Research on intelligent assembly method of aero-engine multi-stage rotors based on SVM and variable-step AFSA-BP neural network |
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| Affiliation: | 1. Center of Ultra-precision Optoelectronic Instrument Engineering, Harbin Institute of Technology, Harbin 150080, China;2. Key Lab of Ultra-precision Intelligent Instrumentation Engineering (Harbin Institute of Technology), Ministry of Industry and Information Technology, Harbin 150080, China;3. Institute of Reactor Operation and Application, Nuclear Power Institute of China, Chengdu 610000, China;1. Tainan Hydraulics Laboratory, National Cheng Kung University, Tainan, 70955, Taiwan;2. Department of Hydraulic and Ocean Engineering, National Cheng Kung University, Tainan, 70101, Taiwan;1. I-Form Advanced Manufacturing Research Center, Dublin City University, Dublin, Ireland;2. School of Mechanical and Manufacturing Engineering, Dublin City University, Dublin, Ireland;3. Advanced Processing Technology Research Center, Dublin City University, Dublin, Ireland;1. CNR-Institute of Atmospheric Pollution Research, Division of Rende, UNICAL Polifuzionale, 87036 Rende (CS), Italy;2. CNR-Institute of Atmospheric Pollution Research, Division of Florence, Via Madonna del Piano, 10 50019 Sesto Fiorentino, Italy;3. Institute for Environmental Research and Sustainable Development, National Observatory of Athens, 15236 P. Penteli, Athens, Greece;4. RECETOX, Faculty of Science, Masaryk University, Kamenice 753/5 Brno 625 00, Czech Republic;5. enviroSPACE, Institute for Environmental Sciences, University of Geneva, 66 Bd. Carl-Vogt, CH-1205, Geneva, Switzerland;6. Dpt. F.-A. Forel for Environment and Water Sciences, Faculty of Sciences, University of Geneva, 66 Bd. Carl-Vogt, CH-1205, Geneva, Switzerland;7. CREAF, Campus de Bellaterra (UAB) Edifici C 08193 Cerdanyola del Vallès, Spain;8. University of Helsinki, P.O. Box 64, FI-00014, Finland;1. Department of Electric Power Systems, Melentiev Energy Systems Institute of SB RAS, 664033, Irkutsk, Russia;2. Department of Complex and Regional Problems in Energy, Melentiev Energy Systems Institute of SB RAS, 664033, Irkutsk, Russia;3. Department of Applied Mathematics, Melentiev Energy Systems Institute Melentiev Energy Systems Institute of SB RAS, 664033, Irkutsk, Russia;4. Department of Thermal Power Systems, Melentiev Energy Systems Institute Melentiev Energy Systems Institute of SB RAS, 664033, Irkutsk, Russia;5. College of Information and Electrical Engineering, China Agricultural University, 100083, Beijing, China;1. State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin, 300350, China;2. School of Civil Engineering, Tianjin University, Tianjin, 300350, China;3. National Ocean Technology Center, Tianjin, 300112, China;4. Luanhe River Water Quality Monitoring Center of Haihe River Basin, Luan River Diversion Project Management Bureau of Haihe River Water Conservancy Commission, Tangshan, Hebei, 064309, China;5. State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research;6. Institute of Water Ecology and Environment, China Institute of Water Resources and Hydropower Research, Beijing, 100038, China |
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| Abstract: | The quality of the aero-engine rotors assembly determines the overall performance of the engine. Aiming at the problems of rotors assembly with different plane types, we proposes a rotor plane classification method based on SVM by using the profile data of PCA dimension reduction. Meanwhile, for the unilateral-tilt plane rotors, the three-objective rotors assembly method of coaxiality, unbalance amount and perpendicularity based on the rigid rotor model is established. For the hyperbolic paraboloid rotors, an intelligent assembly method based on AFSA-BP neural network for coaxiality, unbalance amount and perpendicularity is established. The experiment is based on the double-column ultra-precision measuring instrument and V4L vertical balancing machine and HL5UB horizontal balancing machine to measure rotors geometry and unbalance data. The experimental results show that the plane type classification accuracy can reach 99 %. The prediction error of the coaxiality of the unilateral-tilt plane rotors assembly is 5.1 μm, the prediction error of the unbalance amount is 196 g·mm, and the prediction error of the perpendicularity is 0.6 μm. The average prediction error of the coaxiality of the hyperbolic paraboloid rotors assembly is 0.9 μm, and the average prediction error of the unbalance amount is 73 g·mm, and the average prediction error of the perpendicularity is 0.2 μm. Our method provides a reliable assembly solution for aero-engine rotors assembly and meets actual assembly requirements. |
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| Keywords: | Aero-engine SVM AFSA-BP Coaxiality Unbalance amount Perpendicularity |
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