| PAN Jie,CHEN Fan,YANG Wei,JIN Wen-da.Adaptive Polishing Process Parameter Matching Based on SPSO-BP Neural Network[J],51(8):387-399 |
| Adaptive Polishing Process Parameter Matching Based on SPSO-BP Neural Network |
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| DOI：10.16490/j.cnki.issn.1001-3660.2022.08.035 |
| KeyWord:polish material removal rate surface roughness SPSO neural networks predictive model process parameters self-adaptive |
| Author | Institution |
| PAN Jie |
HUST-Wuxi Research Institute, Jiangsu Wuxi , China |
| CHEN Fan |
HUST-Wuxi Research Institute, Jiangsu Wuxi , China;School of Mechanical Science & Engineering, Huazhong University of Science and Technology, Wuhan , China |
| YANG Wei |
Jiangsu Jitri-Hust Intelligent Equipment Technology Co., Ltd., Jiangsu Wuxi , China |
| JIN Wen-da |
Jiangsu Jitri-Hust Intelligent Equipment Technology Co., Ltd., Jiangsu Wuxi , China |
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| Abstract: |
| With the development of science and technology, the requirements for the surface roughness value and precision polishing efficiency of key parts in the fields of aviation, aerospace, national defense, and medical treatment were getting stricter. The wet physical polishing method can reduce the deformation of the material during the polishing process and obtain a lower surface roughness value. When testing the polishing process parameters, it was necessary to manually select the polishing process parameters, observe the polishing results, and repeatedly adjust the process parameters based on experience to achieve the desired polishing effect. The test process required a lot of time and energy, relying on people's subjective experience to adjust the parameters, the accumulated knowledge and experience were difficult to transfer among different operators. The surface roughness and material removal rate are usually measured after the parts are polished, when the test does not meet the requirements, it often leads to scrapped parts. This paper aims to achieve the self-adaptive matching of polishing parameters according to the requirements of different workpiece surface polishing quality and efficiency, and endeavors to achieve the ideal polishing effect. Based on the principle of material removal on the surface of the workpiece, this paper established a mathematical model of the relationship between process parameters, material removal rate and surface roughness value, and the process parameters that affected the polishing effect was clarified. Aimed at the complex and interactive relationship between process parameters and polishing quality and efficiency, as well as the difference between the theoretically calculated polishing effect and the actual result, the SPSO-BP prediction model was proposed. 20 sets of different polishing process parameters and corresponding polishing results were taken as training samples. The SPSO-BP model was trained with the samples and compared with the traditional PSO-BP model. Based on the trained prediction model, the polishing process parameters are adaptively matched through the model according to different basic conditions, polishing quality and polishing efficiency requirements. For SUS304 plates, the surface roughness value targets Ra1-Ra5 and the material removal rate targets Rm1-Rm5 were set. Moreover, the process parameters in the SPSO-BP and PSO-BP models were predicted, then polishing test was performed. The actual roughness values Raz1-Raz5 and the material removal rate Rmz1-Rmz5 were achieved, compared and verified with the target values. Compared with the PSO-BP prediction model, the SPSO-BP prediction model had higher convergence accuracy. The convergence accuracy of the SPSO-BP and PSO-BP were 1.26×10−6 and 0.180 respectively, and the SPSO-BP model has good tracking ability and generalization ability for samples. The real roughness value Raz and the real material removal rate Rmz obtained by the SPSO-BP prediction model were closer to the target value than the PSO-BP prediction model. The maximum error ratios of the real roughness value Raz and the target value Ra obtained by the SPSO-BP and PSO-BP prediction models were 8.00% and 20.00%, the average error ratios were 5.77% and 14.07%, and the minimum error ratios were 2.50% and 10.00%; the maximum error ratios of the true material removal rate Rmz and the target value Rm are 3.00% and 8.57%, the average error ratios were 2.14% and 7.46%, and the minimum error ratios were:1.11% and 4.38%. According to different basic conditions, polishing quality and polishing efficiency requirements, the SPSO-BP prediction model can be used to adaptively match the polishing process parameters. In comparison with the traditional PSO-BP prediction model, it had higher convergence accuracy, which can achieve a more closer real polishing result to the target requirement. |
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