A Study of Classification Models to Predict Drill-Bit Breakage Using Degradation Signals
Cutting tools are widely used in manufacturing processes and drilling is the most commonly used machining process. Although drill-bits used in drilling may not be expensive, their breakage can cause damage to expensive work piece being drilled and at the same time has major impact on productivity. Predicting drill-bit breakage, therefore, is important in reducing cost and improving productivity. This study uses twenty features extracted from two degradation signals viz., thrust force and torque. The methodology used involves developing and comparing decision tree, random forest, and multinomial logistic regression models for classifying and predicting drill-bit breakage using degradation signals.
[1] B. K. Rai, R. Chinnam, and N. Singh, "Prediction of drill-bit breakage from degradation signals using Mahalanobis-Taguchi system analysis,”International Journal of Industrial and Systems Engineering, Vol. 3(2), pp.134-148, 2008.
[2] T. I. El-Wardany, D. Gao, and M. A. Elbestawi, "Tool condition monitoring in drilling using vibration signature analysis,”International Journal of Machine Tools Manufacturing, Vol. 36, pp.687-711, 1996.
[3] D. E. Dimla, and P. M. Lister, "Online metal cutting tool condition monitoring. II: tool-state classification using multi-layer perceptron neural networks,” International Journal of Machine Tools and Manufacture Design, Research and Applications, Vol. 40, pp.769-781, 2000.
[4] L. Fu,S. Ling, and C. Tseng, "On-line breakage monitoring of small drills with input impedance of driving motor,”Mechanical Systems and Signal Processing, Vol. 21, pp.457-465, 2007.
[5] N. Ghosh, Y. B. Ravi,A. Patra, S. Mukhopadhyay, S. Paul, A. R. Mohanty, and A. B. Chattopadhyay, "Estimation of tool wear during CNC milling using neural network-based sensor fusion,”Mechanical Systems and Signal Processing, Vol. 21, pp.466-479, 2007.
[6] J. Sun, G. S. Hong, Y. S. Wong, M. Rahman, and Z. G. Wang, "Effective training data selection in tool condition monitoring system,” International Journal of Machine Tools and Manufacture, Vol. 46, pp.218-224, 2006.
[7] U. Zuperl, and F. Cus, "Tool cutting force modeling in ball-end milling using multilevel perceptron,” Journal of Materials Processing Technology, Vol. 153, pp.268-275, 2004.
[8] I. Abu-Mahfouz, "Drilling wear detection and classification using vibration signals and artificial neural network,” International Journal of Machine Tools and Manufacture, Vol. 43, pp.707-720, 2003.
[9] R. B. Chinnam, and P. Mohan, "Online Reliability Estimation of Physical Systems Using Neural Networks and Wavelets,”Smart Engineering System Design, Vol. 4, pp.253-264, 2002.
[10] H. M. Ertunc, K. A. Loparo, and H. Ocak, "Tool wear condition monitoring in drilling operations using hidden Markov models,” International Journal of Machine Tools and Manufacture, Vol. 41, pp.1363-1384, 2001.
[11] H. M. Ertunc, and C. Oysu, "Drill wear monitoring using cutting force signals,” Mechatronics, Vol. 14, pp.533-548, 2004.
[12] E. Jantunen, "A summary of methods applied to tool condition monitoring in drilling,” International Journal of Machine Tools and Manufacture, Vol. 42, pp.997-1010, 2002.
[13] B. K. Rai, "Classification, Feature Selection and Prediction with Neural-Network Taguchi System,” International Journal of Industrial and Systems Engineering, Vol. 4, No. 6, 645-664, 2009.
[14] D. T. Larose, and C. D. Larose, Discovering Knowledge in Data: An Introduction to Data Mining. Hoboken, New Jersey: John Wiley & Sons, 2014, ch 8.
[15] G. Shmueli, N. R. Patel, and P. C. Bruce, Data Mining for Business Intelligence: Concepts, Techniques, and Applications. Hoboken, New Jersey: John Wiley & Sons, 2010, ch 9.
