A study of computer vision for measuring surface roughness in the turning process

The paper presents a system for measuring the surface roughness of turned parts using a computer vision system. The images of specimens grabbed by the computer vision system are processed to obtain parameters of their grey levels (spatial frequency, arithmetic mean value, and standard deviation). These parameters are used as input data to a polynomial network. Using the trained polynomial network, the experimental result shows that the surface roughness of a turned part made of S55C steel, measured by the computer vision system over a wide range of turning conditions, can be obtained with reasonable accuracy, compared to that measured by a traditional stylus method. Compared with the stylus method, the computer vision system constructed is a useful method for measuring the surface roughness of this material faster, at a lower cost, and with lower environmental noise.