Abstract
In this work we propose to monitor the cutting tool-wear condition in a CNC-machining center by using continuous Hidden Markov Models (HMM). A database was built with the vibration signals obtained during the machining process. The workpiece used in the milling process was aluminum 6061. Cutting tests were performed on a Huron milling machine equipped with a Sinumerik 840D open CNC. We trained/tested the HMM under 18 different operating conditions. We identified three key transitions in the signals. First, the cutting tool touches the workpiece. Second, a stable waveform is observed when the tool is in contact with the workpiece. Third, the tool finishes the milling process. Considering these transitions, we use a five-state HMM for modeling the process. The HMMs are created by preprocessing the waveforms, followed by training step using Baum-Welch algorithm. In the recognition process, the signal waveform is also preprocessed, then the trained HMM are used for decoding. Early experimental results validate our proposal in exploiting speech recognition frameworks in monitoring machining centers. The classifier was capable of detecting the cutting tool condition within large variations of spindle speed and feed rate, and accuracy of 84.19%.
Chapter PDF
Similar content being viewed by others
Keywords
References
Abouelatta, O.B., Madl, J.: Surface roughness prediction based on cutting parameters and tool vibrations in turning operations. Materials Processing Technology (118), 269–277 (2001)
Atlas, L., Ostendorf, M., Bernard, G.D.: Hidden markov models for monitoring machining tool-wear. IEEE, 3887–3890 (2000)
Bilmes, J.: What hmms can do. Technical report
Davis, S.B., Mermelstein, P.: Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Transactions on Acoustic, Speech, and Signal Processing 4(28), 357–366 (1980)
Haber, R.E., Alique, A.: Intelligent process supervision for predicting tool wear in machining processes. Mechatronics (13), 825–849 (2003)
Haber, R.E., Jiménez, J.E., Peres, C.R., Alique, J.R.: An investigation of tool-wear monitoring in a high-spped machining process. Sensors and Actuators A (116), 539–545 (2004)
Koren, Y., Heisel, U., Jovane, F., Moriwaki, T., Pritschow, G., Ulsoy, G., Van Brussel, H.: Reconfigurable manufacturing systems. Annals of the CIRP 48(2), 527–540 (1999)
Lee, K.Y., Kang, M.C., Jeong, Y.H., Lee, D.W., Kim, J.S.: Simulation of surface roughness and profile in high-speed and milling. Materials Processing Technology (113), 410–415 (2001)
Liang, S.Y., Hecker, R.L., Landers, R.G.: Machining process monitoring and control: The state-of-the-art. ASME International Mechanical Engenieering Congress and Exposition, 1–12 (2002)
Owsley, L.M.D., Atlas, L.E., Bernard, G.D.: Self-organizing feature maps and hidden markov models for machine-tool monitoring. IEEE Transactions on Signal Processing 45(11), 2787–2798 (1997)
Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257–286 (1989)
Rabiner, L.R., Juang, B.H.: Fundamentals of speech recognition. Prentice-Hall, New-Jersey (1993)
Saglam, H., Unuvar, A.: Tool condition monitoring in milling based on cutting forces by a neural network. International Journal of Production Research 41(7), 1519–1532 (2003)
Tsai, Y.H., Chen, J.C., Lou, S.J.: An in-process surface recognition system based on neural networks in end milling cutting operations. Machine Tools and Manufacture (39), 583–605 (1999)
Zhang, G.M., Lin, C.: A hidden markov model approach to the study of random tool motion during machining. Technical report
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vallejo, A.G., Nolazco-Flores, J.A., Morales-Menéndez, R., Sucar, L.E., Rodríguez, C.A. (2005). Tool-Wear Monitoring Based on Continuous Hidden Markov Models. In: Sanfeliu, A., Cortés, M.L. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2005. Lecture Notes in Computer Science, vol 3773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11578079_91
Download citation
DOI: https://doi.org/10.1007/11578079_91
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29850-2
Online ISBN: 978-3-540-32242-9
eBook Packages: Computer ScienceComputer Science (R0)