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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.

In view of the improvements in manufacturing sectors, which are an important component of any economy’s growth, there is a significant need for new and advanced materials, particularly alloy materials need to be analysed and investigated so that new technologies may be effectively utilized. Materials with low weight and high strength, such as aluminium alloys, are recommended for a variety of applications that require both strength and corrosion resistance, such as marine applications and high-temperature applications. Aluminium alloy Al 5052, a nonferrous material with outstanding properties is an Al-Mg alloy with high thermal conductivity and corrosion products that are non-toxic. Minimum Quantity Lubrication (MQL) is a cost-effective and environmentally friendly method of lubrication employed in a variety of machining processes. The investigation of CNC milling of AA5052 alloy with standard Tungsten Carbide (WC) tool inserts with MQL settings are detailed in this paper.