THERMAL SCIENCE

International Scientific Journal

Thermal Science - Online First

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Monitoring and neural network modeling of cutting temperature during turning hard steel

ABSTRACT
In this study, cutting tools average temperature was investigated by using thermal imaging camera of FLIR E50-type. CBN inserts with zero and negative rake angles were taken as cutting tools and round bar of EN 90MnCrV8 hardened steel was used as the workpiece. Since the life of the cutting tool material strongly depends upon cutting temperature, it is important to predict heat generation in the tool with intelligent techniques. This paper proposes a method for the identification of cutting parameters using neural network. The model for determining the cutting temperature of hard steel, was trained and tested by using the experimental data. The test results showed that the proposed neural network model can be used successfully for machinability data selection. The effect on the cutting temperature of machining parameters and their interactions in machining were analyzed in detail and presented in this study.
KEYWORDS
PAPER SUBMITTED: 2017-06-06
PAPER REVISED: 2017-08-23
PAPER ACCEPTED: 2017-09-19
PUBLISHED ONLINE: 2017-10-07
DOI REFERENCE: https://doi.org/10.2298/TSCI170606210T
REFERENCES
  1. Sharma, V.S., S.K. Sharma, and A.K. Sharma, Cutting tool wear estimation for turning. Journal of Intelligent Manufacturing, 2007. 19(1): p. 99-108.
  2. Kovac, P., et al., Application of fuzzy logic and regression analysis for modeling surface roughness in face milliing. Journal of Intelligent Manufacturing, 2012. 24(4): p. 755-762.
  3. Pontes, F.J., et al., Artificial neural networks for machining processes surface roughness modeling. The International Journal of Advanced Manufacturing Technology, 2009. 49(9-12): p. 879-902.
  4. Kovac, P., et al., Multi-output fuzzy inference system for modeling cutting temperature and tool life in face milling. Journal of Mechanical Science and Technology, 2014. 28(10): p. 4247-4256.
  5. Andjelkovic, B., et al., Modeling steady-state thermal defectoscopy of steel solids using two side testing. Thermal Science, 2016. 20(suppl. 5): p. 1333-1343.
  6. Ay, H. and W.J. Yang, Heat transfer and life of metal cutting tools in turning. International Journal of Heat and Mass Transfer, 1998. 41(3): p. 613-623.
  7. Nedic, B. and M. Eric, Cutting temperature measurement and material machinability. Thermal Science, 2014. 18(suppl.1): p. 259-268.
  8. Jawahir, I.S., et al., Towards integration of hybrid models for optimized machining performance in intelligent manufacturing systems. Journal of Materials Processing Technology, 2003. 139(1-3): p. 488-498.
  9. El-Mounayri, H., J.F. Briceno, and M. Gadallah, A new artificial neural network approach to modeling ball-end milling. The International Journal of Advanced Manufacturing Technology, 2009. 47(5-8): p. 527-534.
  10. Kannan, T.D.B., et al., Application of Artificial Neural Network Modeling for Machining Parameters Optimization in Drilling Operation. Procedia Materials Science, 2014. 5: p. 2242-2249.
  11. Zuperl, U., et al., A hybrid analytical-neural network approach to the determination of optimal cutting conditions. Journal of Materials Processing Technology, 2004. 157-158: p. 82-90.
  12. Ambrogio, G., et al., Application of NN technique for predicting the in-depth residual stresses during hard machining of AISI 52100 steel. International Journal of Material Forming, 2008. 1(1): p. 39-45.
  13. Petkovic, D., et al., Modeling of cutting temperature in the biomedical stainless steel turning process. Thermal Science, 2016. 20(suppl. 5): p. 1345-1354.
  14. Balazinski, M., et al., Tool condition monitoring using artificial intelligence methods. Engineering Applications of Artificial Intelligence, 2002. 15: p. 73-80.
  15. Gaitonde, V.N., et al., Performance comparison of conventional and wiper ceramic inserts in hard turning through artificial neural network modeling. The International Journal of Advanced Manufacturing Technology, 2010. 52(1-4): p. 101-114.
  16. Simonovic, M., et al., Heat load prediction of small district heating system using artificial neural networks. Thermal Science, 2016. 20(suppl. 5): p. 1355-1365.
  17. Huang, Y. and T.G. Dawson, Tool crater wear depth modeling in CBN hard turning. Wear, 2005. 258(9): p. 1455-1461.
  18. Ueda, T., et al., Temperature on Flank Face of Cutting Tool in High Speed Milling. CIRP Annals - Manufacturing Technology, 2001. 50(1): p. 37-40.
  19. Ueda, T., et al., Temperature Measurement of CBN Tool in Turning of High Hardness Steel. CIRP Annals - Manufacturing Technology, 1999. 48(1): p. 63-66.
  20. Müller-Hummel, P. and M. Lahres, Infrared temperature measurement on diamond-coated tools during machining. Diamond and Related Materials, 1994. 3(4-6): p. 765-796.
  21. Rao, R.N.S., D.N. Rao, and C.H.S. Rao, Online prediction of diffusion wear on the flank through tool tip temperature in turning using artificial neural networks. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 2006. 220(12): p. 2069-2076.