Preliminary evaluation of a proximity transducer for the measurement of surface texture

Scott, W; Baul, R.M.

doi:10.1016/0043-1648(79)90137-6

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Surface Roughness Assessment 4131

Introduction1

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1979

2001

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Cited by 7 publications

(2 citation statements)

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“…Other non-contact optical proximity methods, which include lasers and fibre optics [5] and complicated Moir6 interferometric techniques [6] available for surface roughness measurement are hardware limited and require high-cost equipment.…”

Section: Surface Roughness Assessment 413mentioning

confidence: 99%

A vision system for surface roughness assessment using neural networks

Tsai

Chen

1998

Int J Adv Manuf Technol

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Section: Surface Roughness Assessment 413mentioning

confidence: 99%

A vision system for surface roughness assessment using neural networks

Tsai

Chen

1998

Int J Adv Manuf Technol

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“…Digital techniques and random process analysis, which have led to a signi® cant increase in the number of parameters, based on classical statistics, have been employed in surface measurement. Ritchards [2] and Scott and Baul [3] applied some of these parameters to image data.…”

Section: Introductionmentioning

confidence: 99%

Using the grey level histogram to distinguish between roughness of surfaces

Gadelmawla

Koura

Maksoud

et al. 2001

Proceedings of the Institution of Mechanical Engineers, Part B:

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In recent years, the advent of high-speed general-purpose digital computers and vision systems has made image analysis easier and more flexible. Images of surfaces captured using vision systems can be used to identify surface texture. In this paper, a new method, called the best grey level histogram (BGLH), is introduced to get the most accurate image of a surface for the purpose of surface texture assessment. A program was written using MS Visual C++ to calculate and display the grey level histogram for any grey image. The software is capable of converting the colour images to grey images. The program is capable of reading six different image file formats; these are BMP, TIFF, GIF, PCX, JPG and WMF. The conversion method depends on calculating three items from the grey levels of the image. These items are the minimum non-zero grey level, the maximum non-zero grey level and the number of used grey levels for the captured image. Many factors affect the quality of the captured image such as the brightness, contrast, saturation and hue. The BGLH method, introduced here, is used to select the best values for these factors to obtain the most accurate image for the surface using vision systems. The BGLH is also used to distinguish between roughness of different surfaces.

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Metrology and properties of engineering surfaces

Evans¹

1979

Precision Engineering

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Preliminary evaluation of a proximity transducer for the measurement of surface texture

Cited by 7 publications

References 2 publications

A vision system for surface roughness assessment using neural networks

A vision system for surface roughness assessment using neural networks

Using the grey level histogram to distinguish between roughness of surfaces

Metrology and properties of engineering surfaces

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