Surface roughness measurement based on image comparison

Dong, Zhiguo; Deng, Yong jun; Li, Yuanzong

doi:10.1109/cmce.2010.5609960

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…Histogram distribution parameters can be correlated with surface roughness. More sophisticated image processing for surface roughness analysis of various sample types, including solar cells, machined surfaces, and woven materials has been reported [26,27] Two other types of educational experiments are enabled or streamlined by digital cameras. Wetting experiments measure the wetting angle of liquid drops on a surface.…”

Section: Desktop Scannersmentioning

confidence: 99%

The Future Laboratory: Leveraging Consumer Imaging Devices for Student Projects and Sustainable, Accessible STEM Education.

Mauk

Chiou

Tseng

2019 ASEE Annual Conference &Amp; Exposition Proceedings

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He received his Ph.D. degree in the G.W. Woodruff School of Mechanical Engineering at Georgia Institute of Technology. His educational background is in manufacturing with an emphasis on mechatronics. In addition to his many years of industrial experience, he has taught many different engineering and technology courses at undergraduate and graduate levels. His tremendous research experience in manufacturing includes environmentally conscious manufacturing, Internet based robotics, and Web based quality. In the past years, he has been involved in sustainable manufacturing for maximizing energy and material recovery while minimizing environmental impact.

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Section: Desktop Scannersmentioning

confidence: 99%

The Future Laboratory: Leveraging Consumer Imaging Devices for Student Projects and Sustainable, Accessible STEM Education.

Mauk

Chiou

Tseng

2019 ASEE Annual Conference &Amp; Exposition Proceedings

View full text Add to dashboard Cite

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Evaluating surface roughness of castings using K-means clustering and watershed transform

Yang

Zhang

Liang

et al. 2011

2011 International Conference on Multimedia Technology

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Surface roughness is an important parameter for the machined parts such as grinding, milling, turning and casting. The conventional digital image processing methods have the capability of detecting the surface roughness of grinding, milling and turning parts. However, these methods can not be utilized to evaluate the surface roughness of castings in that there is no regular or periodic texture in the surface image of casting. Therefore, we proposed a simple but effective texture analysis method to evaluate surface roughness of castings in this paper. The proposed method is based on image partition and edge counting, which are implemented via K-means clustering or watershed transform. Several groups of experiments illustrated that the proposed method is feasible but promising as well.

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Surface roughness measurement based on image comparison

Cited by 2 publications

References 4 publications

The Future Laboratory: Leveraging Consumer Imaging Devices for Student Projects and Sustainable, Accessible STEM Education.

The Future Laboratory: Leveraging Consumer Imaging Devices for Student Projects and Sustainable, Accessible STEM Education.

Evaluating surface roughness of castings using K-means clustering and watershed transform

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