Ceramic materials have been studied and used in considerable industrial applications due to their high hardness and strength at high temperature and extreme wear resistance. However, the machining costs of these materials may reach as high as 90% of their total component costs and the grinding of these materials are much more susceptible to surface damage as compared to metals. Electrolytic InProcess Dressing (ELID) grinding can be used to machine hard and brittle materials, such as aluminum oxide (Al2O3), that are difficult to be machined by conventional grinding processes to achieve high surface quality and high material removal rate. The effect of each major machining parameter in processing Al2O3 on the surface roughness and material removal rate were analyzed and presented. Furthermore, by using the process models built with Neuro-fuzzy theory, an optimization algorithm is constructed for multiple objectives and which incorporates tournament sharing selection and G-bit local search is applied to find the machining parameters for achieving the best product quality and/or production rate.

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