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An Experimental Study on Fuzzy Controller Robustness Augmented by Fuzzy Supervisor for Cutting Force Control of End-Milling | ||
| AUT Journal of Mechanical Engineering | ||
| مقاله 1، دوره 6، شماره 2، شهریور 2022، صفحه 167-178 اصل مقاله (861.45 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/ajme.2021.19774.5966 | ||
| نویسنده | ||
| Mohsen Abedi* | ||
| Faculty of Satellite Research Institute, Iranian Space Research Center, Tehran, Iran | ||
| چکیده | ||
| In this paper, the robustness of controlling the cutting force of an end-milling process using a supervisory fuzzy controller has been experimentally investigated. In the proposed controller an ordinary fuzzy controller is augmented by implementing a fuzzy supervisor. The ordinary fuzzy controller is the highly-used cutting force fuzzy controller due to its advantages. However, because of participating only one cutting parameter in its structure, i.e. mostly feed-rate, the ordinary fuzzy controller is suitable only when other parameters such as depth of cut, spindle speed, etc. have a small amount of variations. Since in practice this assumption is not valid, an ordinary fuzzy controller needs to be augmented. This augmentation has had suggestions in the literature. However, these suggestions never have been on the basis of the fuzzy logic; while fuzzy being is the major advantage of ordinary fuzzy controller for controlling cutting force. Due to this deficit, in this paper, a supervisory fuzzy controller has been added to the system. The designed fuzzy supervisor inspects the dynamic behavior of the cutting force, estimates a pre-defined sensitivity parameter, and cancels the output fluctuations arising from this parameter’s variation. The numerous experiments conducted in different cutting situations proved that this supervisory structure increases the robustness and applicability of the fuzzy controller with respect to an ordinary fuzzy controller. These experiments are conducted on two different computerized numerical control machines to confirm the efficiency of the presented method. | ||
| کلیدواژهها | ||
| Fuzzy supervisor؛ Robust controller؛ Cutting force؛ Experimental validation؛ Adaptive system | ||
| مراجع | ||
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[1] S. Huang, K.K. Tan, G.S. Hong, Y.S. Wong, Cutting force control of milling machine, Mechatronics, 17(10) (2007) 533-541.
[2] Y. Park, T.-Y. Kim, J. Woo, D. Shin, J. Kim, Sliding mode cutting force regulator for turning processes, International Journal of Machine Tools and Manufacture, 38(8) (1998) 911-930.
[3] R. Haber-Haber, R. Haber, M. Schmittdiel, R.M.d. Toro, A classic solution for the control of a high-performance drilling process, International Journal of Machine Tools & Manufacture, 47 (2007) 2290-2297.
[4] P. Gallina, N. Scuor, G. Mosetti, Delayed-reference control (DRC) applied to machining operations, International Journal of Machine Tools and Manufacture, 45(12-13) (2005) 1386-1392.
[5] A.P. Singh, M. Sharma, I. Singh, Optimal control of thrust force for delamination-free drilling in glass-fiber-reinforced plastic laminates, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 231(13) (2016) 2396-2407.
[6] P. Pascutto, P. Gallina, Improvement of a delayed velocity reference control (DVRC) for machining operations, International Journal of Machine Tools and Manufacture, 47(3) (2007) 496-508.
[7] M.-Y. Yang, T.-M. Lee, Hybrid adaptive control based on the characteristics of CNC end milling, International Journal of Machine Tools & Manufacture, 42 (2002) 489-499.
[8] T. Szecsi, Cutting force modeling using artificial neural networks, Journal of Materials Processing Technology, 92 (1999) 344-349.
[9] M. Hanief, M.F. Wani, M.S. Charoo, Modeling and prediction of cutting forces during the turning of red brass (C23000) using ANN and regression analysis, Engineering Science and Technology, an International Journal, 20(3) (2017) 1220-1226.
[10] P.V. Badiger, V. Desai, M.R. Ramesh, B.K. Prajwala, K. Raveendra, Cutting Forces, Surface Roughness and Tool Wear Quality Assessment Using ANN and PSO Approach During Machining of MDN431 with TiN/AlN-Coated Cutting Tool, Arabian Journal for Science and Engineering, 44(9) (2019) 7465-7477.
[11] U. Zuperl, F. Cus, B. Mursec, T. Ploj, A generalized neural network model of ball-end milling force system, Journal of Materials Processing Technology, 175(1) (2006) 98-108.
[12] H. Aouici, M.A. Yallese, K. Chaoui, T. Mabrouki, J.-F. Rigal, Analysis of surface roughness and cutting force components in hard turning with CBN tool: Prediction model and cutting conditions optimization, Measurement, 45(3) (2012) 344-353.
[13] S.L.M.R. Filho, R.B.D. Pereira, C.H. Lauro, L.C. Brandão, Investigation and modelling of the cutting forces in turning process of the Ti-6Al-4V and Ti-6Al-7Nb titanium alloys, The International Journal of Advanced Manufacturing Technology, 101(9) (2019) 2191-2203.
[14] M.C. Yoon, Y.G. Kim, Cutting dynamic force modelling of endmilling operation, Journal of Materials Processing Tech., 155-156(Complete) (2004) 1383-1389.
[15] M. Imad, A. Hosseini, H.A. Kishawy, Optimization Methodologies in Intelligent Machining Systems - A Review, IFAC-PapersOnLine, 52(10) (2019) 282-287.
[16] R. Haber, J. Alique, S. Ros, C.R. Peres, Fuzzy supervisory control of end milling process, Information Sciences, 89(1) (1996) 95-106.
[17] U. Zuperl, F. Cus, M. Milfelner, Fuzzy control strategy for an adaptive force control in end-milling, Journal of Materials Processing Technology, 164 (2005) 1472-1478.
[18] P.B. Huang, An intelligent neural-fuzzy model for an in-process surface roughness monitoring system in end milling operations, Journal of Intelligent Manufacturing, 27(3) (2016) 689-700.
[19] L.C. Moreira, W. Li, X. Lu, M. Fitzpatrick, Supervision controller for real-time surface quality assurance in CNC machining using artificial intelligence, Comput. Ind. Eng., 127 (2019) 158-168.
[20] X. Liu, F. Zhao, X. Mei, T. Tao, J. Shen, High-efficiency gear hobbing technics based on fuzzy adaptive control of spindle torque, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 233(10) (2018) 3331-3345.
[21] M. Liang, T. Yeap, A. Hermansyah, S. Rahmati, Fuzzy control of spindle torque for industrial CNC machining, International Journal of Machine Tools and Manufacture, 43(14) (2003) 1497-1508.
[22] C.-J. Lin, C.-H. Lin, S.-H. Wang, Using Fuzzy Control for Feed Rate Scheduling of Computer Numerical Control Machine Tools, Applied Sciences, 11(10) (2021) 4701.
[23] T.H. Liu, Dongwang, Constant Cutting Force Control for CNC Machining Using Dynamic Characteristic-Based Fuzzy Controller, in, 2015.
[24] L. Wang, X. Yuan, H. Si, F. Duan, Feedrate scheduling method for constant peak cutting force in five-axis flank milling process, Chinese Journal of Aeronautics, 33(7) (2020) 2055-2069. | ||
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