## 1 Introduction
The highest wear resistance was obtained while using the CVD-coated tools under conventional lubricated conditions. But, the MQL system provided good lubrication than cooling with lesser cost and low environmental impact. [PERSON] et al. [17] investigated the impact of using different cooling systems, namely MQL, cryogenic and hybrid of cryogenic and MQL, during the CNC milling of Inconel 718 alloy material. Comparatively, the hybrid cooling system yielded better results in terms of good machinability, less SR, and greater tool life. [PERSON] et al. [18] studied the parameters such as SR, CF, and tool wear during machining of Inconel 718 material. During machining, various sustainable environments, namely dry state, MQL, LN\({}_{2}\) cooling, hybridization of cold air and MQL, and hybridization of MQL and LN\({}_{2}\), were used. The input parameters such as \(\mathrm{a_{p}},f\), and \(v_{\mathrm{c}}\) were kept constant during machining under all the above-said environments. Better surface finish and minimum cutting force were observed during the cold air and MQL environment. Alternatively, the very least tool wear was observed under MQL and LN\({}_{2}\) hybrid cutting environment than the dry environment. Further, the researchers had used different optimization tools to identify the suitable process parameter values for minimizing the manufacturer's objectives. A few of them are discussed here. [PERSON] and [PERSON] [19] proposed an algorithm, namely Robust Grey Wolf Optimizer (RGWO), to minimize total production time by identifying the optimal input parameters multi-pass milling process. The parameter tuning during optimization was carried out using the Taguchi method. Further, an efficient constraint handling approach was implemented to handle the complex constraints of the problem. The results concluded that the RGWO outperformed the meta-heuristic algorithms such as the multi-verse optimizer and dragonfly algorithm and the other solution methodsin the literature. [PERSON] and [PERSON] (2018) developed the lexicographic weighted Tchebycheff method to obtain the optimal decision parameters of the grinding process for maximizing the quality of the surface and production rate and minimizing the machining time and cost. GAMS software was used for this purpose. [PERSON] and [PERSON] (2018) used a novel strategy, namely Robust Stochastic Novel Search, to identify the optimal values of the grinding process parameters to minimize process cost and to maximize the rate of production and surface quality. The proposed method outperformed previously proposed methodologies and novel algorithms, including Multi-Population Ensemble Differential Evolution and Heterogeneous Comprehensive Learning Particle Swarm Optimization. [PERSON] et al. (2018) optimized the abrasive water-jet machining parameters to minimize the kerf and surface roughness using the Jaya algorithm and the multi-objective Jaya algorithm. Better results were obtained through the used algorithms than the simulated annealing, particle swarm optimization, firefly algorithm, cuckoo search algorithm, blackhole algorithm, and biogeography-based optimization algorithms. Further, the PROMETHEE method was used to handpick a specific solution among the possible Pareto-optimal solutions obtained through the proposed algorithms based on the given requirements. [PERSON] et al. (2018) obtained the optimal set of process parameters of focused ion beam micro-milling, laser cutting, wire-electric discharge machining, and electrochemical machining processes. The maximization of material removal rate and minimization of SR were considered as the objectives in all the processes. The multi-objective Jaya algorithm was implemented to find the optimal solutions in all the cases.