| LIU Tao,ZHANG Wen-chao,ZHANG Wen-shuai.Double-predictive Model of Tooth Surface Roughness of Variable-section Scroll[J],48(8):323-329 |
| Double-predictive Model of Tooth Surface Roughness of Variable-section Scroll |
| Received:March 26, 2018 Revised:August 20, 2019 |
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| DOI：10.16490/j.cnki.issn.1001-3660.2019.08.043 |
| KeyWord:variable cross section scroll disk roughness orthogonal test multiple regression model BP neural network double prediction model milling parameters |
| Author | Institution |
| LIU Tao |
School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou , China |
| ZHANG Wen-chao |
School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou , China |
| ZHANG Wen-shuai |
School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou , China |
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| Abstract: |
| The work aims to accurately predict the tooth surface roughness of the three-section base-circular variable-section scroll, determine the reasonable milling parameters and improve the machining quality of the variable-section scroll. Firstly, under the orthogonal test milling parameters, the blanks were milled by XK714 CNC milling machine to obtain three-section circular-variable scrolls, and the side roughness of the processed scrolls was measured by SJ-210 surface roughness measuring instrument. Then, milling parameters and measured roughness values were adopted to establish a multivariate regression prediction model of the tooth surface roughness, the improved BP neural network prediction model and bi-predictive model and verify the accuracy of the three models. Finally, the roughness under single factor was predicted and analyzed. Through calculation, the average error of the multivariate regression prediction model for tooth surface roughness was 1.43% and the maximum error was 3.09%, while the average error of the improved BP neural network prediction model was 1.33%, and the maximum error was 3.22%. When the average value was used as the double prediction model, the predicted average error was 0.627% and the maximum error was 1.51%. Therefore, the average error of the double prediction model of the tooth surface roughness is significantly reduced, and the subjective prediction error can be avoided by the single prediction model. Influence degree of each milling factor on the roughness is different, i.e. the feed rate fz > the knife depth ap > the tool speed n>side knife amount ae. As the feed amount, knife depth, and amount of side knife increase, the tooth surface roughness value increases, but the tooth surface roughness value decreases as the tool speed increases. |
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