Paper Title:
A Novel Approach to Quantifying Surface Roughness in Grinding
  Abstract

Surface roughness is an important quality characteristic in grinding. Measurement of surface roughness by means of mechanical stylus is widely done in metrology. In this paper, a new machine vision system has been utilized to quantify the surface roughness of machined surfaces (ground and milled). Compared with other measurement methods, it is accurate, quick and credible. This system is mounted on the grinding machine and automates the measurement process by using computer control to automatically position the CCD and capture digital images of machined surfaces between grinding cycles. It was proposed that the proportional formula was used in calibrating this system, and calibration precision meets application requirement. Not only the statistic character of gray image but also which of edge image were calculated out. These characters include the mean value of pixels (Mean), standard deviation (σ), maximal value (Max) and minimal value (Min), the number of pixels on the examine line(Count), etc. It was found out that the standard deviation value σ of the gray image could express the surface roughness most. The correlation between σ and Ra is established by interpolating σ value used Lagrange interpolation law, and the σ value is converted into Ra value through the calculation procedure finally.

  Info
Periodical
Edited by
Shen Dong and Yingxue Yao
Pages
147-151
DOI
10.4028/www.scientific.net/KEM.339.147
Citation
C. H. Ju, Y. Xie, "A Novel Approach to Quantifying Surface Roughness in Grinding", Key Engineering Materials, Vol. 339, pp. 147-151, 2007
Online since
May 2007
Authors
Export
Price
$32.00
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

Authors: Dian Yuan Han
Chapter 2: Advanced Manufacturing Systems and Equipment
Abstract:This paper concerns the plant leaf area measurement based on improved image processing. Firstly, the referenced rectangle was detected with...
624
Authors: Tong Qiang Li, Cai Feng Zheng, Jian Peng Gan
Chapter 8: Computer-Aided Design and Simulation
Abstract:By analysing the Mushroom image, the paper puts forward a kind of line-structure extraction algorithm combination of local gray value and...
1199
Authors: Li Zhou, Yu Zhong Li, Cheng Yong Wang
Chapter 14: Materials Machining
Abstract:The surface quality of graphite cannot be completely evaluated only by the roughness value Ra measured by profilometer. The surface damage...
2196
Authors: Bin He
Chapter 14: Signal Processing and Data Mining
Abstract:This paper presents a more accurate and faster method for sub-pixel TDICCD image registration. The method makes the best of the overlapping...
1610