According to the Scientific Reports preprint (published 29 June 2026), the study tested AWJM on an Al6061-0.5 wt.% B4C-1 wt.% ZrO2 hybrid composite produced by ultrasonic-assisted stir casting.
Per the paper, ANOVA identified AFR as the dominant factor for MRR and AJCS as the primary influence on Ra and KTA.
The authors applied a hybrid Grey Relational Analysis-Analytic Hierarchy Process (GRA-AHP) to obtain a multi-objective optimum and developed SVR, RF, and MLP regression models for predictive mapping.
The reported optimal AWJM setting was AFR 430 g/min, WJP 280 MPa, AJCS 80 mm/min, SOD 1.5 mm, and GS 120 mesh.
The study follows that pattern by using RF and MLP as complementary model families and SVR for comparison.
According to the Scientific Reports preprint (published 29 June 2026), the study tested AWJM on an Al6061-0.5 wt.% B4C-1 wt.% ZrO2 hybrid composite produced by ultrasonic-assisted stir casting. The experimental design used a Taguchi L27 orthogonal array across five process factors: abrasive flow rate (AFR), water jet pressure (WJP), abrasive jet cutting speed (AJCS), stand-off distance (SOD), and grit size (GS). The article reports measured response ranges of MRR 7.86 to 15.24 mm^3/min, Ra 3.220 to 3.980 um, and KTA 0.142 degrees to 0.309 degrees. Per the paper, ANOVA identified AFR as the dominant factor for MRR and AJCS as the primary influence on Ra and KTA. The authors applied a hybrid Grey Relational Analysis-Analytic Hierarchy Process (GRA-AHP) to obtain a multi-objective optimum and developed SVR, RF, and MLP regression models for predictive mapping. The reported optimal AWJM setting was AFR 430 g/min, WJP 280 MPa, AJCS 80 mm/min, SOD 1.5 mm, and GS 120 mesh.
Editorial analysis - technical context: Industry-pattern observations: Combining designed experiments with tree-based and neural regressors plus a multi-criteria decision layer is a common, effective approach for manufacturing process optimization. The study follows that pattern by using RF and MLP as complementary model families and SVR for comparison. Because the experimental matrix is a Taguchi L27, dataset size is modest; practitioners frequently confront trade-offs between model complexity and overfitting in similarly sized datasets, and they often rely on cross-validation and feature-selection or physics-informed features to improve generalization.
For practitioners: Key reproducible elements in the paper are the Taguchi L27 layout, the ANOVA breakdown of factor importance, the explicit optimal parameter set from GRA-AHP, and the choice of SVR, RF, and MLP as baseline regressors. Observers using comparable workflows should document validation splits, hyperparameter search ranges, and error metrics when adopting this template.
