Experimental Investigation and Multi-objective Optimization of the CNC Milling Process Parameters for Al7075 Alloys Using Taguchi-GRA and WASPAS Techniques

One of the key challenges in advanced machining is addressed in this paper by determining the optimal process parameters that meet both producer and customer requirements. Estimating the surface condition of a workpiece during milling remains a complex task, particularly in aerospace and automotive applications. Efficient and cost-effective CNC milling is crucial. Implementing multi-optimization approaches is essential to achieving the desired outcomes. This study systematically examines and optimizes material removal rate (MRR) and surface roughness (SR)during the milling of aluminum 7075 (Al7075). Al7075 alloys are ideal for end milling due to their excellent electrical performance, low density, high strength, stiffness, and wear resistance. The process parameters considered in this study include tool diameter, cutting speed, feed rate, and cutting depth, with a focus on their effects on MRR and SR. One way to find the minimum number of experiments needed is to use a Taguchi L9 orthogonal array. Grey Relational Analysis (GRA) and Weighted Aggregate Sum Product Assessment (WASPAS) are very efficient multi-objective approaches used to optimize milling parameters, enhancing MRR and reducing SR. The depth of cut is the most significant milling parameter influencing both MRR and SR. Optimal experimental results show an MRR of 236.39 mm³/min, 2.17 times higher than the average, and a minimum SR of 0.212 μm, 0.76 times lower than the average value.