| GAN Zuo-kun,CAI Yao-jie,XU Xin-qi,HONG Tao,WEN Dong-hui.Processing Characteristics of Linear Hydrodynamic Polishing Waviness[J],51(6):336-345 |
| Processing Characteristics of Linear Hydrodynamic Polishing Waviness |
| |
| View Full Text View/Add Comment Download reader |
| DOI：10.16490/j.cnki.issn.1001-3660.2022.06.032 |
| KeyWord:linear hydrodynamic polishing waviness prediction model process parameters the Support Vector Regression |
| Author | Institution |
| GAN Zuo-kun |
School of Mechanical Engineering, Zhejiang University of Technology, Hangzhou , China |
| CAI Yao-jie |
School of Mechanical Engineering, Zhejiang University of Technology, Hangzhou , China |
| XU Xin-qi |
School of Mechanical Engineering, Zhejiang University of Technology, Hangzhou , China |
| HONG Tao |
School of Mechanical Engineering, Zhejiang University of Technology, Hangzhou , China |
| WEN Dong-hui |
School of Mechanical Engineering, Zhejiang University of Technology, Hangzhou , China |
|
| Hits: |
| Download times: |
| Abstract: |
| This paper aims to explore the characteristics of linear hydrodynamic polishing waviness, establish a polishing waviness prediction model, and obtain the best combination of processing parameters. The waviness generation mechanism was analyzed according to the principle of linear hydraulic pressure polishing, the relevant process parameters that affect the waviness was obtained according to the force distribution characteristics of the flow field. A single factor experiment was designed to explore the influence of each process parameter on the waviness and a significant analysis was conducted, the significant parameter was selected as the experimental factor to design an orthogonal experiment. A Waviness prediction model based on Support Vector Regression (SVR) was established with a training set composed of experimental result, the prediction model was used as the fitness function to perform genetic algorithm optimization to obtain the best process parameters. Linear hydrodynamic polishing waviness is produced by the combined action of the force distribution characteristics of the flow field and the feed motion of the workpiece. Its size is affected by the polishing gap, polishing speed, feed speed and polishing fluid viscosity. The single factor experimental analysis results show:The Wa value of polishing waviness increases with the increase of feed speed and polishing gap, decreases with the increase of polishing speed, and first decreases and then increases with the increase of polishing liquid viscosity. The influence of feed speed is the most significant, followed by polishing gap and polishing speed, while the influence of polishing liquid viscosity has segmental differences, and the significance is the weakest. The regression correlation coefficient R2 of the polishing waviness prediction model established by orthogonal experimental data is 0.992 0. The results of random verification experiments show that the errors between the predicted values and the true values of each group are within 10%. The optimal process parameters obtained by the genetic algorithm optimization are:(h0, u0, vf)=(50, 8, 200), the surface waviness of the workpiece after polishing for one hour is 5.23 nm. Reasonable selection of process parameters can optimize polishing waviness, and the SVR-based linear hydrodynamic polishing waviness prediction model has reliable predictive ability and can realize controlled processing of polishing waviness. |
| Close |
|
|
|