| DONG Hao-sheng,YANG He-ran,SUN Xing-wei,DONG Zhi-xu,LIU Yin.Surface Roughness Prediction of Screw Belt Grinding Based on Improved Neural Network Algorithm[J],51(4):275-283 |
| Surface Roughness Prediction of Screw Belt Grinding Based on Improved Neural Network Algorithm |
| Received:May 08, 2021 Revised:September 30, 2021 |
| View Full Text View/Add Comment Download reader |
| DOI：10.16490/j.cnki.issn.1001-3660.2022.04.028 |
| KeyWord:grinding surface roughness sparrow search algorithm neural network prediction |
| Author | Institution |
| DONG Hao-sheng |
College of Mechanical Engineering,Shenyang , China ;Key Laboratory of Numerical Control Manufacturing Technology for Complex Surfaces of Liaoning Province, Shenyang University of Technology, Shenyang , China |
| YANG He-ran |
College of Mechanical Engineering,Shenyang , China ;Key Laboratory of Numerical Control Manufacturing Technology for Complex Surfaces of Liaoning Province, Shenyang University of Technology, Shenyang , China |
| SUN Xing-wei |
College of Mechanical Engineering,Shenyang , China ;Key Laboratory of Numerical Control Manufacturing Technology for Complex Surfaces of Liaoning Province, Shenyang University of Technology, Shenyang , China |
| DONG Zhi-xu |
College of Mechanical Engineering,Shenyang , China ;Key Laboratory of Numerical Control Manufacturing Technology for Complex Surfaces of Liaoning Province, Shenyang University of Technology, Shenyang , China |
| LIU Yin |
College of Mechanical Engineering,Shenyang , China ;Key Laboratory of Numerical Control Manufacturing Technology for Complex Surfaces of Liaoning Province, Shenyang University of Technology, Shenyang , China |
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| Abstract: |
| 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. |
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