Abstract
The use of adaptive feed rates shortens machining time and increases the potential of efficient machining. Currently, feed rates in an numerically controlled (NC) program are pre-set constant values that are often determined based on the job shop experiences gained over the years by skilled operators. There is no such way to adapt feed rate to on-going cutting conditions since the feed rate is set before the NC code is executed. In this study, an optimizer for canonical machining commands (optiCMC) has been developed. Fuzzy adaptive control is used to keep a constant cutting load by adjusting feed rate automatically to the cutting conditions. The results showed that optimum feed rates can be achieved and controlled during the machining process considering the machine tool's capabilities and limitations. The developed optiCMC helps achieve machining optimization, shorten machining time, and increase product quality.
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Liu Y, Guo X, Li W, Yamazaki K, Kashihara K, Fujishima M (2007) An intelligent NC program processor for CNC system of machine tool. Robot Comput Integr Manuf 23:160–169
Fred Proctor JM, Kramer T (1992) A methodology for integrating sensor feedback in machine tool controllers. Robot Systems Division, National Institute of Standards and Technology, Technology Administration, US Department of Commerce, Gaithersburg, 208991
Dong J, Ferreira PM, Stori JA (2007) Feed-rate optimization with jerk constraints for generating minimum-time trajectories. Int J Mach Tools Manuf 47:1941–1955
Abu Qudeiri J, Yamamoto H, Ramli R (2007) Optimization of operation sequence in CNC machine tools using genetic algorithm. J Adv Mech Des Syst Manuf 1:272–282
Selim M, Akturk SA (1996) Tool allocation and machining conditions optimization for CNC machines. Eur J Oper Res 94:335–348
Tounsi N, Bailey T, Elbestawi MA (2003) Identification of acceleration deceleration profile of feed drive systems in CNC machines. Int J Mach Tools Manuf 43:441–451
Wang S-M, Chiou C-H, Cheng Y-M (2004) An improved dynamic cutting force model for end-milling process. J Mater Process Technol 148:317–327
Rao VS, Rao PVM (2006) Effect of workpiece curvature on cutting forces and surface error in peripheral milling. Proc IMechE B J Eng Manuf 1399(220):1399–1407
Kim S-J, Lee HU, Cho D-W (2007) Prediction of chatter in NC machining based on a dynamic cutting force model for ball end milling. Int J Mach Tools Manuf 47:1827–1838
Rao BC, Shin YC (1999) A comprehensive dynamic cutting force model for chatter prediction in turning. Int J Mach Tools Manuf 39:1631–1654
Wang WP (1988) Solid modeling for optimizing metal removal of three-dimensional NC end milling. J Manuf Syst 7:57–65
Troitsky DI, Novikova MV, Bannatyne MW (2002) NC milling program optimization with 3D geometric modeling. International Conference Graphicon.
Ko JH, Yun WS, Cho D-W (2003) Off-line feed rate scheduling using virtual CNC based on an evaluation of cutting performance. Comput Aided Des 35:383–393
Qian L, Yang B, Lei S (2008) Comparing and combining off-line feed rate rescheduling strategies in free-form surface machining with feed rate acceleration and deceleration. Robot Comput Integr Manuf 24:796–803
Lee H, Cho DW (2003) An intelligent feed rate scheduling based on virtual machining. Int J Adv Manuf Technol 22:873–882
Richards ND, Fussell BK, Jerard RB (2002) Efficient NC machining using off-line optimized feed rates and on-line adaptive control. ASME Conf Proc 2002:181–191
Saturlay PV, Spence AD (2000) Integration of milling process simulation with on-line monitoring and control. Int J Adv Manuf Technol 16:92–99
Kim MK, Cho MW, Kim K (1994) Application of the fuzzy control strategy to adaptive force control of non-minimum phase end milling operations. Int J Mach Tools Manuf 34:677–696
Lian RJ, Lin BF, Huang JH (2005) Self-organizing fuzzy control of constant cutting force in turning. Int J Adv Manuf Technol 29:436–445
Shiuh-Jer H, Chiou-Yuarn S (1999) Fuzzy logic for constant force control of end milling. Ind Electron IEEE Trans 46:169–176
Liang M, Yeap T, Hermansyah A, Rahmati S (2003) Fuzzy control of spindle torque for industrial CNC machining. Int J Mach Tools Manuf 43:1497–1508
Xiaoli L, Han-Xiong L, Xin-Ping G, Du R (2004) Fuzzy estimation of feed-cutting force from current measurement-a case study on intelligent tool wear condition monitoring. Syst Man Cybern C Appl Rev IEEE Trans 34:506–512
Liang SY, Hecker RL, Landers RG (2004) Machining process monitoring and control: the state-of-the-art. J Manuf Sci Eng 126:297–310
Proctor FMKTR, John LM (1997) Canonical machining commands. Intelligent Systems Division, National Institute of Standards and Technology Administration, US Department of Commerce, Gaithersburg, 20899
Jassbi JSP, Ribeiro RA, Donati A (2006) Comparison of Mamdani and Sugeno fuzzy inference systems for a space fault detection application. Proceeding of the 2006 World Automation Congress (WAG 2006).
Driankov DHH, Reinfrank M (1996) An introduction to fuzzy control. Springer, Berlin
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Ridwan, F., Xu, X. & Ho, F.C.L. Adaptive execution of an NC program with feed rate optimization. Int J Adv Manuf Technol 63, 1117–1130 (2012). https://doi.org/10.1007/s00170-012-3959-9
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DOI: https://doi.org/10.1007/s00170-012-3959-9