| Title: | Optimizing abrasive water jet milling of alumina ceramics with RBF neural networks |
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| Authors: | ID Feng, Y.T. (Author)
ID Shi, Z.R. (Author)
ID Yang, X. (Author)
ID Huang, W. (Author)
ID Luo, X. (Author)
ID Li, Y.H. (Author)
ID Yu, L. (Author) |
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| Files: |  APEM20-3_325-339.pdf (1,02 MB)
MD5: 85938461EEDF8CD8C35F47B560C8E9B0
 https://apem-journal.org/Archives/2025/Abstract-APEM20-3_325-339.html
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FS - Faculty of Mechanical Engineering
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| Abstract: | 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. |
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| Keywords: | abrasive water jet, AWJ, milling, alumina ceramic, precisio machining, material removal rate, single-factor experiment, orthogonal array, RFB neural network |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 16.06.2025 |
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| Article acceptance date: | 03.09.2025 |
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| Publication date: | 31.10.2025 |
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| Publisher: | Fakulteta za strojništvo, Inštitut za proizvodno strojništvo |
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| Year of publishing: | 2025 |
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| Number of pages: | str. 325-339 |
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| Numbering: | Vol. 20, no. 3 |
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| PID: | 20.500.12556/DKUM-96651  |
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| UDC: | 658.5 |
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| ISSN on article: | 1854-6250 |
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| COBISS.SI-ID: | 265726467 |
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| DOI: | 10.14743/apem2025.3.543 |
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| Publication date in DKUM: | 22.01.2026 |
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| Views: | 167 |
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| Downloads: | 5 |
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| Metadata: |    |
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| Categories: | Misc.
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