Abstract
In this study, an attempt has been made to develop a Mamdani fuzzy inference system-based predictive model for predicting surface roughness during wet turning of AISI D3 tool steel. The influence of machining parameters on surface roughness is also investigated. The experimental runs were performed on a lathe using Taguchi’s L9 orthogonal array to know the effect of cutting parameters on machining surface roughness. The performance of the fuzzy model was also compared with the regression model. The fuzzy model’s prediction efficiency (92.10%) was found better than the regression model (70.33%). Therefore, the resultant model so developed is found adequate for surface roughness prediction purposes within the parametric range.
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