Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/2882
標題: 智慧化自適應切削控制
Intelligent Adaptive Cutting Control Techniques
作者: 高永銘
Kao, Yung-Ming
關鍵字: 智慧化控制;Intelligent Control;自適應控制;電腦數值控制;Adaptive Control;Computer Numerical Control
出版社: 機械工程學系所
引用: 1. A. G.. Ulsoy and Y. Koren “Applications of Adaptive Control to Machine Tool Process Control,” IEEE on Control Systems Magazine, Vol. 9, No. 4, pp. 33-37, 1989. 2. O. Masory, Y. Koren., “Adaptive Control System for Turning,” CIRP Annals - Manufacturing Technology, Vol. 29, No. 1, pp. 281-284, 1980. 3. B.-S. Chen, Yi.-F. Chang., “Constant Turning Force Adaptive Controller Design,” Journal of Engineering for Industry, Vol. 111, pp. 125-132, 1989. 4. K.-D. Bouzakisa, K. Efstathiou and R. Paraskevopoulou, ”NC-Code Preparation with Optimum Cutting Conditions in 3-Axis Milling,” CIRP Annals - Manufacturing Technology, Vol. 41, No. 1, pp. 513-516, 1992. 5. B.K. Fussell and NHK. Srinivasan, “Adaptive Control of Force in End Milling Operations- an Evaluation of Available Algorithms,” Journal of Manufacturing Systems, Vol. 10, No. 1, pp. 8-20, 1991. 6. Y.S. Tarng, S.T. Hwang. and Y.S. Wang, “A Neural Network Controller for Constant Turing Force,” International Journal of Machine Tools and Manufacture, Vol. 34, No. 4, pp. 643-650, 1990. 7. L. Harder, M. Nicolescu and B. Lindstom, “Stochastic Modelling and Online Adaptive Control of Cutting Force in Turning,” CIRP Annals - Manufacturing Technology, Vol. 43, No. 1, pp. 373-377, 1994. 8. Y. Altintas, “Direct Adaptive Control of End Milling Process,” International Journal of Machine Tools and Manufacture, Vol. 34, No. 4, pp. 461-472, 1994. 9. S.J. Bober and Y.C. Shin, and O.D.I. Nwokah “A Digital Robust Controller for Cutting Force Control in the End Milling Process,” ASME Journal of Dynamic System for Measurement and Control, Vol. 119, No. 6, pp. 146-152, 1997. 10. K. Matsushima and P. Bertok, “In Process Detection of Tool Breakage by Monitoring the Spindle Current of a Machine Tool,” Journal of Measurement and Control for Batch Manufacturing, pp. 145-154, 1982. 11. M.A. Mannan and S. Broms and KTH Stockholm, “Monitoring and Adaptive Control of Cutting Process by Means of Motor Power and Current Measurement,” CIRP Annals - Manufacturing Technology, Vol. 38, No. 1, pp. 347-350, 1989. 12. B.Y. Lee and Y.S. Tarng, “Application of the Discrete Wavelet Transform to the Monitoring of Tool Failure in End Milling Using the Spindle Motor Current,” International Journal of Advanced Manufacturing Technology, Vol. 13, pp. 37-34, 1997. 13. X. Li and A. Djordjevich and P. K. Venuvinod, “Current-sensor-based Feed Cutting Force Intelligent Estimation and Tool Wear Condition Monitoring,” IEEE Transactions on Industrial Electronics, Vol. 47, No. 3, pp. 697-702, 2000. 14. OMAT, System Configurations – Real Time Adaptive Control & Monitoring (ACM) for CNC Metal Cutting Optimization, 2007. 15. ARTIS, Website www.artis.de. 16. HEIDENHAIN, User’s Manual HEIDENHAIN Conversational Programming, 2010. 17. SIEMENS, SINUMERIK & SIMODRIVE Catalog NC60, 2009. 18. K. Srinivasan and T. C. Tsao, “Machine Tool Feed Drives and Their Control – A Survey of the State of the Art,” Journal of Manufacturing Science and Engineering, Vol. 19, No. 4B, pp. 743-748, 1997. 19. H. Nolzen and R. Isermann, “Adaptive Control of the Cutting Power for Milling Operation,” Proceedings of the American Control Conference, pp. 2185-2189, 1995. 20. Y. Altintas, I. Yellowley and J. Tlusty, “The Detection of Tool Breakage in Milling Operations”, Journal of Engineering for Industry, Vol. 110, No. 3, pp. 271-279, 1988. 21. R.-F. Fung, K.-W. Chen and J.-Y. Yeh, “Fuzzy Sliding Mode Controlled Slider-crank Mechanism using a PM Synchronous Servo Motor Drive,” Int. .J Mechanical Sci., Vol.41, pp. 337-355, 1999. 22. S.-C. Lin, Y.-Y. Chen, “Design of Adaptive Fuzzy Sliding Mode for Nonlinear System Control,” IEEE Int. Conf. on Fuzzy Systems, Orlando, pp. 35-39, 1994.
摘要: 
本研究之目的為探討利用刀具移除工件素材所決定的進給速度與刀具轉速兩因素,欲改善加工效率調變給予的因素,分析主軸驅動器的輸出功率與進給速度的關係,並搭配使用Omative ACM來驗證分析得法則正確性。
本文的研究步驟如下,首先闡述影響材料移除效率高低因素及文獻回顧,進而介紹智慧自適應控制現在商品化的產品技術應用在CNC工具機控制器上的現有技術現況與種類。然後藉由主軸功率推導數學模式,再導入滑動模糊控制技術,進而導入模糊動模糊控制技術以推導出此智慧自適應控制技術。推導理論後在以實際機台驗證加工,從驗證平台規格選擇、加工的工件設計、測試平台安裝到實際切削驗證的程序後,得到所需要加工數據。在未啟動與啟動智慧化自適應切削結果顯示對粗胚加工效率提升43%,證明理論分析確實有效。

The purpose of this research is to explore federate and spindle speed command for remove the workpiece material. If we want to improve working efficiency to adjust condition, analysis the output power of spindle driver and feedrate ,We can connect the Omative ACM module to verify this analysis are correct.
The procedure of this research are divide into the following steps: (1)conceptual about working efficiency of remove work piece (2) Describe Intelligent Adaptive Control Techniques apply to machine tool of controller market now. (3) Intelligent Adaptive Control Techniques design from math formula to Adaptive Fuzzy Sliding Mode Control, (4) Working and verify. (5) Conclude this research can enhance the rough working efficiency to 43%.
URI: http://hdl.handle.net/11455/2882
其他識別: U0005-2208201220481300
Appears in Collections:機械工程學系所

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