Analysis of speckle images to assess surface roughness

Hamed, A. A.; El-Ghandoor, H.; El-Diasty, Fouad; Saudy, M. A.

doi:10.1016/j.optlastec.2003.09.005

Cited by 49 publications

(31 citation statements)

References 31 publications

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“…Several experiments were performed to collect the surface roughness values on MANFORD CNC milling machine controlled with Fanuc i-MC series under wet conditions. The CK45 steel material samples had the dimensions of 40x40x40 mm 3 . The surfaces were measured by MAHR Perthometer S4P roughness tester.…”

Section: Experimental Set-upmentioning

confidence: 99%

“…Nevertheless, the surface roughness measurements based on the stylus instrument have some drawbacks such as possible surface damages, long measuring times and difficulty of in-situ measurement process. These drawbacks have prompted the development of alternative techniques including optical methods such as optical interference and light scattering techniques for the surface roughness measurement as non-contact optical methods leading to non-destructive, fast and continuous measurements [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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Autocorrelation analysis of spectral dependency of surface roughness speckle patterns

Kayahan

Gündoğdu

Hacizade

et al. 2009

2009 International Symposium on Optomechatronic Technologies

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When a surface is illuminated with a highly coherent light such as a laser beam, the speckle pattern of bright and dark regions is observed. It depends on the surface parameters and carries important information about the roughness of the surface. Various methods and techniques are employed for the determination of surface roughness parameters from speckle pattern properties. In this paper, an experimental approach for surface roughness evaluation based on the autocorrelation analysis of the spectral properties of speckle patterns caused by milled metal surfaces is reported. The speckles at three 633, 604 and 543 nm wavelengths of He-Ne laser were analyzed. It was found that autocorrelation analysis is very sensitive to small variations in speckle sizes, caused by spectral properties of speckle patterns such as increasing the wavelength lead to increased speckle sizes. The results are in good agreement with the results obtained from the mechanical stylus profilometer for the milled metal surfaces with roughness values R a ; 0.36µm (low roughness) and 1.98µm (high roughness). The technique reported here has a great potential for precise and non-contact optical measurements of rough surfaces.

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Section: Experimental Set-upmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Autocorrelation analysis of spectral dependency of surface roughness speckle patterns

Kayahan

Gündoğdu

Hacizade

et al. 2009

2009 International Symposium on Optomechatronic Technologies

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“…Several studies have been performed to inspect the surface roughness of a part based on image processing. These introduced techniques include light scattering [3][4][5][6], laser speckle [7][8][9], and several types of texture analysis [10,11,12].…”

Section: Introductionmentioning

confidence: 99%

Surface roughness inspection in milling operations with photometric stereo and PNN

Kaya

2015

Int J Adv Manuf Technol

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Offline human-assisted surface quality inspection techniques are time-consuming and need labor intensive setup requirements that are sometimes unacceptable due to their slowing down of manufacturing productivity. This paper proposes an efficient surface roughness inspection method which uses photometric stereo and probabilistic neural network (PNN). A photometric stereo scheme with six images was used to generate a 3D depth map of the machined surface. Probabilistic neural network and statistical features extracted from the depth map were employed for the classification of surface roughness quality. The method was validated on several machined surfaces with different surface roughnesses. The satisfying accuracy of the introduced method demonstrates the feasibility of the proposed system.

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“…In another interesting work [3], an experimental approach for surface roughness measurement based on the coherent speckle scattering pattern caused by a laser beam on machined surfaces (grinding and milling) was developed. Studies of metallic rough surfaces were made using digitalized speckle pattern [4]. A correlation technique is also provided [5] to analyze full-field surface roughness; in this case the measurement is made by means of laser speckle determination.…”

Section: Introductionmentioning

confidence: 99%

Hurst exponent determination for digital speckle patterns in roughness control of metallic surfaces

Sampaio

Lobão

Nunes³

et al. 2011

Optics and Lasers in Engineering

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Analysis of speckle images to assess surface roughness

Cited by 49 publications

References 31 publications

Autocorrelation analysis of spectral dependency of surface roughness speckle patterns

Autocorrelation analysis of spectral dependency of surface roughness speckle patterns

Surface roughness inspection in milling operations with photometric stereo and PNN

Hurst exponent determination for digital speckle patterns in roughness control of metallic surfaces

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