Modelling of Drill Bit Temperature and Cutting Force in Drilling Process Using Artificial Neural Networks, pages: 333-340
This study applied artificial neural networks (ANN) to estimate the drill bit temperature and cutting force in drilling process using Firex® coated carbide and uncoated drills. Also, the effects of the different network structures in the modeling the drill bit temperature and cutting force were also investigated. The numbers of neuron in network structure of ANN models are 2-6-2, 2-5-2, 2-3-5-2, 2-5-4-2, 2-3-4-4-2 and 2-2-4-3-2 structures. The best ANN model, the 2-5-2 network structures in predicting the drill bit temperatures were obtained whereas; the 2-2-4-3-2 structures were found in predicting the cutting force. The empirical equations for the best ANN models in the prediction of drill bit temperature and cutting force were developed and the obtained results were confirmed. When the results of mathematical modelling are examined, the computed the drill bit temperature and cutting forces are observed to be apparently within acceptable values.
Shaw, M. C. "Metal Cutting PrinciplesOxford University Press." New York, NY, USA 457, (1984).
Jaspers, S. P. F. C., J. H. Dautzenberg, and D. A. Taminiau. "Temperature measurement in orthogonal metal cutting." The International Journal of Advanced Manufacturing Technology, 14(1): 7-12, (1998).
R. Çakıroğlu, A. Acır, “Optimization of cutting parameters on drill bit temperature in drilling by Taguchi method”, Measurement, 46(9): 3525-3531,(2013).
S. Yagmur, A. Acır, U. Seker and M.Günay, “An experimental investigation of effect of cutting parameters on cutting zone temperature in drilling”, Journal of the Faculty of Engineering and Architecture of Gazi University, 28(1): 1-6, (2013).
R. Komanduri, Z.B. Hou, “A review of the experimental techniques for the measurement of heat and temperatures generated in some manufacturing processes and tribology”, Tribology International, 34: 653–682, (2001).
Yalcin, A. D. Karaoglan, İ. Korkut, “Optimization of Cutting Parameters in Face Milling with Neural Networks and Taguchi based on Cutting Force, Surface Roughness and Temperatures”, International Journal of Production Research, 51(11): (2013).
T. E. Y. Adesta, M. H.F. Al Hazza, M.Y. Suprianto, M. Riza, “Prediction of Cutting Temperatures by Using Back Propagation Neural Network Modeling when Cutting Hardened H-13 Steel in CNC End Milling”, Advanced Materials Research, 576: 91-94, (2012).
Kara F, Aslantaş K., Çicek A., “ANN and multiple regression method based modeling of cutting forces in orthogonal machining of AISI 316L stainless steel”, Neural Computing and Applications, 26(1): 237-250, (2015).
D. Tanikić, M. Manić, G. Devedžić, Z. Stević: “Modelling, Metal cutting Parameters Using Intelligent Techniques”, Journal of Mechanical Engineering, 561: 52-62, (2010).
A. Al Masud, S. M. Ali, N. R. Dhar, “Modeling of Chip Tool Interface Temperature in Machining Steel- An Artificial Intelligence (AI) Approach”, Proceedings of the 2011 International Conference on Industrial Engineering and Operations Management, Kuala Lumpur, Malaysia, January 22 – 24, (2011).
L.B.Abhang and M. Hameedullah, “Chip-Tool Interface Temperature Prediction Model for Turning Process”, International Journal of Engineering Science and Technology, 2(4): 382-393, (2010).
İ. Korkut, A. Acır, M. Boy, “Application of regression and artificial neural network analysis in modelling of tool-chip interface temperature in machining”, Expert Systems with Applications, 38(9): 11651-11656, (2011).
Wangshen Hao, Xunsheng Zhu, Xifeng Li, Gelvis Turyagyenda, “Prediction of cutting force for self-propelled rotary tool using artificial neural networks”, Journal of Materials Processing Technology, 180: 23–29, (2006).
M. T. Özkan, “Experimental and artificial neural network study of heat formation values of drilling and boring operations on Al 7075 T6 workpiece, Indian Journal of Engineering & Materials Science, 20: 259-268. (2013).
Hagan, M. T., Demuth, H. B., Beale, M. H., & De Jesús, O., “Neural network design” Boston: PWS publishing company, 20: (1996).
Haykin S., “Neural networks, a comprehensive foundation” New Jersey: Prentice, 697 Hall, (1994).
Zain A.M., Haron, H., Sharif S., “Prediction of surface roughness in the end milling machining using Artificial Neural Network”, Expert Systems with Applications, 37: 1755–1768, (2010).
T.H. Martin, B.M. Mohammed, “Training feed forward networks with the Marquardt algorithm”, IEEE Trans. Neural Netw., 5(6): 989–994, (1994).
Battiti, R., “First-and second-order methods for learning: between steepest descent and Newton's method”, Neural computation, 4(2): 141-166, (1992).
Mohanraj, M., Jayaraj, S., & Muraleedharan, C., “Exergy analysis of direct expansion solar‐assisted heat pumps using artificial neural networks”, International Journal of Energy Research, 33(11): 1005-1020, (2009).
Lin J.T., D. Bhattacharyya, V. Kecman, “Multiple regression and neural networks analyses in composites machining”, Composites Science and Technology, 63: 539–548, (2003).
Acır A., “Application of artificial neural network to exergy performance analysis of coal fired thermal power plant”, Int. Journal of Exergy, 12(3): 362-379, (2013).
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