Artificial neural network based prediction of drill flank wear from motor current signals

In this work, a multilayer neural network with back propagation algorithm (BPNN) has been applied to predict the average flank wear of a high speed steel (HSS) drill bit for drilling on a mild steel work piece. Root mean square (RMS) value of the spindle motor current, drill diameter, spindle speed and feed-rate are inputs to the network, and drill wear is the output. Drilling experiments have been carried out over a wide range of cutting conditions and the effects of drill wear, cutting conditions (speed, drill diameter, feed-rate) on the spindle motor current have been investigated. The performance of the trained neural network has been tested for new cutting conditions, and found to be in very good agreement to the experimentally determined drill wear values. The accuracy of the prediction of drill wear using neural network is found to be better than that using regression model.

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

[2]  M. A. Mannan,et al.  Monitoring and Adaptive Control of Cutting Process by Means of Motor Power and Current Measurements , 1989 .

[3]  M. F. DeVries,et al.  Neural Network Sensor Fusion for Tool Condition Monitoring , 1990 .

[4]  Li Dan,et al.  Tool wear and failure monitoring techniques for turning—A review , 1990 .

[5]  Issam Abu-Mahfouz,et al.  Drilling wear detection and classification using vibration signals and artificial neural network , 2003 .

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

[7]  Shih-Chieh Lin,et al.  Drill wear monitoring using neural networks , 1996 .

[8]  Dong-Woo Cho,et al.  Estimating cutting force from rotating and stationary feed motor currents on a milling machine , 2002 .

[9]  Srikanta Pal,et al.  Predicting drill wear using an artificial neural network , 2006 .

[10]  Mauri Routio,et al.  Tool wear and failure in the drilling of stainless steel , 1995 .

[11]  Ranga Komanduri,et al.  On Multisensor Approach to Drill Wear Monitoring , 1993 .

[12]  Li Xiaoli,et al.  On-line detection of the breakage of small diameter drills using current signature wavelet transform , 1999 .

[13]  Ekkard Brinksmeier,et al.  Prediction of Tool Fracture in Drilling , 1990 .

[14]  T. I. Liu,et al.  Intelligent Classification and Measurement of Drill Wear , 1994 .

[15]  Erkki Jantunen,et al.  A summary of methods applied to tool condition monitoring in drilling , 2002 .

[16]  Bernhard Sick,et al.  ON-LINE AND INDIRECT TOOL WEAR MONITORING IN TURNING WITH ARTIFICIAL NEURAL NETWORKS: A REVIEW OF MORE THAN A DECADE OF RESEARCH , 2002 .

[17]  Won Tae Kwon,et al.  Drilling torque control using spindle motor current and its effect on tool wear , 2004 .

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

[19]  Xiaoli Li,et al.  Tool wear detection with fuzzy classification and wavelet fuzzy neural network , 1999 .

[20]  T. I. Liu,et al.  INTELLIGENT DETECTION OF DRILL WEAR , 1998 .

[21]  Ichiro Inasaki,et al.  Tool Condition Monitoring (TCM) — The Status of Research and Industrial Application , 1995 .

[22]  D. E. Dimla,et al.  Neural network solutions to the tool condition monitoring problem in metal cutting—A critical review of methods , 1997 .

[23]  David Dornfeld,et al.  Sensor Integration Using Neural Networks for Intelligent Tool Condition Monitoring , 1990 .

[24]  S. Tso,et al.  Drill wear monitoring based on current signals , 1999 .

[25]  Reza Langari,et al.  A Neuro-Fuzzy System for Tool Condition Monitoring in Metal Cutting , 2001 .