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Traditional and Non-traditional Control Techniques for Grinding Processes

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Abstract

Owing to the highly demanding geometric accuracy and surface finish for many modern products, grinding processes have been extensively used in manufacturing industry. However, it is also well-accepted that grinding is one of the most complicated machining processes due to the high nonlinearities, intrinsic uncertainties and time-varying characteristics. Multiple challenging problems exist in the process that limits its overall quality and production in practice. With the increasing demands for higher part geometry accuracy, better surface integrity, more productivity, and other desired product parameters (e.g., minimization of subsurface micro-damage) with less operator intervention, various control methods have been studied and implemented to control position, velocity, force, power, temperature and the Material Removal Rate (MRR) during the grinding process, in order to achieve the desired system performance within certain cost/time. This paper reviews different control strategies in order to provide a guideline for academic researchers and industrial practitioners to improve the final product quality with increased possible process flexibility.

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References

  1. Tönshoff, H. K., Zinngrebe, M., and Kemmerling, M., 1986, “Optimization of Internal Grinding by Microcomputer-Based Force Control”, Annals of the CIRP, Vol. 35, No. 1, pp. 293–296.

    Article  Google Scholar 

  2. 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, Vol. 28, pp. 67–77.

    Google Scholar 

  3. Kaliszer, H., Mishina, O., and Webster, J., 1979, “Adaptively Controlled Surface Roughness and Roundness during Grinding”, Proceedings of the 20th International MTDR Conference, Birmingham, pp. 471–478.

    Google Scholar 

  4. Amitay, G., Malkin, S., and Koren, Y., 1981, “Adaptive Control Optimization of Grinding”, Transactions of the ASME, Journal of Engineering for Industry, Vol. 103, pp. 103–108.

    Article  Google Scholar 

  5. Malkin, S., 1981, “Grinding Cycle Optimization”, Annals of the CIRP, Vol. 30, pp. 223–226.

    Article  Google Scholar 

  6. Furukawa, Y. and Ohishi, S., 1984, “Adaptive Control of Creep Feed Grinding to Avoid Workpiece Burn”, Proceedings of the Fifth International Conference on Production Engineering, Tokyo, Japan, pp. 64–69.

    Google Scholar 

  7. Elbestawi, M. A., Yuen, K. M., Srivastava, A. K., and Dai, H., 1991, “Adaptive Force Control for Robotic Disk Grinding”, CIRP Annals – Manufacturing Technology, Vol. 401, pp. 391–394.

    Article  Google Scholar 

  8. Bhattacharyya, S. K., Fowell, B., and Wallbank, J., 1984, “Control of Workpiece Surface Quality in Grinding”, ASME Winter Annual Meeting on Production Engineering Division, New Orleans, LA, Vol. 12, pp. 425–444.

    Google Scholar 

  9. Ulrich, B. J., Srivastava, A. K., Elbestawi, M. A., and Veldhuis, S., 1989, “Force Modelling of the Robotic Disk Grinding Process”, ASME Winter Annual Meeting, PED – Vol. 39, San Francisco, pp. 105–130

    Google Scholar 

  10. Srivastava, A. K., Rogers, D. B., and Elbestawi, M. A., 1993, “Optimal Planning of an Adaptively Controlled Robotic Disk Grinding Process”, International Journal of Machine Tools & Manufacturing, Vol. 33, No. 6, pp. 809–825.

    Article  Google Scholar 

  11. Tönshoff, H. K. and Walter, A., 1994, “Self-tuning Fuzzy Controller for Process Control in Internal Grinding”, Fuzzy Sets and Systems, Vol. 63, No. 3, pp. 359–373.

    Article  Google Scholar 

  12. Hahn, R., 1964, “Controlled-Force Grinding – A New Technique for Precision Internal Grinding”, Transactions of the ASME, Journal of Engineering for Industry, pp. 287–293.

    Google Scholar 

  13. Hahn, R., 1965, “Some Characteristics of Controlled Force Grinding”, Proceedings of the Sixth International Machine Tool Design Research Conference, pp. 597–609.

    Google Scholar 

  14. Zhao, Y. W. and Webster, J., 1990, “Fuzzy Pattern Recognition and Automatic Steady Control in Roller Grinding”, Proceedings of Second IEEE International Conference on Computer Integrated Manufacturing, Troy, NY, pp. 395–401.

