Prediction of surface quality and optimization of process parameters in drilling of Delrin using neural network

Abstract

The high-temperature polymers like Acetal homopolymer (Delrin) currently have a wide variety of use. They are quite often utilized in traditional components to reduce weight, cost or meet a specific application requirement, and so on. Some of preferred uses of such polymers include aircraft interiors, wire insulation, wire couplings and fixtures, and so on, particularly at high-temperature applications. The machining process like drilling may affect the near net shape of the final product. This experimental study is done through modeling and optimization for identifying the suitable tool and optimum parameters for drilling of Delrin polymer under dry conditions to achieve high surface finish. The three levels of parameters such as spindle speed ( N), feed rate ( f), and tool point angle ( Θ) are taken as control parameters of the response variable. Two different commercially available tool materials namely high-speed steel drill tool and solid carbide tool are accounted in experiments. L27 orthogonal array is initially taken for the experimentation in CNC turning center with horizontal drilling setup. Artificial neural network is employed to sample, train, and test the input parameters in order to lessen the experimental error and measurement error of response variables. Response surface models are developed and optimal parameters toward the surface quality of the hole are determined through the desirability function approach. It is found that the surface generated under dry mode with speed of 1026 r/min, feed of 0.1 mm/min, point angle of 118° recorded the surface roughness of 0.699 µm, which is considered to be the best for drilling Delrin material.