Abstract
In this study, micro-milling of AISI 304 stainless steel with ball nose end mill was conducted using Taguchi method. The influences of spindle speed, feed rate and depth of cut on tool wear, cutting forces and surface roughness were examined. Taguchi's signal to noise ratio was utilized to optimize the output responses. The influence of control parameters on output responses was determined by analysis of variance. In this study, the models describing the relationship between the independent variables and the dependent variables were also established by using regression and fuzzy logic. Efficiency of both models was determined by analyzing correlation coefficients and by comparing with experimental values. The results showed that both regression and fuzzy logic modelling could be efficiently utilized for the prediction of tool wear, cutting forces and surface roughness in micro-milling of AISI 304 stainless steel.
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