High-precision predictive modeling of AWJM for S275 steel machining

Abstract

This research enhances the precision and efficacy of abrasive waterjet machining (AWJM) of S275 carbon steel. To this end, a precise predictive framework has been developed using artificial neural networks (ANNs) and response surface models (RSMs). By employing an innovative Vectorized Macrographic Analysis, the cutting geometries are accurately mapped and the correlation between width at various depths and energy dissipation is established. The fit accuracy of the ANN is 99%, while that of the RSM is 90%. Furthermore, a minimum cutting energy threshold of 52.20 kJ/m² has been identified, which represents the optimal efficiency threshold. These developments highlight ANN’s ability to model complex AWJM interactions, improving machining precision and adaptability.

Graphical abstract

Keywords

abrasive waterjet machining / S$ 275 $ carbon steel / taper angle optimization / response surface methodology / artificial neural network / comparative predictive model

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