Optimization in Milling of Polymer Materials for High Quality Surfaces

Abstract

High quality surfaces with transparency are required for manufacturing of plastic products. In cutting of polymer materials, surface quality is sometimes deteriorated by tarnish and/or unequal spaces of area on a surface. The cutting parameters should be determined through understanding of surface finish characteristics. This paper presents an optimization approach in milling of polycarbonate with polycrystal diamond tools in terms of the surface finish. Surfaces are finished with changing the feed rate and the clearance angle of the tool. The surface finishes, then, were observed to classify the deterioration type into welding, adhesion, and the unequal space of cutter marks with measurement of the surface profiles. The measured surface roughnesses are decomposed into the theoretical/geometrical term and the irregular term induced by the thermal and the dynamic effects. A map is presented to characterize the irregular term for the feed rates and the clearance angles. Because the surface roughnesses are measured at discrete sets of the cutting parameters in the actual cutting tests, the process design cannot be conducted to optimize the operation parameters. Therefore, a neural network is applied to associate the cutting parameters with the irregular term in the map. An approach is presented to determine the number of hidden nodes/units in the design of the neural network. Three prominent areas of welding, adhesion, and unequal spaces of the cutter marks, appear in the map of irregular roughness. The map of the surface roughness is made to optimize the cutting process. The applicable feed rates and clearance angles are determined for the tolerable surface roughnesses. The gradient information in the map is used to evaluate the stability/robustness of the surface quality for changing the parameters. The optimum parameters were determined to minimize the gradient information in the applicable feed rates and clearance angles.