Research on intelligent assembly method of aero-engine multi-stage rotors based on SVM and variable-step AFSA-BP neural network. (October 2022)
- Record Type:
- Journal Article
- Title:
- Research on intelligent assembly method of aero-engine multi-stage rotors based on SVM and variable-step AFSA-BP neural network. (October 2022)
- Main Title:
- Research on intelligent assembly method of aero-engine multi-stage rotors based on SVM and variable-step AFSA-BP neural network
- Authors:
- Mei, Yingjie
Sun, Chuanzhi
Li, Chengtian
Liu, Yongmeng
Tan, Jiubin - Abstract:
- 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 actualAbstract: 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. … (more)
- Is Part Of:
- Advanced engineering informatics. Volume 54(2022)
- Journal:
- Advanced engineering informatics
- Issue:
- Volume 54(2022)
- Issue Display:
- Volume 54, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 54
- Issue:
- 2022
- Issue Sort Value:
- 2022-0054-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-10
- Subjects:
- Aero-engine -- SVM -- AFSA-BP -- Coaxiality -- Unbalance amount -- Perpendicularity
Computer-aided engineering -- Periodicals
Engineering -- Data processing -- Periodicals
620.00285 - Journal URLs:
- http://www.sciencedirect.com/science/journal/14740346 ↗
http://books.google.com/books?id=KhFVAAAAMAAJ ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.aei.2022.101798 ↗
- Languages:
- English
- ISSNs:
- 1474-0346
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 0696.851100
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 24447.xml