[1] B. K. Rai, R. Chinnam, and N. Singh, "Prediction of drill-bit breakage from degradation signals using Mahalanobis-Taguchi system analysis,”International Journal of Industrial and Systems Engineering, Vol. 3(2), pp.134-148, 2008.
[2] T. I. El-Wardany, D. Gao, and M. A. Elbestawi, "Tool condition monitoring in drilling using vibration signature analysis,”International Journal of Machine Tools Manufacturing, Vol. 36, pp.687-711, 1996.
[3] D. E. Dimla, and P. M. Lister, "Online metal cutting tool condition monitoring. II: tool-state classification using multi-layer perceptron neural networks,” International Journal of Machine Tools and Manufacture Design, Research and Applications, Vol. 40, pp.769-781, 2000.
[4] L. Fu,S. Ling, and C. Tseng, "On-line breakage monitoring of small drills with input impedance of driving motor,”Mechanical Systems and Signal Processing, Vol. 21, pp.457-465, 2007.
[5] N. Ghosh, Y. B. Ravi,A. Patra, S. Mukhopadhyay, S. Paul, A. R. Mohanty, and A. B. Chattopadhyay, "Estimation of tool wear during CNC milling using neural network-based sensor fusion,”Mechanical Systems and Signal Processing, Vol. 21, pp.466-479, 2007.
[6] J. Sun, G. S. Hong, Y. S. Wong, M. Rahman, and Z. G. Wang, "Effective training data selection in tool condition monitoring system,” International Journal of Machine Tools and Manufacture, Vol. 46, pp.218-224, 2006.
[7] U. Zuperl, and F. Cus, "Tool cutting force modeling in ball-end milling using multilevel perceptron,” Journal of Materials Processing Technology, Vol. 153, pp.268-275, 2004.
[8] I. Abu-Mahfouz, "Drilling wear detection and classification using vibration signals and artificial neural network,” International Journal of Machine Tools and Manufacture, Vol. 43, pp.707-720, 2003.
[9] R. B. Chinnam, and P. Mohan, "Online Reliability Estimation of Physical Systems Using Neural Networks and Wavelets,”Smart Engineering System Design, Vol. 4, pp.253-264, 2002.
[10] H. M. Ertunc, K. A. Loparo, and H. Ocak, "Tool wear condition monitoring in drilling operations using hidden Markov models,” International Journal of Machine Tools and Manufacture, Vol. 41, pp.1363-1384, 2001.
[11] H. M. Ertunc, and C. Oysu, "Drill wear monitoring using cutting force signals,” Mechatronics, Vol. 14, pp.533-548, 2004.
[12] E. Jantunen, "A summary of methods applied to tool condition monitoring in drilling,” International Journal of Machine Tools and Manufacture, Vol. 42, pp.997-1010, 2002.
[13] B. K. Rai, "Classification, Feature Selection and Prediction with Neural-Network Taguchi System,” International Journal of Industrial and Systems Engineering, Vol. 4, No. 6, 645-664, 2009.
[14] D. T. Larose, and C. D. Larose, Discovering Knowledge in Data: An Introduction to Data Mining. Hoboken, New Jersey: John Wiley & Sons, 2014, ch 8.
[15] G. Shmueli, N. R. Patel, and P. C. Bruce, Data Mining for Business Intelligence: Concepts, Techniques, and Applications. Hoboken, New Jersey: John Wiley & Sons, 2010, ch 9.
@article{"International Journal of Business, Human and Social Sciences:58067", author = "Bharatendra Rai", title = "A Study of Classification Models to Predict Drill-Bit Breakage Using Degradation Signals", abstract = "Cutting tools are widely used in manufacturing processes and drilling is the most commonly used machining process. Although drill-bits used in drilling may not be expensive, their breakage can cause damage to expensive work piece being drilled and at the same time has major impact on productivity. Predicting drill-bit breakage, therefore, is important in reducing cost and improving productivity. This study uses twenty features extracted from two degradation signals viz., thrust force and torque. The methodology used involves developing and comparing decision tree, random forest, and multinomial logistic regression models for classifying and predicting drill-bit breakage using degradation signals.
", keywords = "Degradation signal, drill-bit breakage, random forest, multinomial logistic regression.", volume = "8", number = "8", pages = "2477-4", }
