Prediction of Surface Roughness in the End Milling Machining Using Fuzzy Rule-Based

In the experiment, 24 samples of data has been tested in real machining by using uncoated, TiAlN coated, and SNTR coated cutting tools of titanium alloy (Ti-6Al-4v). The fuzzy rule-based model is developed using MATLAB fuzzy logic toolbox. Rule-based reasoning and fuzzy logic are used to develop a model to predict the surface roughness value of end milling process. The process parameters considered in this study are cutting speed, feed rate, and radial rake angle, each has five linguistic values. Nine linguistic values and twenty four IF-THEN rules are created for model development. Predicted result of the uncoated, TiAlN coated, and SNTR coated has been compared to the experimental results, and it gave a good agreement with the correlation 0.9842, 0.9378 and 0.9845, respectively. The differences of the uncoated, TiAlN coated, and SNTR coated between experimental results and predicted results have been proven with estimation error value 0.00025, 0.0015 and 0.0008, respectively. It was found that by applying SNTR coated cutting tools with the recommended combination of linguistic values might gave best surface roughness values.