An Artificial Immune Algorithm Based Intelligent Monitoring System in Grinding Process



Grinding is widely used as a precision process for machining difficult-to-cut materials. Grinding productivity is still greatly dependent on the experience and skill of human operators. Focusing on the indirect method, an attempt was made to build up an intelligent system to monitor the condition of grinding wheels with force signals and the acoustic emission (AE) signals. An artificial immune algorithm based multi-signals processing method was presented in this paper. The intelligent system is capable of incremental supervised learning of grinding conditions and quickly pattern recognition, and can continually improve the monitoring precision. The experiment indicates that the accuracy of condition identification is about 87%, and able to meet the industrial need on the whole.




Zhengyi Jiang, Shanqing Li, Jianmin Zeng, Xiaoping Liao and Daoguo Yang






Z. J. Lv et al., "An Artificial Immune Algorithm Based Intelligent Monitoring System in Grinding Process", Advanced Materials Research, Vols. 189-193, pp. 2759-2763, 2011


February 2011




[1] J.F.G. Oliveira, E.J. Silva, C. Guo, F. Hashimoto, Industrial challenges in grinding, CIRP Annals - Manufacturing Technology, Vol 58, Issue 2, 2009, pp.663-680.

DOI: 10.1016/j.cirp.2009.09.006

[2] T. Warren Liao, Feature extraction and selection from acoustic emission signals with an application in grinding wheel condition monitoring, Engineering Applications of Artificial Intelligence, Vol23, Issue 1, February 2010, pp.74-84.

DOI: 10.1016/j.engappai.2009.09.004

[3] Monostori L, A step towards intelligent manufacturing: Modelling and monitoring of manufacturing processes through artificial neural networks, CIRP Annals, 1993, Vol42, No(1): pp.485-488.

DOI: 10.1016/s0007-8506(07)62491-3

[4] Shinno H. and Hashizume H., In-Process Monitoring Method for Machining Environment Based on Simultaneous Multiphenomena Sensing, CIRP Annals, 1997, Vol46, No(1): pp.53-56.

DOI: 10.1016/s0007-8506(07)60774-4

[5] S. Forrest, A.S. Perelson, L. Allen and R. Cherukuri: Self-nonself discrimination in a computer, in Proceedings of the 1994 IEEE Symposium on Research in Security and Privacy, (Los Alamitos, CA), IEEE, IEEE Computer Society Press, (1994).

DOI: 10.1109/risp.1994.296580

[6] Leandro Nunes de Castro, Fundamentals of natural computing: an overview, Physics of Life Reviews, 2007, Vol4, No(1): pp.1-36.

[7] T. Warren Liao, Chi-Fen Ting, J. Qu, et al, A wavelet-based methodology for grinding wheel condition monitoring, International Journal of machine tools & manufacture, 47(1), 2007: pp.580-592.

DOI: 10.1016/j.ijmachtools.2006.05.008

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