# 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
[37] have obtained optimal values of AWJM process parameters for machining Inconel 718, utilizing the Vlsekriterijumska Optimizacija Kompromisno Resenje in Serbian (VIKOR) method. The researchers advanced the VIKOR method by employing an analytic hierarchy process (AHP) to convey the weight of the comparative significance  
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**Material** & **Input Parameter** & **Output Parameter** & **Optimisation Techniques** & **Key Findings and References** \\ \hline \multirow{8}{*}{AA5083-H32} & \multirow{8}{*}{P, JIA, AS} & \multirow{8}{*}{R\({}_{a}\), KTA, KTW, KBW} & \multirow{8}{*}{Fuzzy TOPSIS method} & \multirow{8}{*}{[PERSON] et al. [35] employed an optimisation technique to select optimal values of input parameters, specifically, P} & \multirow{8}{*}{[PERSON] et al. of the aspects. This involved a multi-criterion decision-making (MCDM) system suitable for selecting feasible or almost ideal solutions from a set of presented alternatives [111]. The results showed that low pressure, abrasive mass flow rate, and standoff distance can minimize the kerf taper angle and roughness and maximize the material removal rate. A hybrid response surface and Box-Behnken methodology (RSM-BBD), [PERSON] et al. [42] have shown the influence of pressure, traverse speed, abrasive mass flow rate, and abrasive particle in determining a tolerable level of roughness for cutting Inconel 718 and in formulating a mathematical model for predicting results. Confirmation experiments have validated the precision of these developed models. [PERSON] et al. [43] have employed a Taguchi-DEAR methodology to evaluate AWJ cutting process parameters--i.e., waterjet pressure, transverse speed and standoff distance for TiB\({}_{2}\) particles in reinforced AI7075 composite materials. They anticipated higher levels of material removal rate and lower levels of surface roughness and taper angle. A customary of results of investigation were plotted to determine combinations of suitable process parameters based on a multi-response performance index (MRPI). [PERSON] et al. [110] considered the traverse speed, standoff distance, and abrasive mass flow rate as input parameters of an AWJ cutting process of AA631-T6. The results of material removal, roughness, and taper angle were considered against the application of the Jaya algorithm (JO). This optimization algorithm can be used to solve constrained and unconstrained conditions, to achieve an optimum alternative and avoid the worst ones [112]. [PERSON] and [PERSON] [107] used weighted principal components analysis (WPCA) to optimize AWJM process parameters in machining Inconel 617. The measured performance indicators included the material removal rate (MRR) and geometric accuracy. The WPCA method uses internal test and training samples to calculate the 'weighted' covariance matrix. The theoretical basis of WPCA is determined through a 'weighted' covariance matrix. Moreover, WPCA highlights training samples similar to the test sample and lessens the impact of other training samples [113]. [PERSON] and [PERSON] [47] have carried out the grey wolf optimizer (GWO) technique for AWJ cutting of AA6061, considering multiple objectives including material removal rate, surface roughness, overcut and taper. The GWO algorithm is a non-dominated set of Pareto solutions whose optimization imitates the hunting activities of grey wolves. A distinct advantage of GWO is that it identifies the best possible solution and stores this through the aid of social hierarchy. [PERSON] et al. [72] developed mathematical equations using the regression investigation method (RIM) artificial neural network (ANN) procedures to select the optimum parameters. ANN is a computer-aided program mimicking the way the human brain manages information, collecting information by identifying outlines and interactions in data, acquired through experience other than from programming [39]. They observed that the developed model using ANN can predict AWJ cutting responses with at least 90% accuracy.