20.500.12556/DKUM-96651 Optimizing abrasive water jet milling of alumina ceramics with RBF neural networks Abrasive 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. abrasive water jet AWJ milling alumina ceramic precisio machining material removal rate single-factor experiment orthogonal array RFB neural network abrazivni vodni curek rezkanje natančna obdelava true false true Fakulteta za strojništvo, Inštitut za proizvodno strojništvo Angleški jezik Slovenski jezik https://apem-journal.org/index.html Članek v reviji 2026-01-22 13:08:06 2026-01-22 13:08:08 2026-01-23 03:13:53 0000-00-00 00:00:00 2025 0 0 str. 325-339 no. 3 Vol. 20 Sep. 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2025-06-16 2025-09-03 2025-10-31 658.5 265726467 10.14743/apem2025.3.543 1854-6250 APEM20-3_325-339.pdf APEM20-3_325-339.pdf 1 85938461EEDF8CD8C35F47B560C8E9B0 93d33ac314e67146e251674703ef8d7d07ac68ed200c7bf3486705cf2c475f4c 2b292fda-f790-11f0-a060-00155d00018d https://dk.um.si/Dokument.php?lang=slv&id=202411 https://apem-journal.org/Archives/2025/Abstract-APEM20-3_325-339.html 0 7d94b50e-f790-11f0-a060-00155d00018d https://dk.um.si/Dokument.php?lang=slv&id=202412 Fakulteta za strojništvo Advances in production engineering & management 0 0 0