Remote Servo Tuning System for Multi-Axis CNC Machine Tools Using a Virtual Machine Tool Approach
Source abstract
Abstract
Servo systems affect the performances of machining in accuracy and surface quality for high speed and precision machine tools. This study introduces an efficient servo tuning technique for Computer Numerical Control (CNC) feed drive systems using particle swarm optimization (PSO) algorithm by virtual machine tool approach. The proposed approach contained a system identification phase and a servo tuning phase based on the same bandwidth for all axes feed drive systems. The PSO algorithm was adopted to obtain the system parameters and maximize the corresponding bandwidth. An efficient two-step servo tuning method based on gain and phase margins was proposed for high speed and precision requirements. All feed drive systems controller gains were optimized simultaneously for synchronization. A remote system called Machine Dr. was established for servo tuning and monitoring. Simulation and experimental results were introduced to illustrate the effectiveness of the proposed approach.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Iterative Learning Control Systems · Advanced machining processes and optimization · Advanced Surface Polishing Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mechanical Engineering, National Chung Hsing University, Taichung 40227, Taiwan
Funding
Ministry of Science and Technology, Taiwan
References (33)
Show source-provided references
1. Machine Tool Feed Drives and Their Control—A Survey of the State of the Art10.1115/1.2836818 ↗
2. Contour error control of CNC machine tools with vibration avoidance10.1016/j.cirp.2012.03.132 ↗
3. Cross-coupled biaxial computer control for manufacturing system10.1115/1.3149612 ↗
4. Zaeh, M.F., Kleinwort, R., Fagerer, P., and Altintas, Y. (2017). Automatic tuning of active vibration control systems using inertial actuators. CIRP Ann. Manuf. Technol.10.1016/j.cirp.2017.04.051 ↗
5. Altintas, Y., and Aslan, D. (2017). Integration of virtual and on-line machining process control and monitoring. CIRP Ann. Manuf. Technol.10.1016/j.cirp.2017.04.047 ↗
6. Virtual High Performance Machining10.1016/j.procir.2016.04.154 ↗
7. Generalized modelling of cutting tool geometries for unified process simulation10.1016/j.ijmachtools.2016.01.007 ↗
8. Beudaert, X., Mancisidor, I., Ruiz, L.M., Barrios, A., Erkorkmaz, K., and Munoa, J. (2015, January 18–20). Analysis of the feed drives control parameters on structural chatter vibrations. Proceedings of the XIIIth International Conference on High Speed Machining (HSM2016), Nanjing, China.
9. Multi-Motor sychronization techniques
10. Modeling and control of twin parallel-axis linear servo mechanisms for high-speed machine tools10.5875/ausmt.v1i1.72 ↗
11. Sencer, B., and Tajima, S. (2017). Frequency Optimal Feed Motion planning in computer numerical controller machine tools for vibration avoidance. J. Manuf. Sci. Eng., 139.10.1115/1.4034140 ↗
12. Identification of 5-axis machine tools feed drive systems for contouring simulation10.20965/ijat.2011.p0377 ↗
13. A systematic approach to design high-performance feed drive systems10.1016/j.ijmachtools.2005.01.032 ↗
14. Wong, W.W.-S. (2007). Rapid Identification of virtual CNC drives. [Master’s Thesis, University of Waterloo].
15. Virtual computer numerical control system10.1016/S0007-8506(07)60444-2 ↗
16. Virtual machine tool10.1016/S0007-8506(07)60022-5 ↗
17. Lin, M.C. (2014). A Study of the CNC Parameter Tuning criteria according to the machining purposes. [Master’s Thesis, National Chung Hsing University].
18. Chang, C.H. (2014). Study of virtual CNC servo control technology. [Master’s Thesis, National Chung Hsing University].
19. Design of linear motor servo systems under the influence of structural vibrations10.2493/jjspe.66.122 ↗
20. A study on total tuning of feed drive systems of NC machine tools (2nd Report)—Single Axis Tuning10.2493/jjspe.61.268 ↗
21. Matsubara, A., Umemoto, M., Tanaka, T., Ibaraki, S., and Kakino, Y. Vibration analysis of feed drive systems including base dynamics. Proceedings of the 2002 Annual Meeting of JSPE Kansai Division, Osaka, Japan.
22. Heidenhain (2014). iTNC 530 HSCI—The Versatile Contouring Control for Milling, Drilling, Boring Machines and Machining Centers, Heidenhain Co.
23. Kennedy, J., and Eberhart, R.C. (December, January 27). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia.
24. Efficient collision-free path-planning of multiple mobile robots system Using Efficient Artificial Bee Colony Algorithm10.1016/j.advengsoft.2014.09.006 ↗
25. Species-based improved electromagnetism-like mechanism algorithm for TSK-type Interval-valued neural fuzzy system optimization10.1016/j.fss.2011.02.004 ↗
26. Network-based type-2 fuzzy system with water flow like algorithm for system identification and signal processing10.1080/23080477.2015.11665633 ↗
27. Nonlinear systems design by a novel fuzzy neural system via hybridization of EM and PSO algorithms10.1016/j.ins.2011.09.036 ↗
28. Chao, C.T., Sutarna, N., Chiou, J.S., and Wang, C.J. (2017). Equivalence between fuzzy PID controllers and conventional PID controllers. Appl. Sci., 7.10.3390/app7060513 ↗
29. Chen, L.H., and Peng, C.C. (2017). Extended backstepping sliding controller design for chattering attenuation and its application for servo motor control. Appl. Sci., 7.10.3390/app7030220 ↗
30. Su, H.Y., Hsu, Y.L., and Chen, Y.C. (2016). PSO-based voltage control strategy for loadability enhancement in smart power grids. Appl. Sci., 6.10.3390/app6120449 ↗
31. Virtual CNC system. Part I. System architecture10.1016/j.ijmachtools.2005.08.002 ↗
32. A servo parameter tuning method for high-speed nc machine tools based on contouring error measurement
33. Lee, W.H. (2002). Improvement of Contour Errors Using Cross-Coupled Control. [Master’s Thesis, National Sun Yat-sen University].