A vision system for surface roughness assessment using neural networks

In this study we use machine vision to assess surface roughness of machined parts produced by the shaping and milling processes. Machine vision allows for the assessment of surface roughness without touching or scratching the surface, and provides the flexibility for inspecting parts without fixing them in a precise position. The quantitative measures of surface roughness are extracted in the spatial frequency domain using a two-dimensional Fourier transform. Two artificial neural networks, which take roughness features as the input, are developed to determine the surface roughness. The first network is for test parts placed in a fixed orientation, which minimises the deviation of roughness measures. The second network is for test parts placed in random orientations, which gives maximum flexibility for inspection tasks. Experimental results have shown that the proposed roughness features and neural networks are efficient and effective for automated classification of surface roughness.