In recent years, the utilization of metal matrix composites (MMC) materials in many engineering
fields has increased tremendously. Accordingly the need for accurate machining
of composites has also increased enormously. Despite the recent developments in the
near net shape manufacture, composite parts often require post-mold machining to meet
dimensional tolerances, surface quality and other functional requirements. In the present
work, the surface roughness of Al–SiC (20 p) has been studied in this paper by turning
the composite bars using coarse grade polycrystalline diamond (PCD) insert under different
cutting conditions. Experimental data collected are tested with analysis of variance
(ANOVA) and artificial neural network (ANN) techniques. Multilayer perceptron model has
been constructed with back-propagation algorithm using the input parameters of depth of
cut, cutting speed and feed. Output parameter is surface finish of the machined component.
On completion of the experimental test, ANOVA and an ANN are used to validate the results
obtained and also to predict the behavior of the system under any condition within the
operating range.