| ZHU Chuan-min,GU Peng,LIU Ding-hao,WU Yin-yue.Surface Quality Prediction of SiCp/Al Composite in Grinding Based on Support Vector Machine[J],48(3):240-248 |
| Surface Quality Prediction of SiCp/Al Composite in Grinding Based on Support Vector Machine |
| Received:October 15, 2018 Revised:March 20, 2019 |
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| DOI：10.16490/j.cnki.issn.1001-3660.2019.03.033 |
| KeyWord:aluminum-based silicon carbide grinding process surface topography cavity defect average deviation support vector machine |
| Author | Institution |
| ZHU Chuan-min |
School of Mechanical Engineering, Tongji University, Shanghai , China |
| GU Peng |
School of Mechanical Engineering, Tongji University, Shanghai , China |
| LIU Ding-hao |
School of Mechanical Engineering, Tongji University, Shanghai , China |
| WU Yin-yue |
School of Mechanical Engineering, Tongji University, Shanghai , China |
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| Abstract: |
| The work aims to propose the modified surface roughness evaluation indexes based on topography and the predic-tion model since the traditional surface roughness indexes have limitations on the evaluation of cavity defect in the grinding process of SiCp/Al composite. The contour map was constructed based on surface topography to obtain the relationship curve between the projection area of adjacent contour and contour height. The average deviation of whole topography to the sampling area and the maximum deviation height index of cavity defect were proposed to characterize grinding surface quality including cavity defect. Based on Support Vector Machine (SVM), the two prediction models of average deviation of topography and the maximum devia-tion height of cavity were proposed and optimized and the influence of laws of grinding parameters on evaluation index was ana-lyzed. The evaluation indexes of the average deviation of whole topography and the maximum deviation height of cavity defect in-cluded more surface features. The errors between the predicted and experimental results were both within 5% and decreased with the increase of wheel speed, and increased with the increase of feed speed and grinding depth. It is reasonable to use the arithmetic average deviation of topography and the maximum deviation height of cavity to evaluate the grinding surface quality including pit defects, and the measurement and determination method of evaluation index is feasible and effective. The prediction method of evaluation index based on support vector machine is correct, which lays a foundation for the evaluation of grinding surface quality of aluminum-based silicon carbide and the study of application performance. |
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