Manufacturing Technology 2021, 21(4):456-463 | DOI: 10.21062/mft.2021.053

Comparison on Milling Force Model Prediction of New Cold Saw Blade Milling Cutter Based on Deep Neural Network and Regression Analysis

Shuailiang Guo ORCID...1, Han Zheng ORCID...2, Xiangzeng Liu ORCID...2, Lizhi Gu ORCID...1
1 College of Mechanical Engineering and Automation, National Huaqiao University, Xiamen, 361021. China
2 School of Computer Science and Technology at Xidian University, Xi’an, 710071. China

A four factors and three levels orthogonal milling force (MF) test is designed, which qualitatively obtains the influence of four factors, namely workpiece material, workpiece diameter, milling speed and feed per tooth, on MF of the new cold saw blade milling cutter (NCSBMC), then further verifies the reliability of test data with simulation analysis of MF. The multiple linear regression analysis and deep neural network (DNN) are used to accurately fit and predict the magnitude of MF in three directions of NCSBMC, taking into account the influence of workpiece material factors on MF. Compared with the results of empirical formula, DNN has higher prediction accuracy. The research results provide theoretical guidance for the optimization of milling parameters in actual machining process.

Keywords: NCSBMC,MF, Orthogonal Test, Multiple Linear Regression Analysis, DNN
Grants and funding:

The research was financially supported by the National International Scientific and Technological Cooperation Special with Granted No. 2018DFR50520 and by Fujian Provincial Key Project of Science and Technology with Granted No. 2019H0034.

Received: December 16, 2020; Revised: April 7, 2021; Accepted: May 5, 2021; Prepublished online: July 4, 2021; Published: September 18, 2021  Show citation

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Guo S, Zheng H, Liu X, Gu L. Comparison on Milling Force Model Prediction of New Cold Saw Blade Milling Cutter Based on Deep Neural Network and Regression Analysis. Manufacturing Technology. 2021;21(4):456-463. doi: 10.21062/mft.2021.053.
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