NDLI: Composite adaptive control of belt polishing force for aero-engine blade

Content Provider	Springer Nature Link
Author	Zhsao, Pengbing Shi, Yaoyao
Copyright Year	2013
Abstract	The existing methods for blade polishing mainly focus on robot polishing and manual grinding. Due to the difficulty in high-precision control of the polishing force, the blade surface precision is very low in robot polishing, in particular, quality of the inlet and exhaust edges can not satisfy the processing requirements. Manual grinding has low efficiency, high labor intensity and unstable processing quality, moreover, the polished surface is vulnerable to burn, and the surface precision and integrity are difficult to ensure. In order to further improve the profile accuracy and surface quality, a pneumatic flexible polishing force-exerting mechanism is designed and a dual-mode switching composite adaptive control(DSCAC) strategy is proposed, which combines Bang-Bang control and model reference adaptive control based on fuzzy neural network(MRACFNN) together. By the mode decision-making mechanism, Bang-Bang control is used to track the control command signal quickly when the actual polishing force is far away from the target value, and MRACFNN is utilized in smaller error ranges to improve the system robustness and control precision. Based on the mathematical model of the force-exerting mechanism, simulation analysis is implemented on DSCAC. Simulation results show that the output polishing force can better track the given signal. Finally, the blade polishing experiments are carried out on the designed polishing equipment. Experimental results show that DSCAC can effectively mitigate the influence of gas compressibility, valve dead-time effect, valve nonlinear flow, cylinder friction, measurement noise and other interference on the control precision of polishing force, which has high control precision, strong robustness, strong anti-interference ability and other advantages compared with MRACFNN. The proposed research achieves high-precision control of the polishing force, effectively improves the blade machining precision and surface consistency, and significantly reduces the surface roughness.
Starting Page	988
Ending Page	996
Page Count	9
File Format	PDF
ISSN	10009345
Journal	Chinese Journal of Mechanical Engineering
Volume Number	26
Issue Number	5
e-ISSN	21928258
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-09-28
Publisher Place	Berlin, Heidelberg
Access Restriction	Subscribed
Subject Keyword	blade polishing force Bang-Bang control fuzzy neural network model reference adaptive control Mechanical Engineering Theoretical and Applied Mechanics Manufacturing, Machines, Tools Engineering Thermodynamics, Heat and Mass Transfer Power Electronics, Electrical Machines and Networks Electronics and Microelectronics, Instrumentation
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Mechanical Engineering

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