Experimental analysis and optimization of MQL turning of nitinol 56 alloy: a comparative study of grey, utility, and TOPSIS methods

The demand for Nitinol alloy machined parts in the automotive and aerospace industries necessitates the optimization of machining parameters to enhance process performance in terms of product quality and cost. Specifically, the spindle speed (S), feed (f), and depth of cut (t) are crucial process variables in machining. Output performance indices, such as material removal rate (MRR), tool wear (TW), and surface roughness (Ra), are important measures of process effectiveness. This study focuses on evaluating process performance in machining Nitinol 56 alloy, utilizing the Taguchi method to assess the impact of the aforementioned process variables on the output performance indices. It has been noticed that the feed was the most influential variable for surface roughness and material removal rate whereas spindle speed for the tool wear. Subsequently, multiple multi-attribute decision-making optimization methods (MADM), including Grey, Utility, and TOPSIS, are employed to identify the optimal combination of process variables that satisfy the conflicting performance indices. The optimal process variable combination determined across all methods is S = 835, f = 0.111, and d = 0.6. A Moreover, a confirmatory test was conducted to validate the optimal conditions, revealing an increase of 5% in the overall value of utility, 5.98% in grey coefficient, and 1.23% in the closeness coefficient.