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Adaptive execution of an NC program with feed rate optimization

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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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References

  1. 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

    Article  Google Scholar 

  2. 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

    Google Scholar 

  3. 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

    Article  Google Scholar 

  4. 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

    Google Scholar 

  5. Selim M, Akturk SA (1996) Tool allocation and machining conditions optimization for CNC machines. Eur J Oper Res 94:335–348

    Article  MATH  Google Scholar 

  6. 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

    Article  Google Scholar 

  7. 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

    Article  Google Scholar 

  8. 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

    Article  Google Scholar 

  9. 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

    Article  Google Scholar 

  10. Rao BC, Shin YC (1999) A comprehensive dynamic cutting force model for chatter prediction in turning. Int J Mach Tools Manuf 39:1631–1654

    Article  Google Scholar 

  11. Wang WP (1988) Solid modeling for optimizing metal removal of three-dimensional NC end milling. J Manuf Syst 7:57–65

    Article  Google Scholar 

  12. Troitsky DI, Novikova MV, Bannatyne MW (2002) NC milling program optimization with 3D geometric modeling. International Conference Graphicon.

  13. 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

    Article  Google Scholar 

  14. 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

    Article  Google Scholar 

  15. Lee H, Cho DW (2003) An intelligent feed rate scheduling based on virtual machining. Int J Adv Manuf Technol 22:873–882

    Article  Google Scholar 

  16. 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

    Google Scholar 

  17. Saturlay PV, Spence AD (2000) Integration of milling process simulation with on-line monitoring and control. Int J Adv Manuf Technol 16:92–99

    Article  Google Scholar 

  18. 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

    Article  MathSciNet  Google Scholar 

  19. 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

    Google Scholar 

  20. Shiuh-Jer H, Chiou-Yuarn S (1999) Fuzzy logic for constant force control of end milling. Ind Electron IEEE Trans 46:169–176

    Article  Google Scholar 

  21. 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

    Article  Google Scholar 

  22. 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

    Article  Google Scholar 

  23. Liang SY, Hecker RL, Landers RG (2004) Machining process monitoring and control: the state-of-the-art. J Manuf Sci Eng 126:297–310

    Article  Google Scholar 

  24. Proctor FMKTR, John LM (1997) Canonical machining commands. Intelligent Systems Division, National Institute of Standards and Technology Administration, US Department of Commerce, Gaithersburg, 20899

    Google Scholar 

  25. 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).

  26. Driankov DHH, Reinfrank M (1996) An introduction to fuzzy control. Springer, Berlin

    MATH  Google Scholar 

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Correspondence to Xun Xu.

Appendix

Appendix

Table 4 Some typical canonical machining commands

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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

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