On-line measurement of surface roughness is an important element in a Computer Integrated Manufacturing (CIM) environment. Current methods of contact measurement are not suitable as they interfere with the machining process. Optical methods, such as laser profilometry, in general, use expensive and large equipment, which pose difficulties in the implementation on the machine tool for on-line monitoring. This thesis presents an optical area based surface characterization technique, which applies fractal geometry and basic light scattering principles, to correlate surface roughness and fractal dimension. The unique contribution of this work is that the theory of electromagnetic scattering observed in rough surfaces and the fractal nature of the machined surfaces, are used to implement an inprocess surface quality measurement. Fractal geometry is used to describe the surface topography of the machined surface. Fractal dimension is found to be a parameter that can be used to estimate the surface roughness of machined surfaces.

The surface roughness of the machined surface is estimated by analyzing the image obtained from a CCD camera. An algorithm, which calculates the fractal dimension of the image, is incorporated within the image processing software to accomplish on-line monitoring of surface roughness. A calibration curve is generated by measuring the fractal dimension of samples of known surface roughness. This curve is then used for on-line monitoring of surface roughness. The capabilities and future potential of this system are demonstrated.