## 3 Machinability of AM Ti6 Al4V Alloy
### Optimization Process  
The cutting speed, feed rate, and depth of cut are the key cutting parameters that must be optimized in machining experiments to enhance the quality and efficiency of machined parts. Proper optimization of these parameters significantly impacts surface finish, dimensional accuracy, and overall process efficiency, making them critical for achieving high-quality results in machining operations [95]. The report shows that the optimization process reduced surface roughness and specific cutting energy by 2% and 6%, whereas it increased the material removal rate and tool life by 34% and 7%, respectively [92].  
Dr. [PERSON] first developed the Taguchi method, which is employed in engineering analysis to determine the optimum combinations of process conditions for improving performance characteristics in manufacturing systems [89]. The Taguchi method was combined with statistical analysis to optimize the machining process for design purposes [88]. It was widely acknowledged that the Taguchi approach is a solid and efficient technique for reducing energy intensity, significantly increasing process performance with minimal testing, production, and cycle time to determine the optimal conditions for the objectives [78; 96; 97; 98].  
Grey relational analysis is another optimization technique that converts multiple response values into a single grey relational grade value. It can be applied in decision-making to find the most significant relations in the complicated context system characterized by multiple impute [96]. The main advantage of grey relational analysis is that it simplifies the optimization process by converting the numerous performance characteristics problem into a single performance characteristic [82]. The report shows that during the machining of Ti6 Al7 Nb alloy, the feed rate was the most influencing factor; about 11.3% enhancement was achieved compared to the previous value by using the GRA optimization process [96]. In addition, multi-objective optimization can achieve the sustainable goal of machining Ti6 Al4V alloys with a high material removal rate, longer tool life, and minimum energy consumption. A multi-objective analysis is an optimization process that involves establishing multiple objectives by combining multiple prediction models to obtain an objective model's final and optimal solution for the evaluation indices [99]. In implementing energy-saving efficiently, a multi-objective analysis has been conducted between cutting conditions and energy consumption to optimize energy efficiency and meet other manufacturing requirements [90].  
Analysis of variance (ANOVA) is a statistical analysis package that assesses the significant involvement of cutting parameters on output responses. This is achieved by computing the sequential sum of squares for the given parameters at a stated confidence level. ANOVAwas performed to assess the most significant cutting conditions for material removal rate, tool wear, and surface roughness in machining operations with the determination of the optimal parameters [96; 100; 101].  
MINITAB 21(r) is statistical software that designs the experiment for the optimization process, where the software investigates the significance levels of the cutting conditions (v, f, and d) on the machining responses. It was reported that the interaction effect (vvx, fcv) was more significant than the individual effect in investigating the effect of cutting conditions on the machining responses using statistical software, where ANOVA was achieved at a 95% confidence level. Table 7 represents many studies that have optimized the machining process using the Taguchi method, Grey relational analysis, ANN, and regression. However, the Taguchi method was found to be the most effective.