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Application of response surface methodology and fuzzy logic based system for determining metal cutting temperature

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The heat produced in metal cutting process has negative influence on the cutting tool and the machined part in many aspects. This paper deals with measurement of cutting temperature during single-point dry machining of the AISI 4140 steel, using an infrared camera. Various combinations of cutting parameters, i.e. cutting speed, feed rate and depth of cut lead to different values of the measured cutting temperature. Analysis of the measured data should explain the trends in temperature changes depending on changes in the cutting regimes. Furthermore, the temperature data is modelled using response surface methodology and fuzzy logic. The models obtained should determine the influence of cutting regimes on cutting temperature. The main objective is the reduction of cutting temperature, i.e. enabling metal cutting process in optimum conditions.
Rocznik
Strony
435--445
Opis fizyczny
Bibliogr. 34 poz., il., rys., tab., fot., wykr.
Twórcy
autor
  • Technical Faculty in Bor, University of Belgrade, V. J. 12, 19210 Bor, Serbia
  • Faculty of Mechanical Engineering, University of Niš, A. Medvedeva 14, 18000 Niš, Serbia
autor
  • Faculty of Mechanical Engineering, University of Niš, A. Medvedeva 14, 18000 Niš, Serbia
  • Faculty of Engineering, University of Kragujevac, Sestre Janić 6, 34000 Kragujevac, Serbia
  • Faculty of Mechanical Engineering, University of Niš, A. Medvedeva 14, 18000 Niš, Serbia
Bibliografia
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  • [3] L.R. Silva, A.M. Abrão, P. Faria and J.P. Davim, “Machinability study of steels in precision orthogonal cutting”, Materials Research 15 (4), 589-595 (2012).
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  • [8] G. Sutter, L. Faure, A. Molinari, N. Ranc and V. Pina, “An experimental technique for the measurement of temperature fields for the orthogonal cutting in high speed machining”, International Journal of Machine Tools & Manufacture 43 (7), 671-678 (2003).
  • [9] L. Liang, X. Hao and K. Zhiyong, “An improved three-dimensional inverse heat conduction procedure to determine the toolchip interface temperature in dry turning”, International Journal of Thermal Sciences 64, 152-161 (2013).
  • [10] J. Dorr, T. Mertens, G. Engering and M. Lahres, “In-situ temperature measurement to determine the machining potential of different tool coatings”, Surf. Coat. Technol. 174-175, 389-392 (2003).
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  • [12] N.A. Abukhshim, P.T. Mativenga and M.A. Sheikh, “Heat generation and temperature prediction in metal cutting: A review and implications for high speed machining”, International Journal of Machine Tools & Manufacture 46 (7-8), 782-800 (2006).
  • [13] C. Courbon, T. Mabrouki, J. Rech, D. Mazuyer and E. D’Eramod, “On the existence of a thermal contact resistance at the tool-chip interface in dry cutting of AISI 1045: Formation mechanisms and influence on the cutting process”, Appl. Therm. Eng. 50 (1), 1311-1325 (2013).
  • [14] D. Ulutan, I. Lazoglu and C. Dinc, “Three-dimensional temperature predictions in machining processes using finite difference method”, J. Mater. Process. Technol. 209, 1111-1121 (2009).
  • [15] P. Kwon, T. Schiemann and R. Kountanya, “An inverse estimation scheme to measure steady-state tool-chip interface temperatures using an infrared camera”, International Journal of Machine Tools & Manufacture 41 (7), 1015-1030 (2001).
  • [16] H. Saglam, S. Yaldiz and F. Unsacar, “The effect of tool geometry and cutting speed on main cutting force and tool tip temperature”, Mater. Des. 28 (1), 101-111 (2007).
  • [17] D. Tanikić and V. Marinković, “Modelling and optimization of the surface roughness in the dry turning of the cold rolled alloyed steel using regression analysis”, Journal of the Brazilian Society of Mechanical Sciences and Engineering 34 (1), 41-48 (2012).
  • [18] D. Tanikić, M. Manić, G. Devedžić and Ž. Ćojbašić, “Modelling of the temperature in the chip-forming zone using artificial intelligence techniques”, Neural Network World 20 (2), 171-187 (2010).
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  • [22] T. Erzurumlu and H. Oktem, “Comparison of response surface model with neural network in determining the surface quality of moulded parts”, Mater. Des. 28 (2), 459-465 (2007).
  • [23] D.C. Montgomery, Design and Analysis of Experiments, John Wiley & Sons, New York, 2001.
  • [24] J.S.R. Jang, C.T. Sun and E. Mizutani, Neuro-Fuzzy and soft computing: a computational approach to learning and machine intelligence, Prentice Hall, Upper Saddle River, 1997.
  • [25] J.S.R. Jang, “ANFIS: Adaptive-Network-Based Fuzzy Inference System”, IEEE Transactions on Systems, Man and Cybernetics 23 (3), 665-685 (1993).
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  • [28] P. Kovač, D. Rodić, V. Pucovsky, B. Savković and M. Gostimirović, “Multi-output fuzzy inference system for modeling cutting temperature and tool life in face milling”, Journal of Mechanical Science and Technology 28 (10), 4247-4256 (2014).
  • [29] S.Z. Razali, S.V. Wong and N. Ismail, “Fuzzy Logic Modeling For Peripheral End Milling Process”, IOP Conf. Series: Materials Science and Engineering 17 (1), 012050 (2011).
  • [30] E. Kuram and B. Ozcelik, “Fuzzy logic and regression modelling of cutting parameters in drilling using vegetable based cutting fluids”, Indian J. Eng. Mater. Sci. 20 (1), 51-58 (2013).
  • [31] O. Yumak and H.M. Ertunc, “Tool Wear Condition Monitoring in Drilling Processes Using Fuzzy Logic”, Lecture Notes in Computer Science 4234, 508-517 (2006).
  • [31] R. Masakasin and C. Raksiri, “Tool Wear Condition Monitoring in Tapping Process by Fuzzy Logic”, Proceedings of 2013 International Conference on Technology Innovation and Industrial Management, 85-93 (2013).
  • [33] R. Gessing, “Whether the opinion about superiority of fuzzy controllers is justified”, Bull. Pol. Ac.: Tech. 58 (1), 59-65 (2010).
  • [34] A. Niewiadomski and M. Kacprowicz, “Higher order fuzzy logic in controlling selective catalytic reduction systems”, Bull. Pol. Ac.: Tech. 62 (4), 743-750 (2014).
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b521f5ea-5703-48e1-af47-d8847e20212a
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