Multi-objective optimization and experimental investigation of the CNC milling process parameters for Al7075 alloys using Taguchi-GRA, DEAR and WASPAS techniques

Abstract In the recent scenario, the demand for high production with minimal cost is the main focus of the production industries. Industry 4.0 requires manufacturers to develop a process that meets production demand and efficiency. This research aims to utilise the multi-objective optimization method, designed by the Taguchi method for Al7075 alloys in a CNC milling machine. In the industries where a variety of materials are used, aluminium is one of them. The most commonly used aluminium grade material is aluminium 7075 (Al7075) that is in high demand. The application of Al7075 is found in many areas including aerospace, automobiles, defence, and structural industries. Machining is the essential choice to produce the component with a closer tolerance. A milling machine is one of them to generate a good surface quality. To minimize the number of experiments, Taguchi L9 orthogonal array is applied to know the effect of milling parameters like: depth of cut, tool diameter, and spindle speed on surface roughness (SR) and material removal rate (MRR). To optimize the outputs, the Taguchi signal-to-noise (S/N) ratio is used. In the final step, outputs are simultaneously optimized and compared the performance between the different multi-criteria decision making (MCDM) techniques. The best choice for MCDM is to use the Data Envelopment Analysis-based ranking (DEAR) method with responses, an increase in MRR of 2.062 times, and a decrease in SR of 0.832 times from the average value. A future research direction is also discussed in this research.