RT Journal Article
T1 High-precision predictive modeling of AWJM for S275 steel machining
A1 Bañón-García, Fermín
A1 Martín-Béjar, Sergio
A1 Bermudo-Gamboa, Carolina
A1 Trujillo-Vilches, Francisco Javier
A1 Sevilla-Hurtado, Lorenzo
K1 Acero al carbono
K1 Redes neuronales artificiales
AB 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.
PB Springer Nature
YR 2026
FD 2026-02-02
LK https://hdl.handle.net/10630/46406
UL https://hdl.handle.net/10630/46406
LA eng
NO Bañon, F., Martín-Béjar, S., Bermudo, C. et al. High-precision predictive modeling of AWJM for S275 steel machining. ENG. Mech. Eng. 21, 100872 (2026). https://doi.org/10.1007/s11465-026-0872-8
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 3 may 2026