Surface roughness measurement through a speckle method (original) (raw)
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Optics and Lasers in Engineering, 2010
Quantification of surface roughness greater than a micron is desirable for many industrial and biomedical applications. Polychromatic speckle contrast has been shown theoretically to be able to detect such roughness range using an appropriate light source with a Gaussian spectral shape. In this paper, we extend the theory to arbitrary spectral profile by formulating speckle contrast as a function of spectral profile, surface roughness, and the geometry of speckle formation. Under a far-field set-up, the formulation can be simplified and a calibration curve for contrast and roughness can be calculated. We demonstrated the technique using a blue diode laser with a set of 20 metal surface roughness standards in the range 1-73 mm, and found that the method worked well with both Gaussian and non-Gaussian surfaces.
Evaluation of surface roughness based on monochromatic speckle correlation using image processing
Precision …, 2008
The measurement of roughness on machined surfaces is of great importance for manufacturing industries as the roughness of a surface has a considerable influence on its quality and function of products. In this paper, an experimental approach for surface roughness measurement based on the coherent speckle scattering pattern caused by a laser beam on the machined surfaces (grinding and milling) is presented. Speckle is the random pattern of bright and dark regions that is observed when a surface is illuminated with a highly or partially coherent light beam. When the illuminating beam is reflected from a surface, the optical path difference between various wavelets with different wavelength would result in interference showing up as a granular pattern of intensity termed as speckle. The properties of this speckle pattern are used for estimation/quantification of roughness parameters. For measurement of surface roughness, initially the speckle patterns formed are filtered in the spatial frequency domain. The optical technique, namely spectral speckle correlation (autocorrelation) is utilized in this work for the measurement of roughness on machined surfaces. It has been observed that the pattern formed is dependent on the roughness of the illuminated surface. For example, a rough surface (milled) shows a small central bright region with a rapid decrease in intensity towards the edges, while a smooth surface (ground) shows a large central bright region with gradually decreasing intensity towards the edges. The complete methodology and analysis for quantification/estimation of surface finish of milled and ground surfaces based on speckle images that could be implemented in practice, is presented in this paper.
Surface roughness measurements by means of speckle wavelength decorrelation
Optics Communications, 1979
We exploit speckle wavelength decorrelation techniques for the characterization of surface roughness up to a few microns. The experimental setup includes an argon laser, a grating, two photodetectors, and a commerical cross correlator. Experimental results are compared with data obtained with a stylus instrument. Fair agreement is observed.
Characterizing surface roughness by speckle pattern analysis
2009
Speckle photography is a non-destructive technique for making moderate sensitivity measurements for strain, rotation, vibration, plane displacements, and surface texture. This paper presents characterization of surface roughness by studying speckle patterns correlation and visibility during object displacement.
Surface roughness measurement on machined surfaces using angular speckle correlation
Journal of Materials Processing Technology, 2006
Surface roughness is of great importance in fields, such as tribology, semiconductor technology and medicine. Stylus techniques, in which a stylus is drawn along the surface and the vertical movement of the stylus is recorded, have been used traditionally in measuring surface roughness. Non-contact methods, such as optical ones, have the advantage that they can be used for the in-process measurement of surface roughness. In this paper, results of the measurement of surface roughness using angular speckle-correlation on machined surfaces are presented. Surfaces of approximately 1.6 < R a < 6.3 m have been measured, the surfaces being classified in the same manner as when using a stylus instrument. ASC is a technique that also allows in-process measurement of the roughness of surfaces on machined surfaces. A new technique to achieve increased repeatability by using an angle detection unit is also presented in this paper.
Different methods for characterizing surface roughness using laser speckle technique
Iraqi Journal of Physics, 2019
In this work, results from an optical technique (laser speckle technique) for measuring surface roughness was done by using statistical properties of speckle pattern from the point of view of computer image texture analysis. Four calibration relationships were used to cover wide range of measurement with the same laser speckle technique. The first one is based on intensity contrast of the speckle, the second is based on analysis of speckle binary image, the third is on size of speckle pattern spot, and the latest one is based on characterization of the energy feature of the gray level co-occurrence matrices for the speckle pattern. By these calibration relationships surface roughness of an object surface can be evaluated within these relations ranges from single speckle pattern image which was taken from the surface.
Autocorrelation analysis of spectral dependency of surface roughness speckle patterns
2009
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 Ra; 0.36 mum (low roughness) and 1.98 mum (high roughness). The technique reported here has a great potential for precise and non-contact optical measurements of rough surfaces.
Analysis of speckle images to assess surface roughness
Optics & Laser Technology, 2004
Digital speckle images are photographed for di erent aluminum rough surfaces using a CCD camera. The obtained speckle images are fed to a PC and analyzed making use of the MATLAB program. The computerized binary images are investigated. The signal-to-noise ratio is computed from these numerical images. It is shown that the surface roughness of the examined surfaces is dependent upon the degree of agglomeration of the speckle images.