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| 工件粗糙度在线检测的试验研究 |
| Experimental study on online detection of workpiece roughness |
| Received:March 17, 2017 Revised:May 23, 2017 |
| DOI：10.7520/1001-4888-17-065 |
| 中文关键词: 粗糙度 灰度图像自仿射维数 在线检测 车削 高速摄像机 |
| 英文关键词:roughness gray image self-affine dimension on-line detection turning high-speed video camera |
| 基金项目:湖北省教育厅重点项目(D20172601)资助 |
| Author Name | Affiliation | | LI Ye-xue* | School of materials, Hubei University of Science and arts, Xiangyang 410053, Hubei, China | | ZHANG Xue-lin | School of Materials Science and Engineering, Nanyang Technological University, Singapore 639798 | | XU Fu-wei | School of materials, Hubei University of Science and arts, Xiangyang 410053, Hubei, China | | LI Chang-jiang | School of materials, Hubei University of Science and arts, Xiangyang 410053, Hubei, China |
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| 中文摘要: |
| 采用分形几何与图形图像学等交叉学科理论，基于分形布朗函数，本文提出了用于描述工件表面粗糙度的灰度图像自仿射维数计算新方法。在提出上述理论的基础上，采用高速摄像机拍摄工件车削的实时过程，计算工件的灰度图像自仿射维数，评估不同时刻工件表面粗糙度变化，进而提出可实现在线检测工件表面粗糙度的新技术。研究显示：(1)工件表面图像揭示了其表面存在清晰可见的切痕和纹理，但粗糙度不大，同时计算出的对应灰度图像自仿射维数较小，这与描述表面粗糙度的维数定义是吻合的，因而，灰度图像自仿射维数能很好地描述工件表面的粗糙度特征。(2)在初始车削不稳定状态时，灰度图像自仿射维数较大,灰度图像自仿射维数随着进刀量增加而增大，这些结论与传统检测技术所得结论是一致的，由此充分表明，本文所提的灰度图像自仿射维数计算理论是正确的，基于该理论所提的在线检测技术也是实用可行的。 |
| 英文摘要: |
| Adopting interdisciplinary theory of fractal geometry and graphics and iconography, based on fractal Brown function, a new method for self affine dimension calculation of gray-scale image is proposed in this paper for describing the surface roughness of workpiece. On the basis of above theory, the real time process of workpiece turning was recorded by high-speed vedio camera; the self-affine dimension of workpiece gray-scale image was calculated; the variation of workpiece surface roughness at different time was evaluated; and then, a new technology to on-line detect workpiece surface roughnessis was proposed. Results show that (1) the images reveal that there are clearly visible cut marks and textures on workpiece surface, but the roughness is not very obvious. At the same time, the self affine dimension of corresponding gray image is smaller, which is consistent with the definition of dimension describing surface roughness. Therefore, the self-affine dimension of gray image can well describe the roughness characteristics of workpiece surface. (2) The self-affine dimension of gray-scale image is larger in the initial turning unstable state, and dimension increases with the increase of knife feeding amount, which is consistent with those obtained by traditional detection techniques. Above results show that the proposed theory of calculating the self-affine dimension of gray image is correct, and the on-line detection technique based on the theory is also practical and feasible. |
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