Fuzzy surface roughness modeling of CNC down milling of Alumic-79

Machining processes are complex and highly dynamic systems that can have many variables affecting the desired results. Fuzzy modeling proved to be effective in modeling such complex systems. Down milling machining process of Alumic-79 was modeled in this paper using the adaptive neuro fuzzy inference system (ANFIS) to predict the effect of machining variables (spindle speed, feed rate, depth of cut, and number of flutes) on the surface finish (represented by the surface roughness) of Alumic-79 in order to improve and increase its range of application. Optimum surface roughness of 0.224 μm is achieved for the four flutes at the spindle speed of 2000 rpm, feed rate of 0.06 mm/tooth, and depth of cut of 2 mm. In the meantime, it was found for the two flutes that the minimum surface roughness of 0.327 μm is achieved at the spindle speed of 2000 rpm, feed rate of 0.05 mm/tooth, and depth of cut of 2 mm.