Abstract
In this paper, a mechanical imbalance prediction problem for a milling tool heads used in Computer Numerical Control (CNC) machines was studied. Four classes of the head imbalance were examined. The data set included 27334 records with 14 features in the time and frequency domains. The feature selection procedure was applied in order to extract the most significant attributes. Only 3 out of 14 attributes were selected and utilized for the representation of each signal. Seven computational intelligence methods were applied in the prediction task: K–Means clustering algorithm, probabilistic neural network, single decision tree, boosted decision trees, multilayer perceptron, radial basis function neural network and support vector machine. The accuracy, sensitivity and specificity were computed in order to asses the performance of the algorithms.
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References
Mączka, T., Żabiński, T.: Platform for Intelligent Manufacturing Systems with elements of knowledge discovery. In: Manufacturing System, pp. 183–204. InTech, Croatia (2012)
Żabiński, T., Mączka, T., Kluska, J., Kusy, M., Hajduk, Z., Prucnal, S.: Failures Prediction in the Cold Forging Process Using Machine Learning Methods. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014, Part I. LNCS(LNAI), vol. 8467, pp. 622–633. Springer, Heidelberg (2014)
IPC–BECKHOFF New Automation Technology, http://www.beckhoff.com/ipc
Żabiński, T., Mączka, T.: Implementation of Human-System Interface for Manufacturing Organizations. In: Hippe, Z.S., Kulikowski, J.L., Mroczek, T. (eds.) Human – Computer Systems Interaction. AISC, Part I, vol. 98, pp. 13–31. Springer, Heidelberg (2012)
TwinCAT 3, http://www.beckhoff.com/english/twincat/twincat-3.htm
Matlab Coder, http://www.mathworks.com/products/matlab-coder/
Simulink Coder, http://www.mathworks.com/products/simulink-coder/
Shen, C., Wang, G., Wang, S., Liu, G.: The Imbalance Source of Spindle-Tool System and Influence to Machine Vibration Characteristics. In: Second International Conference on Digital Manufacturing & Automation (2011)
Smith, G.T.: Cutting tool technology: industrial handbook. Springer-Verlag London Limited (2008)
Haas VM-3 CNC machine, http://int.haascnc.com
HAIMER Tool Dynamic (2009), www.haimer.com
ISO 1940–1:2003 Mechanical vibration - Balance quality requirements for rotors in a constant (rigid) state - Part 1: Specification and verification of balance tolerances
Wu, X., Kumar, V., Quinlan, J.R., et al.: Top 10 algorithms in data mining. Knowledge Information Systems 14, 1–37 (2008)
Sherrod, P.H.: DTREG predictive modelling software, http://www.dtreg.com
Hartigan, J.A., Wong, M.A.: A k-means clustering algorithm. Journal of the Royal Statistical Society - Series C (Applied Statistics) 1, 100–108 (1979)
Specht, D.F.: Probabilistic neural networks. Neural Networks 3, 109–118 (1990)
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and regression trees. Wadsworth, Belmont (1984)
Friedman, J.H.: Greedy Function Approximation: A Gradient Boosting Machine. IMS Reitz Lecture (1999)
Rumelhart, D., McClelland, J.: Parallel Distributed Processing. MIT Press, Cambridge (1986)
Chen, S., Wang, X., Harris, C.J.: Experiments with repeating weighted boosting search for optimization in signal processing applications. IEEE Trans. Systems Man Cybernetics Part B Cybernetics 35, 682–693 (2005)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
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Żabiński, T. et al. (2015). CNC Milling Tool Head Imbalance Prediction Using Computational Intelligence Methods. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2015. Lecture Notes in Computer Science(), vol 9119. Springer, Cham. https://doi.org/10.1007/978-3-319-19324-3_45
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DOI: https://doi.org/10.1007/978-3-319-19324-3_45
Publisher Name: Springer, Cham
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