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描述表面粗糙度的新方法及在线检测工件粗糙度的试验研究
A new method to characterize surface roughness and experimental study on online testing of workpiece roughness
投稿时间: 2017-03-17  最后修改时间: 2017-05-23
DOI:
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 testing  turning  high speed camera
基金项目:湖北省教育厅重点项目(D20142603)
作者单位E-mail
李业学 襄樊学院 liyexue.student@sina.com 
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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,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。通过拍摄铸铁工件在车削中的侧表面,依据所提计算方法,依图片灰度换算值构建工件灰度表面,提取表面三维坐标,计算出工件车削过程中不同时刻的灰度图像自仿射维数,验证灰度图像自仿射维数理论的可行性。在提出灰度图像自仿射维数理论的基础上,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,评估在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背吃刀量增加而增大,这些结论与传统检测技术所得结论是一致的。上述一致充分表明: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:
      Interdisciplinary theories on fractal geometry、graphics and iconography and mechanics are employed to present gray image self-affine dimension to characterize the roughness of workpiece surface by fractal Brownian function. Based on picture obtained by shooting Side surface of cast iron workpiece and the method proposed in the paper, conversion of actual height and gray degree of workpiece gray surface is applied to construct gray surface. 3D coordinates are acquired. And then gray image self-affine dimensions in different time during the turning are calculated to verify the feasibility of gray image self-affine dimension theory. On the base of presenting gray image self-affine dimension theory, high speed camera is used to shoot turning real-time process. Workpiece surface roughness in different times described by gray image self-affine dimension are calculated. Forwardly new technique that can realize online testing on workpiece surface roughness is proposed. It is indicated, (1) there are clear cut marks and textures on the surface of workpiece picture, but their rough degree are low. And self-affine dimension of corresponding gray image are also little. It is same with the definition of fractal dimension used to describe the surface roughness. (2) When workpiece is initially unstable, self-affine dimension of its gray image is comparatively big. Gray image self-affine dimension increases with the increasing of back cutting depth. These conclusions are coincident with the ones drawn by traditional testing techniques. Therefore, calculation theory on gray image self-affine dimension presented in the paper is correct. Online testing technique proposed by the theory is also applicable and feasible.
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