqiu, Gang Z, et al. Prediction of machined surface roughness of polymer-bonded Explosives Based on radial basis function neural network [J]. Journal of Military Engineering 2014; 35(02): 200-206.

Tatar A, Nasery S, Bahadori A, et al. Implementing radial basis function neural network for prediction of surfactant retention in petroleum production and processing industries[J]. Liquid Fuels Technology 2016; 34(11-12): 992-999. https://doi.org/10.1080/10916466.2016.1177548

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