Modeling and experimental optimization of shear-thickening media-based abrasive flow finishing (STMAFF) were performed to achieve sub-micron surface refinement (Ra 8 kPa) and abrasive particle trajectories with 0.90) for the percentage improvements in roughness parameters (%ΔRa and %ΔRz). A multilayer perceptron artificial neural network (ANN) surrogate model was subsequently trained on the experimental dataset and coupled with the Harris Hawks Optimization (HHO) algorithm to globally refine the process parameters beyond the RSM local optimum. The optimal conditions identified by the hybrid RSM–ANN–HHO framework were 35 wt. % abrasive concentration, 1.8 rps pump rotation speed, and 50 min finishing duration, yielding a final Ra 99 % roughness reduction. The novel hybrid magnetic abrasive flow finishing (HMAFF) setup enabled both magnetic-assisted and conventional modes, ensuring uniform material removal across complex internal geometries. This integrated experimental–computational–optimization framework establishes STMAFF as a sustainable and highly effective post-processing route for ultra-smooth internal surfaces in additively manufactured cooling channels.
Hashmi et al. (Fri,) studied this question.