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.

[1]  L. Abhang,et al.  Chip-Tool Interface Temperature Prediction Model for Turning Process , 2010 .

[2]  Habibollah Haron,et al.  Prediction of surface roughness in the end milling machining using Artificial Neural Network , 2010, Expert Syst. Appl..

[3]  R. Komanduri,et al.  A review of the experimental techniques for the measurement of heat and temperatures generated in some manufacturing processes and tribology , 2001 .

[4]  Adem Acır,et al.  Optimization of cutting parameters on drill bit temperature in drilling by Taguchi method , 2013 .

[5]  M. Mohanraj,et al.  Exergy analysis of direct expansion solar‐assisted heat pumps using artificial neural networks , 2009 .

[6]  Erry Yulian Triblas Adesta,et al.  Prediction of Cutting Temperatures by Using Back Propagation Neural Network Modeling when Cutting Hardened H-13 Steel in CNC End Milling , 2012 .

[7]  Dejan Tanikić,et al.  Modelling Metal cutting Parameters Using Intelligent Techniques , 2010 .

[8]  J. H. Dautzenberg,et al.  Temperature measurement in orthogonal metal cutting , 1998 .

[9]  Xifeng Li,et al.  Prediction of cutting force for self-propelled rotary tool using artificial neural networks , 2006 .

[10]  Syed Mithun Ali Modeling of Chip Tool Interface Temperature in Machining Steel- An Artificial Intelligence (AI) Approach , 2011 .

[11]  M. C. Shaw Metal Cutting Principles , 1960 .

[12]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[13]  Ihsan Korkut,et al.  Application of regression and artificial neural network analysis in modelling of tool-chip interface temperature in machining , 2011, Expert Syst. Appl..

[14]  Adem Acır Application of artificial neural network to exergy performance analysis of coal fired thermal power plant , 2013 .

[15]  Adem Çiçek,et al.  ANN and multiple regression method-based modelling of cutting forces in orthogonal machining of AISI 316L stainless steel , 2014, Neural Computing and Applications.

[16]  Aslan Deniz Karaoglan,et al.  Optimization of Cutting Parameters in Face Milling with Neural Networks and Taguchi based on Cutting Force, Surface Roughness and Temperatures , 2013 .

[17]  D. Bhattacharyya,et al.  Multiple regression and neural networks analyses in composites machining , 2003 .

[18]  Roberto Battiti,et al.  First- and Second-Order Methods for Learning: Between Steepest Descent and Newton's Method , 1992, Neural Computation.

[19]  Murat Tolga Ozkan Experimental and artificial neural network study of heat formation values of drilling and boring operations on Al 7075 T6 workpiece , 2013 .

[20]  T.,et al.  Training Feedforward Networks with the Marquardt Algorithm , 2004 .