Online tool wear prediction in wet machining using modified back propagation neural network

Tool wear monitoring is one of the critical issues in the automated industry. Though use of artificial neural networks for tool wear monitoring is widely reported in the literature, the models are built only for dry machining. In the present work, a neural network model for cutting fluid assisted machining is proposed. Experimentation has been carried out using different cutting fluids and the results were used to build up and test the model. Further, an improvement in the network is proposed using simulated annealing, which can automatically and effectively optimize the network architecture, as opposed to the conventional trial and error method.

[1]  Geok Soon Hong,et al.  Using neural network for tool condition monitoring based on wavelet decomposition , 1996 .

[2]  Wen Wang,et al.  Design of neural network-based estimator for tool wear modeling in hard turning , 2008, J. Intell. Manuf..

[3]  Snr Dimla E Dimla.  Application of perceptron neural networks to tool-state classification in a metal-turning operation , 1999 .

[4]  Steven Y. Liang,et al.  Machining Process Monitoring and Control: The State–of–the–Art , 2002 .

[5]  Tuğrul Özel,et al.  Prediction of flank wear by using back propagation neural network modeling when cutting hardened H-13 steel with chamfered and honed CBN tools , 2002 .

[6]  D. Nageswara Rao,et al.  Influence of emulsifier content on cutting fluid properties , 2006 .

[7]  N. K. Bose,et al.  Neural Network Fundamentals with Graphs, Algorithms and Applications , 1995 .

[8]  Ramón Quiza,et al.  Comparing statistical models and artificial neural networks on predicting the tool wear in hard machining D2 AISI steel , 2008 .

[9]  Amiya R Mohanty,et al.  Estimation of tool wear during CNC milling using neural network-based sensor fusion , 2007 .

[10]  David M. Skapura,et al.  Neural networks - algorithms, applications, and programming techniques , 1991, Computation and neural systems series.

[11]  Y. G. Srinivasa,et al.  A back-propagation algorithm applied to tool wear monitoring , 1994 .

[12]  Uday S. Dixit,et al.  Neural network modelling of forces and indirect prediction of tool wear in turning of grey cast iron with ceramic tool , 2010 .

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

[14]  Danko Brezak,et al.  Flank wear regulation using artificial neural networks , 2010 .

[15]  Charles Leave.  Neural Networks: Algorithms, Applications and Programming Techniques , 1992 .

[16]  Hang Liang,et al.  Tool Wear State Diagnosis Based on Wavelet Analysis-BP Neural Network , 2010 .

[17]  Jose Vicente Abellan-Nebot,et al.  A review of machining monitoring systems based on artificial intelligence process models , 2010 .

[18]  Laura Ignizio Burke,et al.  Automated identification of tool wear states in machining processes: an application of self-organizing neural networks , 1989 .

[19]  C. Sanjay,et al.  Modeling of tool wear in drilling by statistical analysis and artificial neural network , 2005 .

[20]  S. S. Rangwala,et al.  Machining process characterization and intelligent tool condition monitoring using acoustic emission signal analysis , 1994 .

[21]  Martin B.G. Jun,et al.  Tool wear monitoring of micro-milling operations , 2009 .