Wear state recognition of drills based on K-means cluster and radial basis function neural network
Author:
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computer Science Applications,Modeling and Simulation,Control and Systems Engineering
Link
http://link.springer.com/content/pdf/10.1007/s11633-010-0502-z.pdf
Reference21 articles.
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2. B. Sick. On-line and indirect tool wear monitoring in turning with artificial networks: A review of more than a decade of research. Mechanical Systems and Signal Processing, vol. 16, no. 4, pp. 487–546, 2002.
3. A. Noori-Khajavi, R. Komanduri. Frequency and time domain analyses of sensor signals in drilling, Part 1: Correlation with drill wear. International Journal of Machine Tools and Manufacture, vol. 35, no. 6, pp. 775–793, 1995.
4. A. L. Quadro, J. R. T. Branco. Analysis of the acoustic emission during drilling test. Surface and Coating Technology, vol. 94–95, no. 1–3, pp. 691–695, 1997.
5. Y. T. Oh, W. T. Kwon, C. N. Chu. Drilling torque control using spindle motor current and its effect on tool wear. International Journal of Advanced Manufacturing Technology, vol. 24, no. 5–6, pp. 327–334, 2004.
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1. Enhancing Clustering Algorithm with Initial Centroids in Tool Wear Region Recognition;International Journal of Precision Engineering and Manufacturing;2021-03-29
2. Multi-layer radial basis function neural network based on multi-scale kernel learning;Applied Soft Computing;2019-09
3. Dynamic Bayesian Network-Based Approach by Integrating Sensor Deployment for Machining Process Monitoring;IEEE Access;2018
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