Artificial Intelligence Model for Machinability Investigations on Drilling of AA6061 with Micro Textured Tool for Automobile Applications

Considering the advancements in manufacturing industries, which are crucial for economic growth, there is a substantial demand for exploration and analysis of advanced materials, especially alloy materials, to enable efficient utilization of new technologies. Lightweight and high-strength materials, like aluminium alloys, are highly recommended for various applications that necessitate both strength and resistance to corrosion, such as automobile, marine and high-temperature applications. Therefore, there is a significant need to investigate and analyse these materials to facilitate their effective application in manufacturing sectors. This study investigates the machinability of drilling AA6061 using a micro-textured tool and proposes an Adaptive Neuro Fuzzy Inference System (ANFIS) model for investigating the machinability of drilling AA6061 aluminium alloy with a micro-textured uncoated tool. The ANFIS model considers various input parameters such as spindle speed, feed rate, and Coolant type to predict the machinability performance of the drilling process. The results indicate that the ANFIS model is an effective tool for predicting the machinability performance of AA6061 during the drilling process. The model can help optimize the drilling process by identifying the best combination of input parameters that yield the desired machinability performance. This study demonstrates the potential of ANFIS models in the field of machining, particularly in the development of predictive models for optimizing machining processes.