# Recent Progress Trend on Abrasive Waterjet Cutting of Metallic Materials: A Review
## 4 Abrasive Waterjet Cutting Process Parameters Improvements and Optimization
### AWJ Cutting Process Parameters Optimisation  
Generally, optimization is utilized to achieve the minimum probable costs of cutting operations with a proper combination of process parameters considering indexes such as quality, productivity, and cost. Recently, an effective optimization technique, the Taguchi method, has become increasingly successful in optimizing some AWJM applications [108]. Several studies using the Taguchi method in their experimental work are shown in Table 9, mitigating the effectiveness of this technique. Taguchi is a technique applied for improving quality performance depending merely on process parameters. Taguchi's orthogonal array is useful in establishing impacts created by these cutting parameters with two or more mixed levels, which lessens the number of required experimental trials [109]. Aside from the Taguchi methodology, there are several other tools applied to quantify the correlation between AWJM input and output parameters. Table 10 enlisted several noteworthy research studies that have been published, which \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Input Parameter** & **Output Parameter** & **Key Findings and References** \\ \hline \hline \multirow{4}{*}{Metal Matrix Composites} & \multirow{4}{*}{SOD, TS, AFR} & \multirow{4}{*}{R\({}_{\text{a}}\)} & [PERSON] et al. [25] used Taguchi-L9 \\  & & & orthogonal array in their experimental investigations. The results showed that the essential parameters in reducing R\({}_{\text{a}}\) were TS and AFR. \\ \hline \hline \multirow{4}{*}{Ti6 ALAV} & \multirow{4}{*}{P, TS, AFR, ND, OD} & \multirow{4}{*}{R\({}_{\text{a}}\), DOC} & [PERSON] et al. [27] worked in the prediction of cutting depth by using the Taguchi method. It was proven that TS was the most influencing parameter for a higher depth of cut. \\ \hline \hline \multirow{4}{*}{Inconel 625} & \multirow{4}{*}{P, AFR, SOD} & \multirow{4}{*}{KTA} & [PERSON] et al. [29] employed [PERSON]’s technique in this study, and it was observed that P played a significant role in lower kerr taper angle. \\ \hline \hline \multirow{4}{*}{Brass} & \multirow{4}{*}{P, TS, AFR} & \multirow{4}{*}{R\({}_{\text{a}}\), MRR} & By utilizing [PERSON]’s L9 orthogonal array, [PERSON] et al. [30] proved the feasibility of utilising an abrasive waterjet machine in cutting brass material. They concluded that increasing P, TS, and AFR can minimise R\({}_{\text{a}}\) and maximise MRR. \\ \hline \hline \multirow{4}{*}{AZ91 Magnesium alloy} & \multirow{4}{*}{P, TS} & \multirow{4}{*}{DOC} & [PERSON] et al. [33] examined influence of process parameters in the depth of cut through the Taguchi experimental design of the L9 orthogonal array. The result showed that a higher DOC could be obtained with high P and low TS. \\ \hline \hline \multirow{4}{*}{Ti-6 Al-4V and Inconel 825} & \multirow{4}{*}{P, SOD, AFR} & \multirow{4}{*}{R\({}_{\text{a}}\)} & [PERSON] et al. [34] achieved a higher MRR for Ti-6 Al-4V at a value of 3.132 gm/min and 3.246 gm/min for Inconel 825 by utilising an experimental Taguchi approach. \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Several studies (2018 to 2020) in AWJ cutting carried out through the Taguchi method. focused on different experimental and simulation studies to achieve the optimum degree of these process parameters. [PERSON] and [PERSON] [35] applied the fuzzy TOPSIS (Technique for Order Preference by Similarities to Ideal Solution) method as an optimization technique to attain better AWJ cutting performance with AA5083-H32. They considered the pressure, jet impact angle, and abrasive size as input parameters to achieve minimum values of surface roughness, kerr taper angle, and kerr width. Furthermore, they also used an additional algorithm method, the Grey-fuzzy method, to optimize AWJM process parameters for different materials like glass fiber reinforced polymer [35]. [PERSON] et al.