Cluster Analysis for Drilling-Quality Based on the Modified Algorithm of InDBSCAN

Abstract:

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To detect the quality of batch drilling quickly,a new approach based on Acoustic Emission signals is presented. The signals’ statistical characteristics are extracted from acoustic emission signals in Time-domain, and then the signals’ eigenvectors are constructed to reflect each drilling process. A modified incremental clustering algorithm InDBSCAN is used to cluster these eigenvectors,and the batch drilling-quality can be analysed indirectly. Calculation and analysis results show that: the conclusion of incremental cluster analysis is more reasonable by the modified incremental clustering method of InDBSCAN. The detection accuracy of the batch drilling-quality is up to 84.3% according to the manual quality inspection.

Info:

Periodical:

Key Engineering Materials (Volumes 480-481)

Edited by:

Yanwen Wu

Pages:

877-882

Citation:

Y. H. Zhou et al., "Cluster Analysis for Drilling-Quality Based on the Modified Algorithm of InDBSCAN", Key Engineering Materials, Vols. 480-481, pp. 877-882, 2011

Online since:

June 2011

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Price:

$38.00

[1] SHAO Ren-ping, HUANG Xin-na, HU Jun-hui. Analysis of data mining of clustering and its application to mechanical transmission fault diagnosis [C]. Journal of Aerospace Power, 2008, 23(10): 1933-(1938).

[2] ZHOU You-hang, ZHANG Jian-xun. Analysis of Relationship between batch drilling process and Multi-Sensor Synchronization Signals [J]. 2009 International Conference on Measuring Technology and Mechatronics Automation, 2009, 2: 127-130.

DOI: https://doi.org/10.1109/icmtma.2009.583

[3] ZHOU You-hang, ZHANG Jian-xun, TANG Wen-zhuang. Mapping between phases and signals in drilling process based on transient features of signals [J]. Journal of Central South University (Science and Technology), 2010, 41(3): 971-976.

[4] Singh R, Khamba J S. Comparison of slurry effect on machining characteristics of titanium in ultrasonic drilling [J]. Journal of Materials Processing Technology, 2008, 197(2): 200-205.

DOI: https://doi.org/10.1016/j.jmatprotec.2007.06.026

[5] Jiawei Han, Micheline Kamber. Data Mining: Concepts and Techniques (Second Edition)[M]. FAN Ming, MENG Xiao-feng, translate. Beijing: China Machine Press, 2007: 251-301.

[6] Deng Dong-mei, LONG Ji-zhen, YIN Xiang-zhou. A co-clustering algorithm based on structured Web document [J]. Journal of Central South University (Science and Technology), 2010, 41(5): 1871-1876.

[7] Ester M, Kriegel H-P, Sander J, etc. Incremental Clustering for Mining in a Data Warehousing Environment[C]. In: Proceedings of 24th International conference on very large Data base, 1998: 323-333.

[8] WANG Xin-min, ZHAO Bin, ZHANG Qin-li. Mining method choice based on AHP and fuzzy mathematics [J]. Journal of Central South University (Science and Technology), 2008, 39(5): 875-880.

[9] YANG Yan, JIN Fan, et al. Survey of clustering validity evaluation [J]. Application Research of Computers, 2008, 25(6): 1630-1632.

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