In conventional polishing processes, the polishing
parameters are constant along the surface. Hence, if
the desired material to be removed from the surface is not
equally distributed, an over-polishing may occur for the
areas with small material removal and under-polishing for
the areas with large material removal. Consequently, the
quality of the processed surface may not meet the manufacture
requirements. In this paper, the authors proposed a
polishing algorithm to deal with this problem using neural
network (NNW) and genetic algorithm (GA). The NNW is
used to predict the polishing performance parameters corresponding
to a certain polishing parameters. In addition,
the GA is employed to optimize the polishing parameters
according to an objective function that includes the desired
material removal and surface roughness improvement using
the output from the trained NNW model. The effectiveness
of the proposed algorithm is verified through experiments
of polishing uneven surface.