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Intelligent cutting tool condition monitoring in milling

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Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Purpose: of this paper is to present a tool condition monitoring (TCM) system that can detect tool breakage in real time by using a combination of neural decision system, ANFIS tool wear estimator and machining error compensation module. Design/methodology/approach: The principal presumption was that the force signals contain the most useful information for determining the tool condition. Therefore, ANFIS method is used to extract the features of tool states from cutting force signals. The trained ANFIS model of tool wear is then merged with a neural network for identifying tool wear condition (fresh, worn). Findings: The overall machining error is predicted with very high accuracy by using the deflection module and a large percentage of it is eliminated through the proposed error compensation process. Research limitations/implications: This study also briefly presents a compensation method in milling in order to take into account tool deflection during cutting condition optimization or tool-path generation. The results indicate that surface errors due to tool deflections can be reduced by 65-78%. Practical implications: The fundamental limitation of research was to develop a single-sensor monitoring system, reliable as commercially available system, but much cheaper than multi-sensor approach. Originality/value: A neural network is used in TCM as a decision making system to discriminate different malfunction states from measured signals.
Rocznik
Strony
477--486
Opis fizyczny
Bibliogr. 19 poz., rys., tab.
Twórcy
autor
  • Faculty of Mechanical Engineering, University of Maribor, Smetanova 17, 2000 Maribor, Slovenia
autor
  • Faculty of Mechanical Engineering, University of Maribor, Smetanova 17, 2000 Maribor, Slovenia
autor
  • Faculty of Mechanical Engineering, University of Maribor, Smetanova 17, 2000 Maribor, Slovenia
Bibliografia
  • [1] P. Fu, A.D. Hope, Intelligent Classification of Cutting Tool Wear States, Advances in Neural Networks 39 (2008) 16113349.
  • [2] T. Mulc, T. Udiljak, F. Cus, M. Milfelner, Monitoring cutting-tool wear using signals from the control system, Strojniski vestnik - Journal of Mechanical Engineering 12 (2004) 568-579.
  • [3] R.J. Kuo, Multi-sensor integration for on-line tool wear estimation through artificial neural networks and fuzzy neural network, Engineering Applications of Artificial Intelligence 3 (2003) 49-261.
  • [4] S. Achiche, M. Balazinski, L. Baron, K. Jemielniak, Tool wear monitoring using genetically-generated fuzzy knowledge bases, Engineering Applications of Artificial Intelligence 15 (2008) 303-314.
  • [5] J. Kopac, Cutting forces and their influence on the economics of machining, Strojniski vestnik - Journal of Mechanical Engineering 3 (2002) 72-79.
  • [6] A. Iqbal, N. He, N. Dar, L. Li, Comparison of fuzzy expert system based strategies of offline and online estimation of flank wear in hard milling process, Expert Systems with Applications 33 (2009) 61-66.
  • [7] R.E. Haber, A. Alique, Intelligent process supervision for predicting tool wear in machining processes, Mechatronics 13 (2005) 825-849.
  • [8] W.-T. Chien, Ch.-S. Tsai, The investigation on the prediction of tool wear and the determination of optimum cutting conditions in machining 17-4PH stainless steel, Journal of Materials Processing Technology 140/1-3 (2005) 340-345.
  • [9] F. Čuš, U. Župerl, Adaptive self-learning controller design for feedrate maximization of machining process, Advances in Production Engineering & Management 2 (2007) 18-27.
  • [10] M. Smaoui, Z. Bouaziz, A. Zghal, Simulation of cutting forces for complex surfaces in ball-end milling, International Journal of Simulation Modelling 7/2 (2008) 93-105.
  • [11] U. Župerl, F. Čuš, Tool cutting force modelling in ball-end milling using multilevel perceptron, Journal of Materials Processing Technology 153/154 (2004) 268-275.
  • [12] M. Norgaard, O. Ravn, N.K. Poulsen, L.K. Hansen, Neural networks for modelling and control of dynamic systems, Springer, London, 2000.
  • [13] F. Dweiri, M. Al-Jarrah, H. Al-Wedyan, Fuzzy surface roughness modeling of CNC down milling of Alumic-79, Journal of Materials Processing Technology 133 (2001) 266-275.
  • [14] M.A Shoorehdeli, M. Teshnehlab, A.K. Sedig, A. Khanesar, Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods, Applied Soft Computing 2 (2001) 833-850.
  • [15] R. Sirkant, S. Subrahmanyam, K. Chen, V.P. Krishna, Experimental selection of special geometry cutting tool for minimal tool wear, Advances in Production Engineering & Management 5/1 (2010) 13-24.
  • [16] M. Milfelner, U. Zuperl, F. Cus, Optimisation of cutting parameters in high speed milling process by GA, International Journal of Simulation Modelling 3/4 (2004) 121-131.
  • [17] J. Wang, C.Z. Huang, W.G. Song, The effects of tool flank wear on the orthogonal cutting process and its practical implications, Journal of Materials Processing Technology 142 (2003) 338-346.
  • [18] L.A. Dobrzański, K. Golombek, J. Kopac, M. Sokovic, Effect of depositing the hard surface coatings on properties of the selected cemented carbides and tool cermets, Journal of Materials Processing Technology 157-158 (2004) 304-311.
  • [19] J. Kopac, Influence of high speed cutting on the structure of machined high speed steel material, Proceedings of the 11th International Scientific Conference “Contemporary Achievements in Mechanics, Manufacturing and Materials Science” CAM3S'2005, Gliwice - Zakopane, 2005, 40-44.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-50ca120a-b05b-46ae-b334-382dfd65795d
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