Development of Artificial Neural Network Model for CNC Drilling of AA6061 with Coated Textured Tool for Auto Parts

With the progress of manufacturing industries being critical for economic development, there is a significant requirement to explore and scrutinize advanced materials, particularly alloy materials, to facilitate the efficient utilization of modern technologies. Lightweight and high-strength materials, such as aluminium alloys, are extensively suggested for various applications requiring strength and corrosion resistance, including but not limited to automotive, marine, and high-temperature applications. As a result, there is a significant necessity to examine and evaluate these materials to promote their effective use in the manufacturing sectors. This research paper presents the development of an Artificial Neural Network (ANN) model for Computer Numerical Control (CNC) drilling of AA6061 aluminium alloy with a coated textured tool. The primary aim of the study is to optimize the drilling process and enhance the machinability of the material. The ANN model utilizes spindle speed, feed rate and Coolant type as input parameters, while the surface roughness, Material removal rate and temperature are the output parameters. A coated textured tool is chosen due to its exceptional performance over conventional drilling tools drilling. The textured surface helps in efficient chip evacuation, which reduces friction and heat generation during machining, while the coating on the tool improves its wear resistance and prolongs its lifespan. Experimental data obtained from CNC drilling of AA6061 with the coated textured tool is used to train and test the ANN model. The results demonstrate that the ANN model provides accurate predictions of the output performance of the machined hole under different drilling conditions.