Prediction of Surface Roughness in the Turning of AISI 304 Stainless Steel Using Fuzzy Logic Approach

In this study, the effects of various cutting parameters on surface roughness in the turning of AISI 304 stainless steel were investigated, and a fuzzy logic-based prediction model was developed based on the obtained experimental data. The experimental studies were carried out on a 2-axis CNC turning machine, considering three different machining parameters: cutting speed, feed rate, and depth of cut. The experimental design was structured using a full factorial design method (2³), resulting in a total of nine experimental conditions. The surface roughness (Ra) values obtained after machining were measured using a Mitutoyo surface roughness measurement device. The collected data were modeled in MATLAB using a Mamdani-type fuzzy inference system, and surface roughness predictions were performed. The predicted results were compared with the experimental results, and it was observed that the model achieved high accuracy. This study demonstrates the effectiveness of fuzzy logic-based models in achieving optimum surface quality in the machining of AISI 304 stainless steel.