Computer Vision Based Non-contact Surface Roughness Assessment Using Wavelet Transform and Response Surface Methodology

This paper describes a new non-contact measurement approach in characterizing manufactured surfaces. Computer vision is applied to capture digital images of three types of anisotropic steel specimen surfaces from shaping, grinding, and polishing processes. Multiresolution wavelet decomposition is used to obtain signatures of surface profiles from the digital images. Relationships between these signatures and surface roughness parameters (Ra and Rq) are built by response surface methodology (RSM). The proposed models thus developed are suitable for predicting roughness in terms of the roughness parameters. Experimental results show that the proposed approach successfully correlates wavelet signals to Ra and Rq values. In addition, they also show repeatable gage capabilities. The proposed method is a good candidate for on-line, real-time surface roughness inspection when specimens of known surface roughness are available.