Surface roughness prediction model of SiCp/Al composite in grinding

Abstract

Grinding is significant for hard and brittle material machining, and it has been applied in particle reinforced composites machining for higher surface quality. In this paper, surface characteristics of SiCp/Al composite in grinding was observed by the surface profiler and SEM. The Rayleigh distribution function was adopted to model the randomness of abrasive grains by assuming that the chip thickness for a single grain conforms to the distribution. The theoretical surface roughness model of aluminum alloy and silicon carbide were established based on the expectation idea. The surface roughness prediction model of SiCp/Al composite was established by the combination of theoretical surface roughness model of aluminum alloy and silicon carbide. Different combination modes were tried and the exponential composition function proved the best, the coefficients of the function were fitted by the experimental surface roughness. Rapid Non-dominated Sequencing Genetic Algorithm (NSGA-II) was adopted to optimize grinding process parameters of SiCp/Al composite considering grinding efficiency and surface roughness. It indicated that the experimental results were in good agreement with the prediction model. The surface roughness prediction model of SiCp/Al composite is helpful to improve surface quality in grinding.