The traditional metallic materials are replaced by some applications for turning process in pa6 gf35 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 35% of glass fibers
pa6 gf35 properties
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.