A novel online method of monitoring the roughness and damage of workpiece during cutting - Li, Y. X. (2018)
Theories related to grayscale image self-affine dimension were presented in this study to describe roughness and damage in a workpiece. A novel technique was also proposed to monitor the surface quality of a workpiece online by using the grayscale image self-affine dimension and damage. A MEMRECAM HX-3E highspeed camera was used to capture the surface of cylinder cast iron workpieces during cutting and to verify the theoretical and actual online monitoring of workpiece surface roughness and damage. The proposed online monitoring technique was then compared with traditional monitoring techniques. Results revealed: (1) clear and small cut marks and textures on the workpiece surface. The corresponding calculated grayscale image self-affine dimension was also small, and thus grayscale image self-affine dimension satisfied the definition of fractal. (2) In the initial stage when cutting was unstable, the self-affine dimension and damage calculated using workpiece grayscale images were relatively larger than those obtained in other moments when cutting was stable. It was indicated that these parameters were sensitive to changes in workpiece surface stage. (3) The grayscale image self-affine dimension and damage increased as back cutting depth increased, and these findings were consistent with those observed on traditional workpiece quality tests performed while the machines were shut down. These phenomena indicated that, (1) the calculation theories on grayscale image self-affine dimension and damage were correct, and damage was also an important parameter to assess the workpiece quality. (2) Our proposed approach was a novel and effective technique for the online monitoring of workpiece quality. Therefore, this technique could be applied to monitor workpiece quality online and provide relatively higher sensitivity to changes in the workpiece surface stage than those of other traditional techniques.