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Study on The Self-Adaptive Forecast and Optimal Control Method for Grinding Wheel Infeed

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Abstract:

A self-adaptive forecast & optimal control method for grinding wheel in-feed is presented, it can control grinding wheel plunge by using the new program of grinding process, and can compensate availably the size errors produced by the elasticity deflection of the grinding system and the deference of work-pieces rough and the wear of the grinding wheel, et al. The result of computer simulation and real testing indicate that this method can improve grinding quality.

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Periodical:

Key Engineering Materials (Volume 329)

Pages:

87-92

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.329.87

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Online since:

January 2007

Authors:

Gui Jie Liu, Ning Mei, Ya Dong Gong, Wan Shan Wang

Keywords:

Grinding Quality, Grinding Wheel, Self-Adaptive & Optimal Control, Wheel Infeed

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© 2007 Trans Tech Publications Ltd. All Rights Reserved

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References

[1] A. D. Dornfeld. Neural network sensor fusion for tool condition monitoring [J], Annals of the CIRP Vol. 39 (1990), p.101~105.

DOI: 10.1016/s0007-8506(07)61012-9

Google Scholar

[2] Inasaki I. Monitoring Technology for Achieving Intelligent Grinding System [J]. ASME, MED, 1997, Vol. 6-1: 75~78.

Google Scholar

[3] G. J. Liu: Research on Some Key Techniques for Intelligent Grinding and Its Application [D], Dissertation Northeastern university, 2003 (In Chinese).

Google Scholar

[4] Liu Guijie, Huang Guicong, Cheng Qiang. Self-study control of cylinder displacement [J]. Machine Engineer, 1999(6): 21~22. (In Chinese).

Google Scholar

[5] Liu Guijie, Gong Yadong. Wang Wan Shan. Self-studying control of feeding displacement of grinding machine based on AC-asynchronous motor driving [J]. Journal of Northeastern University, 2001, Vol. 22(3): 268~270 (a ) Ra=0. 598�m (b) Ra=0. 702�m Fig. 4 The roughness of the typical work-pieces Fig. 5 The roundness of the typical work-pieces �a�0. 0035mm �b�0. 0052mm.

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