Multi-dimensional assessment of nano/micro scale surface texture using laser speckle pattern analysis

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Abstract Nano/micro scale surface structure on precision machined parts dominates the various product functions such as friction, wear resistance, corrosion, fatigue, and wetting. This paper presents a multi-dimensional surface texture assessment using laser speckle pattern analysis. From the observation result of laser speckle in precision machined surface, the probability density distribution of light intensity on laser speckle changes with the surface roughness. In addition, the autocorrelation function of speckle intensity distribution changes with the periodicity of micro surface structure. By analyzing the relation between surface texture and laser speckle pattern, characteristic parameters of laser speckle reflecting surface texture properties are identified; i.e. the mean light intensity, the deviation of the probability density distribution of light intensity, the autocorrelation length of intensity distribution, and the degree of dissimilarity between adjacent speckle patterns. The proposed parameters can evaluate the roughness, the period, and the degree of dissimilarity in periodic micro surface structure. Furthermore, the surface texture distribution and the anisotropy of micro surface structure can be assessed by detecting the variation of the proposed parameters. In consequence, the proposed method can quantitatively assess the nano/micro scale surface texture.