Modeling and optimization of TAM in turning of Inconel 718 superalloy using RSM and PSO

This paper presents the optimization and modeling of thermally assisted machining (TAM) process parameters for machining Inconel 718 superalloy with a titanium aluminum nitride (TiAlN) nano-multilayer carbide insert by using response surface methodology (RSM) and particle swarm optimization (PSO) techniques. Machining parameters such as cutting speed, feed rate, depth of cut and workpiece temperature were considered as control factors. Experiments were conducted based on central composite rotatable design and the output responses were cutting force, surface roughness, tool wear and metal removal. From the experimental results, it was found that cutting speed and workpiece temperature were the most influence factors of cutting force, surface roughness and metal removal rate. The relation between control factors and output responses was developed by the multiple regression approach, and good agreement was found between them. Henceforth, PSO has the ability to obtain appropriate machining parameters while turning Inconel 718 superalloy under TAM conditions.