Optimization of Machining Parameters to Minimize Surface Roughness in the Turning of Carbon-Filled and Glass Fiber-Filled Polytetrafluoroethylene

Abstract

Polytetrafluoroethylene (PTFE) has an attractive combination of properties such as low coefficient of friction, very high dielectric strength and almost complete resistance to acidic and caustic materials. Although glass fiber and carbon fillers can enhance the mechanical properties of PTFE, they can also increase the machining difficulties. Thus, in order to achieve both high tolerance and good surface finish, an investigation of the machinability of this engineering plastic was needed. The purpose of this study was to achieve minimum surface roughness values by determining the optimum cutting parameters (cutting speed, feed rate, depth of cut) in the turning of 25% carbon- and 25% glass fiber-filled PTFE. The dry turning process was carried out, and the average surface roughness was determined using a MAHR mobile roughness-measuring instrument. An artificial neural network (ANN) with nonlinear autoregressive models having exogenous input (NARX) was used to predict the effect of the machining parameters on the surface roughness. Consequently, the lowest surface roughness value (1.35 µm) was obtained on carbon-filled PTFE in turning at 150 m/min cutting speed, 0.1 mm/rev feed rate and 1 mm depth of cut. The predicted results using the ANN with NARX indicated a good agreement with the experimental values.