Abstract
This paper proposes a method for cutting parameters identification using the multi-inputs-multi-outputs fuzzy inference system (MIMO-FIS). The fuzzy inference system (FIS) was used to identify the initial values for cutting parameters (cutting speed, feed rate and depth of cut) and flank wear using cutting temperature and tool life as outputs. The objective was to determine the influence of cutting parameters on cutting temperature and tool life. The model for determining the cutting temperature and tool life of steel AISI 1060 was trained (design rules) and tested by using the experimental data. The average deviation of the testing data for tool life was 11.6 %, while that of the cutting temperature was 3.28 %. The parameters used in these testing data were different from the data collected for the design rules. The test results showed that the proposed MIMO-FIS model can be used successfully for machinability data selection. The effect of parameters and their interactions in machining is analyzed in detail and presented in this study.
Similar content being viewed by others
References
R. V. Rao, Advanced modelling and optimization of manufacturing processes, Springer-Verlag, London (2011).
S. Markos, Z. J. Viharos and L. Monostori, Quality-oriented, comprehensive modelling of machining processes, Sixth ISMQC IMEKO symposium on metrology for quality control in production (1998) 67–74.
T. Rajmohana, K. Palanikumar and S. Prakashc, Grey-fuzzy algorithm to optimise machining parameters in drilling of hybrid metal matrix composites, Composites Part B: Engineering, 50 (2013) 297–308.
E. Usui, T. Shirakashi and T. Kitagawa, Analytical prediction of three dimensional cutting process, Part 3: Cutting temperature and crater wear of carbide tool, Journal of Manufacturing Science and Engineering, 100 (1978) 236–243.
F. Jafarian, M. Taghipour and H. Amirabadi, Application of artificial neural network and optimization algorithms for optimizing surface roughness, tool life and cutting forces in turning operation, Journal of Mechanical Science and Technology, 27 (2013) 1469–1477.
S. Khamel, N. Ouelaa and K. Bouacha, Analysis and prediction of tool wear, surface roughness and cutting forces in hard turning with CBN tool, Journal of Mechanical Science and Technology, 26 (2012) 3605–3616.
E. Usui, T. Shirakashi and T. Kitagawa, Analytical prediction of cutting tool wear, Wear, 100 (1984) 129–151.
T. Matsumura, T. Obikawa, T. Shirakashi and E. Usui, Autonomous turning operation planning with adaptive prediction of tool wear and surface roughness, Journal of Manufacturing Systems, 12 (1993) 253–262.
M. Alauddinel, M. A. Baradie and M. S. J. Hashmi, Prediction of tool life in end milling by response surface methodology, Journal of Materials Processing Technology, 71 (1997) 456–465.
P. C. Wanigarathne, A. D. Kardekar, O. W. Dillon, G. Poulachon and I. S. Jawahir, Progressive tool-wear in machining with coated grooved tools and its correlation with cutting temperature, Wear, 259 (2005) 1215–1224.
M. A. Lajis, K. A. N. Mustafizul, A. K. M. Nurul and L. G. T. Hafiz, Prediction of tool life in end milling of hardened steel AISI D2, European Journal of Scientific Research, 21 (2008) 592–602.
M. C. Shaw, Metal cutting principles, Clarendon Press, Oxford (1984).
Minis, R. Yanushevsky, A new theoretical approach for the prediction of machine tool chatter, ASME Journal of Engineering for Industry, 115 (1993) 1–8.
M. R. H. Adnan, A. Sarkheyli, A. M. Zain and H. Haron, Fuzzy logic for modeling machining process: a review, Artificial Intelligence Review (2013) doi: 10.1007/s10462-012-9381-8.
Z. Jakovljevic, B. P. Petrovic, V. D. Mikovic and M. Pajic, Fuzzy inference mechanism for recognition of contact states in intelligent robotic assembly, Journal of Intelligent Manufacturing (2012) doi: 10.1007/s10845-012-0706-x.
