Deep learning neural network–based prediction of abrasive water jet machining performance and surface roughness of DMR249A steel
ABSTRACT Abrasive waterjet machining (AWJM) is increasingly adopted for precision cutting of high-strength steels; however, accurate prediction of machining responses remains challenging due to complex nonlinear interactions among process parameters. This study aims to develop a reliable data-driven framework for predicting material removal rate (MRR), surface roughness (Ra), and taper angle during AWJM of naval-grade DMR249A steel. Experiments were designed using a Taguchi L27 orthogonal array considering water pressure, traverse speed, stand-off distance, and abrasive flow rate as control factors. A multi-output deep learning neural network (DLNN) was implemented and systematically tuned to model the nonlinear relationships between input parameters and machining responses. The model performance was evaluated using statistical error metrics and parity analysis. Results demonstrate that the DLNN achieved high predictive accuracy and effectively captured parameter interactions, outperforming conventional regression approaches, particularly for Ra and taper angle. SEM analysis further confirmed the progressive transition from cutting-dominated to deformation-dominated erosion along the jet path. The developed framework reduces reliance on extensive experimentation and supports intelligent process planning for difficult-to-machine steels.