Multi-Objective Optimization of Turning for Nickel-Based Alloys Using Taguchi-GRA and TOPSIS Approaches

One of the significant challenges faced by industries today is obtaining the best process parameters while meeting the needs of both producers and users. It is necessary to introduce and use optimization strategies to accomplish this aim and satisfy these demands. This paper examines the exploit of Taguchi grey relational analysis (GRA) to optimize the turning process parameters of a nickel-based alloy, considering surface roughness (SR), tool wear rate (TW), and material removal rate (MRR). The approach combines L9 experiments with grey relational analysis, incorporating control parameters such as speed ( A ) at 300 rpm, 400 rpm, and 500 rpm; feed rate ( B ) at 0.05 mm/rev, 0.10 mm/rev, and 0.15 mm/rev; and cutting depth ( C ) at 0.1 mm, 0.3 mm, and 0.5 mm. The optimal parameter values obtained were A = 300 rpm, B = 0.15 mm/rev, and C = 0.5 mm, resulting in the best production outcomes: SR = 1.56 µm, TW = 0.0178 mm, and MRR = 2.14884 cm 3 /min. To compare the results, technique TOPSIS, a Multiple Attribute Decision Making technique, was also employed. The optimal parameter values derived from TOPSIS were A = 500 rpm, B = 0.15 mm/rev, and C = 0.5 mm, leading to ideal output parameters: SR = 1.774 µm, TW = 0.0191 mm, and MRR = 3.85226 cm 3 /min. The comparative study demonstrates the efficiency of the Taguchi GRA approach in optimizing turning process parameters for nickel-based alloys. Using this approach, we accomplished significant decreases in surface roughness (SR), tool wear rate (TW), and material removal rate (MRR) by 12.6%, 6.81%, and 44.21%, respectively.