Optimizing abrasive water jet milling of alumina ceramics with RBF neural networksFeng, Y.T. (Avtor) Shi, Z.R. (Avtor) Yang, X. (Avtor) Huang, W. (Avtor) Luo, X. (Avtor) Li, Y.H. (Avtor) Yu, L. (Avtor) abrasive water jetAWJmillingalumina ceramicprecisio machiningmaterial removal ratesingle-factor experimentorthogonal arrayRFB neural networkAbrasive water jet technology is an advanced machining method that combines high-pressure water jet with solid abrasives. Owing to its unique coldprocessing characteristics, high flexibility, and environmental bebefits, it has been widely applied in aerospace, medical devices, microelectronics, defense and other fields. Focusing on alumina ceramic plates, this study systematically investigates abrasive water jet (AWJ) milling through an integrated experimental and modeling approach. The research framework consists of three main phases: the development of an experimental design for abrasive water jet milling of alumina ceramics; systematic parameter optimization using single-factor and orthogonal array experiments, with material removal rate and milling depth as key performance indicators; and the application of a radial basis function (RBF) neural network model for milling depth prediction. The experimental results demonstrate that optimal parameter combinations improve machining efficiency by 38 % compared to baseline conditions. The developed RBF model achieves exceptional predictive accuracy, with maximum absolute and relative errors of 0.30 mm and 18.8 %, respectively, and a mean absolute error of 12.01 % across validation trials. This work provides a theoretical foundation for precision machining of advanced ceramics while demonstrating a viable pathway toward intelligent process optimization in AWJ technology.Fakulteta za strojništvo, Inštitut za proizvodno strojništvo20252026-01-22 13:08:06Članek v reviji96651UDK: 658.5COBISS_ID: 265726467DOI: 10.14743/apem2025.3.543ISSN pri članku: 1854-6250sl