Development of an artificial neural network model for criticizing the burr formation during flat bottom drilling of CuZn38As brass alloy considering cutting tool geometry

Abstract

The approved laws and regulations on restricting the use of Lead in the products’ chemical composition, have made the industries to come up with development of low-lead brass alloys as an innovative solution during the last years. These alloys are the most predominantly utilized materials in pumping, drinking water industry. However, as alternatives for the conventional brass alloys, they contain lower machinability. Most of the components need to be aesthetic and compact. Therefore, the designed components have complex geometries. Flat bottom drilling is one of the new processes which is generally taken advantage in machining of these components. In this study, artificial neural networks (ANN) modelling, have been deployed to investigate the effects of the cutting tool geometries including axial rake, radial rake angles and edge radius as well as plunging angle to the work material focusing on the burr formation. A multilayer feed-forward ANN using error back-propagation training algorithm has been employed for this purpose. The results revealed possible reduction of burr formation on both the entry and exit sides of the low-lead brass alloy.