    Google Scholar 

  15. Guo, L., Schöne, A., and Ding, X., 1992, “A Comprehensive Approach to Nonlinear Adaptive Control and its Application to Form Grinding Processes”, 31st IEEE Conference on Decision and Control, Tucson, AZ, Vol. 2, pp. 1267–1272.

    Google Scholar 

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

    Google Scholar 

  17. Jenkins, H. E., Kurfess, T. R., and Dorf, R. C., 1996, “Design of A Robust Controller for A Grinding System”, IEEE Transactions on Control Systems Technology, Vol. 4, No. 1, pp. 40–49.

    Article  Google Scholar 

  18. Jenkins, H. E. and Kurfess, T. R., 1999, “Adaptive Pole-Zero Cancellation in Grinding Force Control”, IEEE Transactions on Control Systems Technology, Vol. 7, No. 3, pp. 363–370.

    Article  Google Scholar 

  19. Brinksmeier, E. and Popp, C., 1991, “A Self-tuning Adaptive Control System for Grinding Processes”, Annuals of the CIRP, Vol. 40, No. 1, pp. 355–358.

    Article  Google Scholar 

  20. Wada, R. and Kodama, H., 1977, “Adaptive Control in Grinding”, Japanese Society of Precision Engineering, Vol. 11, No. 1, pp. 1–10.

    Google Scholar 

  21. König, W. and Werner, G., 1974, “Adaptive Control Optimization of High Efficiency External Grinding – Concept, Technological Basics and Application”, Annals of the CIRP, Vol. 23, No. 1, pp. 101–102.

    Google Scholar 

  22. Kelly, S., Rowe, W. B., and Moruzzi, J. L., 1989, “Adaptive Grinding Control”, Advanced Manufacturing Engineering, Vol. 1, No. 5, pp. 287–295.

    Google Scholar 

  23. Xiao, G., Malkin, S., and Danai, K., 1992, “Intelligent Control of Cylindrical Plunge Grinding”, Proceedings of the 1992 American Control Conference, Chicago, IL, pp. 391–398.

    Google Scholar 

  24. Xiao, G., Malkin, S., and Danai, K., 1993, “An Autonomous System for Cylindrical Plunge Grinding”, Proceedings of NSF Design and Manufacturing Systems Conference, pp. 399–403.

    Google Scholar 

  25. Xiao, G. and Malkin, S., 1996, “On-Line Optimization for Internal Plunge Grinding”, Annals of the CIRP, Vol. 45, pp. 287–292.

    Article  Google Scholar 

  26. Zhu, J. Y., Shumsheruddin, A. A., and Bollinger, J. G., 1982, “Control of Machine Tools Using the Fuzzy Control Technique”, Annals of the CIRP, Vol. 31, No. 1, pp. 347–352.

    Article  Google Scholar 

  27. Li, G. F., Wang, L. S., and Yang, L. B., 2002, “Multi-Parameter Optimization and Control of the Cylindrical Grinding Process”, Journal of Materials Processing Technology, Vol. 129, pp. 232–236.

    Article  Google Scholar 

  28. Dong, S., Danai, K., Malkin, S., and Deshmukh, A., 2004, “Continuous Optimal Infeed Control for Cylindrical Plunge Grinding, Part I: Methodology”, Transactions of the ASME, Journal of Manufacturing Science and Engineering, Vol. 126, pp. 327–333.

    Article  Google Scholar 

  29. Dong, S., Danai, K., Malkin, S., and Deshmukh, A., 2004, “Continuous Optimal Infeed Control for Cylindrical Plunge Grinding, Part I: Controller Design and Implementation”, Transactions of the ASME, Journal of Manufacturing Science and Engineering, Vol. 126, pp. 334–340.

    Article  Google Scholar 

  30. Srivastava, A. K., Ulrich, B. J., and Elbestawi, M. A., 1990, “Analysis of Rigid-Disk Wear During Robotic Grinding”, International Journal of Machining Tool and Manufacturing, Vol. 30, No. 4, pp. 521–534.

    Article  Google Scholar 

  31. Malkin, S. and Koren, Y., 1984, “Optimal Infeed Control for accelerated Spark-Out in Plunge Grinding”, Transactions of the ASME, Journal of Engineering for Industry, Vol. 106, pp. 70–74.

