Control of Cutting Force for Creep-Feed Grinding Processes Using a Multi-Level Fuzzy Controller

Chengying Xu1, Yung C. Shin1
1School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907

Tóm tắt

In this paper, a multi-level fuzzy control (MLFC) technique is developed and implemented for a creep-feed grinding process. The grinding force is maintained at the maximum allowable level under varying depth of cut, so that the highest metal removal rate is achieved with a good workpiece surface quality. The control rules are generated heuristically without any analytical model of the grinding process. Based on the real-time force measurement, the control parameters are adapted automatically within a stable range. A National Instrument real-time control computer is implemented in an open architecture control system for the grinding machine. Experimental results show that the cycle time has been reduced by up to 25% over those without force control and by 10–20% compared with the conventional fuzzy logic controller, which indicates its effectiveness in improving the productivity of actual manufacturing processes. The effect of grinding wheel wear is also considered in the creep-feed grinding process, where the grinding force/power can be maintained around the specified value by the proposed MLFC controller as the wheel dulls gradually.

Từ khóa


Tài liệu tham khảo

Brinksmeier, A Selftuning Adaptive Control System for Grinding Processes, CIRP Ann., 40, 355, 10.1016/S0007-8506(07)62005-8

Hahn, Some Characteristics of Controlled Force Grinding, Proceeding of the Sixth International Machine Tool Design Research Conference, 597

Jenkins, Design of a Robust Controller for a Grinding System, IEEE Trans. Control Syst. Technol., 4, 40, 10.1109/87.481765

Ulrich, Analysis of the Robotic Disc Grinding Process, Int. J. Adv. Manuf. Technol., 7, 82, 10.1007/BF02601574

Whitney, Development of an Automated Robotic Weld Bead Grinding System, ASME J. Dyn. Syst., Meas., Control, 112, 166, 10.1115/1.2896123

Werner, Influence of Work Material on Grinding Forces, CIRP Ann., 27, 243

Tönshoff, H. K., Zinngrebe, M., and Kemmerling, M., 1991, “Optimization of Internal Grinding by Microcomputer-Based Force Control,” Control of Manufacturing Processes, ASME Winter Annual Meeting, DSC Division, Atlanta, GA, Dec. 1–6, Vol. 28, pp. 67–77.

Jenkins, Adaptive Pole-Zero Cancellation in Grinding Force Control, IEEE Trans. Control Syst. Technol., 7, 363, 10.1109/87.761056

Guo, L., Schöne, A., and Ding, X., 1993, “Grinding Force Control Using Nonlinear Adaptive Strategy,” 12th World Congress IFAC, Sydney, Australia, July 18–23, Vol. 5, pp. 459–462.

Shin, Neuro-Fuzzy Control of Complex Manufacturing Processes, Int. J. Prod. Res., 34, 3291, 10.1080/00207549608905091

Rowe, Applications of Artificial Intelligence in Grinding, CIRP Ann., 43, 521, 10.1016/S0007-8506(07)60498-3

Zhu, Control of Machine Tools Using the Fuzzy Control Technique, CIRP Ann., 31, 347, 10.1016/S0007-8506(07)63326-5

Chen, Y. T., and Shin, Y. C., 1991, “A Surface Grinding Process Advisory System With Fuzzy Logic,” Control of Manufacturing Processes, ASME Winter Annual Meeting, DSC Division, Atlanta, GA, Dec. 1–6, Vol. 28, pp. 67–77.

Lee, Intelligent Model-Based Optimization of the Surface Grinding Process for Heat-Treated 4140 Steel Alloys With Aluminum Oxide Grinding Wheels, ASME J. Manuf. Sci. Eng., 125, 65, 10.1115/1.1537738

Zhao, Fuzzy Pattern Recognition and Automatic Steady Control in Roller Grinding, Proceedings of 2nd IEEE International Conference of Computer Integrated Manufacturing, 395

Xu, Design of a Multi-Level Fuzzy Controller and Stability Analysis of Nonlinear Processes, IEEE Trans. Fuzzy Syst., 13, 761, 10.1109/TFUZZ.2005.859308

Farinwata, Fuzzy Control Synthesis and Analysis

Calcev, Some Remarks on the Stability of Mamdani Fuzzy Control Systems, IEEE Trans. Fuzzy Syst., 6, 436, 10.1109/91.705511

Aracil, Stability Issues in Fuzzy Control

Ying, Practical Design of Nonlinear Fuzzy Controllers With Stability Analysis for Regulating Processes With Unknown Mathematical Models, Automatica, 30, 1185, 10.1016/0005-1098(94)90213-5

Procyk, A Linguistic Self-Organizing Process Controller, Automatica, 15, 15, 10.1016/0005-1098(79)90084-0

Lee, Fuzzy Logic in Control System: Fuzzy Logic Controller—Part I, IEEE Trans. Syst. Man Cybern., 20, 404, 10.1109/21.52551

Passino, Fuzzy Control

Linkens, Self-Organising Fuzzy Logic Control and the Selection of its Scaling Factors, Trans. Inst. Meas. Control (London), 14, 114, 10.1177/014233129201400301

Hsu, Fuzzy Adaptive Control of Machining Processes With a Self-Learning Algorithm, ASME J. Manuf. Sci. Eng., 118, 522, 10.1115/1.2831062

Haber, Hierarchical Fuzzy Control of the Milling Process With a Self-Tuning Algorithm, Proceedings of the 2000 IEEE International Symposium on Intelligent Control, 115

Rober, Modeling and Control of CNC Machines Using a PC-Based Open Architecture Controller, Mechatronics, 5, 401, 10.1016/0957-4158(95)00009-T

Amitay, Adaptive Control Optimization of Grinding, ASME J. Eng. Ind., 103, 103, 10.1115/1.3184449

Malkin, Burning Limits for Surface and Cylindrical Grinding of Steels, CIRP Ann., 27, 233

Younis, A New Approach to Development of a Grinding Force Model, ASME J. Eng. Ind., 109, 306, 10.1115/1.3187133

Saini, Wheel Hardness and Local Elastic Deflections in Grinding, Int. J. Mach. Tools Manuf., 30, 637, 10.1016/0890-6955(90)90013-9

Saini, Local Contact Deflections and Forces in Grinding, CIRP Ann., 34, 281, 10.1016/S0007-8506(07)61773-9

Chiu, Computer Simulation for Cylindrical Plunge Grinding, CIRP Ann., 42, 383, 10.1016/S0007-8506(07)62467-6

Brenner, Wheel Sharpness Measurement for Force Prediction in Grinding, Wear, 160, 317, 10.1016/0043-1648(93)90436-P

Malkin, The Wear of Grinding Wheels, Part I—Attritious Wear, ASME J. Eng. Ind., 93, 1120, 10.1115/1.3428051

Malkin, Thermal Aspects of Grinding: Part I—Energy Partition, ASME J. Eng. Ind., 96, 1177, 10.1115/1.3438492

Furukawa, Adaptive Control of Creep Feed Grinding to Avoid Workpiece Burn, Proceedings of the 5th International Conference on Production Engineering, 64

Kirk, Wheel Sharpness Measurement for Force Prediction in Grinding, Wear, 160, 317, 10.1016/0043-1648(93)90436-P

Calcev, Passivity Approach to Fuzzy Control Systems, Automatica, 34, 339, 10.1016/S0005-1098(97)00202-1

Calcev, A Passivity Result for Fuzzy Control Systems, Proceedings of the 35th Conference on Decision and Control, 2727