YX Yao, XL Li and ZJ Yuan, Tool wear detection with fuzzy classification and wavelet fuzzy neural network, Int. J. Mach. Tool Manuf., 39 (1999) 1525–1538.
V. Susanto and J. C. Chen, Fuzzy logic based in- process tool-wear monitoring system in face milling operations, The International Journal of Advanced Manufacturing Technology, 21 (2003) 186–192.
M. Balazinski and K. Jemielniak, Tool condition monitoring using fuzzy decision support system, In: au]V CIRP, International conference on monitoring and automatic supervision in manufacturing (1998) 115–121.
R. Quiza, L. Figueira and J. P. Davim, Comparing statistical models and artificial neural networks on predicting the tool wear in hard machining D2 AISI steel, International Journal of Advanced Manufacturing Technology, 37 (2008) 641–648.
Simranpreet Singh Gill, Rupinder Singh, Jagdev Singh and Harpreet Singh, Adaptive neuro-fuzzy inference system modeling of cryogenically treated AISI M2 HSS turning tool for estimation of flank wear, Expert Systems with Applications, 39 (2012) 4171–4180.
D. Tanikic, M. Manic, G. Devedzic and Z. Stevic, Modelling Metal Cutting Parameters Using Intelligent Techniques, Journal of Mechanical Engineering, 56 (2010) 52–62.
M. Aydın, C. Karakuzu, M. Ucar, A. Cengiz and M. A. Cavuslu, Prediction of surface roughness and cutting zone temperature in dry turning processes of AISI304 stainless steel using ANFIS with PSO learning, International Journal of Advanced Manufacturing Technology, 64 (2012) 1045–1060.
A. Majumder. Process parameter optimization during EDM of AISI 316 LN stainless steel by using fuzzy based multiobjective PSO, Journal of Mechanical Science and Technology, 27 (2013) 2143–2151.
Chi-Yao Hsu, Sheng-Fuu Lin and Jyun-Wei, Chang Data mining-based hierarchical cooperative coevolutionary algorithm for TSK-type neuro-fuzzy networks design, Neural Computing and Applications (2012) doi: 10.1007/s00521-012-0943-0.
D. Tikk, L. T. Koczy and T. D. Gedeon, A survey on universal approximation and its limits in soft computing techniques, International Journal of Approximate Reasoning, 33 (2003) 185–202.
G. J. Klir and T. A. Folger, Fuzzy sets, uncertainty and information, Prentice-Hall of India Private Limited, New Delhi (1988).
B. N Colding, A tool-temperature/tool-life relationship covering a wide range of cutting data, CIRP Annals - Manufacturing Technology, 40 (1991) 35–40.
X. L. Liu, D. H. Wen, Z. J. Li, L. Xiao and F. G. Yan, Cutting temperature and tool wear of hard turning hardened bearing steel, Journal of Materials Processing Technology, 129 (2002) 200–206.
A. D. Makarow, Optimization of cutting processes, Mashinostroenie, Moscow (1976).
Gholamreza Khalaj, Hossein Yoozbashizadeh, Alireza Khodabandeh and Ali Nazari, Modeling hardness of Nbmicroalloyed steels using fuzzy logic, Neural Computing and Applications (2012) doi:10.1007/s00521-011-0802-4.
Author information
Authors and Affiliations
Corresponding author
Additional information
Recommended by Associate Editor In-Ha Sung
Pavel Kovac received his B.S, M.S. and Ph.D. degrees from the University of Novi Sad, Serbia, in 1975, 1980 and 1987, respectively. He is currently a full professor at the Faculty of Technical Science at University of Novi Sad, Serbia. His research interests include machining technology, metal cutting and high productive technologies, ecological systems and technologies, plastics and environment, design of experiment and artificial intelligence.
Rights and permissions
About this article
Cite this article
Kovac, P., Rodic, D., Pucovsky, V. et al. Multi-output fuzzy inference system for modeling cutting temperature and tool life in face milling. J MECH SCI TECHNOL 28, 4247–4256 (2014). https://doi.org/10.1007/s12206-014-0938-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12206-014-0938-0