Surface finish quality characterisation of machined workpieces using fractal analysis

Characterisation of surface finishes of machined components and parts in machining operations is becoming more important as the world tends towards globalisation and competitiveness. Companies are choosing new technologies to improve on the surface finish of components and parts. A new method of fractal characterisation of the spectral trace of machined surfaces is investigated in this work. Fractals have been used to describe and quantify irregular fragments or complex shapes of materials such as shore-line, clouds, plants, brain cells, gold colloids, and sponge iron [1, 2]. Fractal analysis has also been used to study structural and mechanical attributes of some food products [3]. Kerdpiboon et al. [3] use artificial neural network analysis to predict shrinkage and rehydration of dried carrots, based on the inputs of moisture content and normalised fractal dimension analysis of the cell wall structure. Measured values of shrinkage and rehydration were predicted with an R2 > 0.95 for the entire test samples.