Modeling surface finish in end milling using fuzzy subtractive clustering-based system identification method

In this article, fuzzy subtractive clustering-based system identification and a Sugeno-type fuzzy inference system are used to model the Surface finish of the machined surfaces in end milling and to develop a better understanding of the effect of process parameters on surface quality. Such an understanding can provide insight into the problems of controlling the quality of the machined surface when the process parameters are adjusted to obtain certain characteristics. The surface finish model is identified by using spindle speed, feed rate; and depth of cut as input data. Surface finish of the machined part is the output of the process. The model building process is carried out by using fuzzy subtracting clustering-based system identification in both input and output space. Minimum error is obtained through numerous searches of clustering parameters. The fuzzy logic model is capable of predicting the surface finish for a given set of inputs (spindle speed, feed rate, and depth of cut). As such, the machinist may predict the quality of the surface for a given set of working parameters and may also set the process parameters to achieve a certain surface finish. The model is verified experimentally by employing different sets of inputs. This study deals with the experimental results obtained during end milling on aluminum alloy 390.