    Article  Google Scholar 

  32. Ljung, L., 1987, “System Identification: Theory for the user”, Englewood Cliffs, NJ: Prentice-Hall.

    MATH  Google Scholar 

  33. Rowe, W. B., Li, Y., Mills, B., and Allanson, D. R., 1996, “Applications of Intelligent CNC in Grinding”, Computers in Industry, Vol. 31, pp. 45–60.

    Article  Google Scholar 

  34. Rowe, W. B., Chen, Y., Moruzzi, J. L., and Mills, B., 1997, “A Generic Intelligent Control System for Grinding”, Computers Integrated Manufacturing Systems, Vol. 10, No. 3, pp. 231–241.

    Article  Google Scholar 

  35. Nakajima, T., Tsukamoto, S., Murakami, D., and Yasuda, H., 1993, “Neuro & Fuzzy In-Process Control Grinding Techniques – Study on Intelligent Automation of Grinding Process”, Journal of the Japan Society for Precision Engineering, Vol. 59, No. 8, pp. 1313–1318.

    Google Scholar 

  36. 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, Vol. 28, pp. 67–77.

    Google Scholar 

  37. Lee, C. W., Choi, T., and Shin, Y. C., 2003, “Intelligent Model-based Optimization of the Surface Grinding Process for Heat-Treated 4140 Steel Alloys with Aluminum Oxide Grinding Wheels”, Transactions of the ASME, Journal of Manufacturing Science and Engineering, Vol. 125, pp. 65–76.

    Article  Google Scholar 

  38. Xu, C. and Shin, Y. C., 2005, “Design of a Multi-level Fuzzy Controller for Nonlinear Systems and Stability Analysis”, IEEE Transactions on Fuzzy Systems, Vol. 13, No. 6, pp. 761–778.

    Article  Google Scholar 

  39. Xu, C. and Shin, Y. C., 2007, “Control of Cutting Force for Creep-feed Grinding Processes using a Multi-level Fuzzy Controller”, ASME Transaction, Journal of Dynamic Systems, Measurement and Control. Vol. 129, No. 4, pp. 480–492.

    Article  Google Scholar 

  40. Mise, R., Itoga, K., and Kato, C., 1994, “Applying Fuzzy Logic for Hybrid Control of Grinding Work”, IEEE Proceedings of the Tenth Anniversary Advanced Technologies in Instrumentation & Measurement Technology, Hamamatsu, Japan, Vol. 2, pp. 615–618.

    Google Scholar 

  41. Yuan, L., Järvenpää, V. M., and Keskinen, E., 2004, “Design of Fuzzy Logic-Based Controller in Roll Grinding System with Double Regenerative Chatter”, Proceedings of IMECE, ASME International Mechanical Engineering Congress and Exposition, Anaheim, CA, pp. 463–469.

    Google Scholar 

  42. Xiao, B. X., Xia, M., and Zhao C. M., 1996, “The Main Control Mode and Fuzzy Control Strategy of CNC System for Gear Hobbing and Grinding Machine”, Proceedings of the IEEE International Conference on Industrial Technology, Shanghai, China, pp. 643–646.

    Google Scholar 

  43. Orchard, M., Flores, A., Muñoz, C., and Cipriano, A., 2001, “Predictive Control with Fuzzy Characterization of Percentage of Solids, Particle Size and Power Demand for Minerals Grinding”, Proceedings of the 2001 IEEE International Conference on Control Applications, Mexico City, pp. 600–605.

    Google Scholar 

  44. Orchard, M., Flores, A., Muñoz, C., and Cipriano, A., 2001b, “Model-based Predictive Control with Fuzzy Characterization of Goals and Constraints, Applied to the Dynamic Optimization of Grinding Plants”, The Tenth IEEE International Conference on Fuzzy Systems, Melbourne, Australia, Vol. 2, pp. 916–919.

    Google Scholar 

  45. Tang, Y.-G. and Song, G., 2002, “The Mill Load Control for Grinding Plant Based on Fuzzy Logic”, Proceedings of the First International Conference on Machine Learning and Cybernetics, Beijing, China, pp. 416–419.

    Google Scholar 

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Correspondence to Chengying Xu .

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Liu, J., Xu, C., Jackson, M. (2011). Traditional and Non-traditional Control Techniques for Grinding Processes. In: Jackson, M., Davim, J. (eds) Machining with Abrasives. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-7302-3_6

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  • DOI: https://doi.org/10.1007/978-1-4419-7302-3_6

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