FUZZY CLUSTERING MODELLING FOR SURFACE FINISH PREDICTION IN FINE TURNING PROCESS

ABSTRACT

In this paper, fuzzy subtractive clustering based system identification and Sugeno type fuzzy inference system are used to model the surface finish of the machined surfaces in fine turning process 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. Surface finish data were generated for aluminum alloy 390 (73 BHN), ductile cast iron (186 BHN), and inconel 718 (BHN 335) for a wide range of machining conditions defined by cutting speed, cutting feed rate and cutting tool nose radius. These data were used to develop a surface finish prediction fuzzy clustering model as a function of hardness of the machined material, cutting speed, cutting feed rate, and cutting tool nose radius. 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 (workpiece hardness, cutting speed, cutting feed rate and nose radius of the cutting tool). 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 further experimentation using different sets of inputs. This study deals with the experimental results obtained during fine turning operation. The findings indicate that while the effects of cutting feed and tool nose radius on surface finish were generally consistent for all materials, the effect of cutting speed was not. The surface finish improved for aluminum alloy and ductile cast iron but it deteriorated with speed for inconel.

Keywords:

• Fuzzy logic
• Fuzzy subtractive clustering
• Fine turning
• Surface roughness
• Machining
• Sugeno fuzzy model

ACKNOWLEDGMENTS

The authors would like to gratefully acknowledge the use of Machine shop and the laboratory facilities at Jordan University of Science and Technology, Irbid, Jordan.