Modeling and Analysis of Machinability Characteristics in PA6 and PA66 GF30 Polyamides through Artificial Neural Network

Abstract

The traditional metallic materials are replaced by some applications for turning process in PA6 and PA66 GF30 polyamides due to excellent properties such as high specific strength and stiffness, wear resistance, dimensional stability, low weight and directional properties. The addition of short fibers to the polyamides improves the properties over the unreinforced polyamides. As a result of these improved properties and potential applications in several fields of engineering, there is a need to understand the machining of unreinforced and reinforced polyamides. Selection of cutting tool and process parameters is important in machining of these composites. This article presents the application of artificial neural network (ANN) modeling to assess the machinability characteristics of unreinforced polyamide (PA6) and reinforced polyamide with 30% of glass fibers (PA66 GF30). The effects of process parameters such as work material, tool material, cutting speed, and feed rate on three aspects of machinability, namely, machining force, power, and specific cutting force have been analyzed through a multilayer feed forward ANN. The input—output patterns required for training are obtained through turning experiments planned as per full factorial design. The model analysis revealed that the minimum machining force results at low feed rate and independent of cutting speed, whereas the power is minimal when both the cutting speed and feed rate are at low levels for PA6 and PA66 GF30 polyamides machining irrespective of the cutting tool. On the other hand, the specific cutting force is minimal at low cutting speed and high feed rate in case of PA6 material, whereas high values of cutting speed and feed rate are essential for minimizing the specific cutting force for PA66 GF30 polyamide machining.