| WANG Chun-shui,HE Sheng-xin,ZHANG Er-liang,LI Da-lei.Analysis and Characterization of Sandblasted Surfaces Using Multi-scale Analysis[J],44(6):127-132 |
| Analysis and Characterization of Sandblasted Surfaces Using Multi-scale Analysis |
| Received:January 16, 2015 Revised:June 20, 2015 |
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| DOI：10.16490/j.cnki.issn.1001-3660.2015.06.024 |
| KeyWord:sandblasting 3D roughness multi-scale analysis regression analysis |
| Author | Institution |
| WANG Chun-shui |
School of Mechanical Engineering, Zhengzhou University, Zhengzhou , China |
| HE Sheng-xin |
School of Mechanical Engineering, Zhengzhou University, Zhengzhou , China |
| ZHANG Er-liang |
School of Mechanical Engineering, Zhengzhou University, Zhengzhou , China |
| LI Da-lei |
School of Mechanical Engineering, Zhengzhou University, Zhengzhou , China |
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| Abstract: |
| Objective To analyze and characterize the complicated surface topography of sandblasted surface, so as to obtain the 3D roughness parameter which is optimal in describing the influence of sandblasting parameters on surface topography. Methods The multi-scale analysis was conducted on the sandblasted surfaces of AISI 304L steel specimens, which were sandblasted at different distances as the processing parameter. Each surface was characterized using 12 common 3D roughness parameters at 5 evaluation scales. The variation trend of each roughness parameter against the evaluation scale was analyzed, meanwhile, the influence of process parameter (distance) on surface topography was considered, the linear regression model between the roughness parameters and the process parameter was established at an appropriate evaluation scale. Results The optimal evaluation scale of most roughness parameters was 80 μm, and a linear relationship was found to be pertinent in modeling Sku as a function of the distance at this evaluation scale, besides, the coefficient of determination of Sku was the biggest among the 12 roughness parameters. The number of high peaks and low valleys of the surface topography increased and the value of Sku increased with the increasing of sandblasting distance. Conclusion For this sandblasting experiment, the optimal evaluation scale was 80 μm, the parameter Sku was more promising than the widely used Sa and Sq as it collected more spatial information which allowed to better describing the effect of distance on the sandblasted topography. |
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