Surface Roughness Prediction of Screw Belt Grinding Based on Improved Neural Network Algorithm

被引：0
|
作者：
Dong H.-S. [1 ,2 ]
Yang H.-R. [1 ,2 ]
Sun X.-W. [1 ,2 ]
Dong Z.-X. [1 ,2 ]
Liu Y. [1 ,2 ]
机构：
[1] College of Mechanical Engineering, Shenyang University of Technology, Shenyang
[2] Key Laboratory of Numerical Control Manufacturing Technology for Complex Surfaces of Liaoning Province, Shenyang University of Technology, Shenyang
来源：
Surface Technology | 2022年 / 51卷 / 04期
基金：
中国国家自然科学基金;
关键词：
grinding; neural network prediction; sparrow search algorithm; surface roughness;
D O I：
10.16490/j.cnki.issn.1001-3660.2022.04.028
中图分类号：
学科分类号：
摘要：
This paper aims to explore the influence of process parameters on the surface quality of screw rotor abrasive belt grinding. The orthogonal experiment of screw rotor abrasive belt grinding is carried out for the axial feed speed of workpiece is 100~300 mm/min, the linear speed of abrasive belt is 4.4~13.1 m/s, the tension pressure of abrasive belt is 0.2~0.3 MPa, the grinding pressure is 0.4~0.5 MPa and the mesh number of abrasive belt is 120~800. Based on the improved neural network algorithm, the prediction model of surface roughness value after screw rotor abrasive belt grinding is established to predict and analyze the surface quality of the workpiece after grinding. On this basis, the influence of process parameters on grinding quality is predicted and analyzed by using the prediction model. Using multi-factor grinding experiments to obtain prediction samples and comparison samples, the comparison results show that the average training accuracy of the prediction model is about 93.38% and the prediction accuracy is 92.46%. The single factor prediction results of screw rotor abrasive belt grinding surface roughness value show that the workpiece surface roughness value increases with the increase of contact wheel positive pressure and axial feed speed of the grinding device, and decreases with the increase of abrasive belt linear speed and abrasive belt mesh. It can be seen from the above research results, the proposed algorithm can provide a theoretical basis for the selection of process parameters of screw rotor abrasive belt grinding. Higher surface quality can be obtained by appropriately increasing the linear speed and mesh number of the abrasive belts, reducing the cylinder pressure of the contact wheel and the axial feed speed of the grinding device. © 2022, Chongqing Wujiu Periodicals Press. All rights reserved.
引用
收藏
页码：275 / 283
页数：8
相关论文
共 26 条
  • [1] CHEN Guan-feng, CHEN Song-ping, CAI Si-jie, A Clearance Arrangement Method for Screw Rotors of Twin-Screw Compressors Based on Discrete Pixel Method, China Mechanical Engineering, 31, 24, pp. 2937-2942, (2020)
  • [2] LIU Xing-zhou, LIU Ming-kun, CHAI Xiao-tong, Et al., Machining with Numerical Control Lathe and Clearance Design of Fixed Pitch Screw Rotor, Chinese Journal of Vacuum Science and Technology, 40, 2, pp. 169-173, (2020)
  • [3] MICHAL B, MICHAEL B., Manufacturing of Screw Rotors via 5-Axis Double-Flank CNC Machining, Com-puter-Aided Design, 132, (2020)
  • [4] HUANG Yun, LIU Shuai, HUANG Tao, Et al., Model of Residual Stress Formation on Belt Grinding Surface of Titanium Alloy and Experimental Research, Surface Technology, 49, 4, pp. 30-37, (2020)
  • [5] WANG Guang-chun, HAN Juan-juan, LIN Yao, Et al., Investigation on Size Effect of Surface Roughness and Establishment of Prediction Model in Micro-Forming Process, Materials Today Communications, 27, (2021)
  • [6] ZHANG Wen-he, Surface Roughness Prediction with Machine Learning, Journal of Physics: Conference Series, 1856, 1, (2021)
  • [7] BAROIU N, COSTIN G A, TEODOR V G, Et al., Prediction of Surface Roughness in Drilling of Polymers Using a Geometrical Model and Artificial Neural Networks, Materiale Plastice, 57, 3, pp. 160-173, (2020)
  • [8] YANG He-ran, HE Yuan, SUN Xing-wei, Et al., Development of Belt Grinding Devices for Screw Rotor and Prediction of Material Removal Rates, China Mechanical Engineering, 32, 17, pp. 2055-2062, (2021)
  • [9] LIU Yao, ZHOU Wen-wen, QUAN Yu, Modeling of Ground Surface Roughness Based on the Grinding Wheel Tomography, Modular Machine Tool & Automatic Manufacturing Technique, 12, pp. 149-152, (2020)
  • [10] ZHANG Fei-hu, LI Chen, ZHAO Hang, Et al., Prediction Model and Experimental Study of Subsurface Damage Depths in Grinding for K9 Glasses, China Mechanical Engineering, 27, 18, pp. 2442-2446, (2016)
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