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. Keywords—Degradation signal, drill-bit breakage, random forest, multinomial logistic regression.

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

[2]  Cuneyt Oysu,et al.  Drill wear monitoring using cutting force signals , 2004 .

[3]  Ching-Huan Tseng,et al.  On-line breakage monitoring of small drills with input impedance of driving motor , 2007 .

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

[5]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .

[6]  Ratna Babu Chinnam,et al.  Prediction of drill-bit breakage from degradation signals using Mahalanobis-Taguchi system analysis , 2008 .

[7]  D. E. Dimla,et al.  On-line metal cutting tool condition monitoring.: II: tool-state classification using multi-layer perceptron neural networks , 2000 .

[8]  Kenneth A. Loparo,et al.  Tool wear condition monitoring in drilling operations using hidden Markov models (HMMs) , 2001 .

[9]  T. I. El-Wardany,et al.  Tool condition monitoring in drilling using vibration signature analysis , 1996 .

[10]  Ratna Babu Chinnam,et al.  Online Reliability Estimation of Physical Systems Using Neural Networks and Wavelets , 2002 .

[11]  U. Zuperl,et al.  Tool cutting force modeling in ball-end milling using multilevel perceptron , 2004 .

[12]  Glenn J. Myatt,et al.  Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel(r) with Xlminer(tm) + Making Sense of Data Set , 2008 .

[13]  Bharatendra K. Rai.  Classification, feature selection and prediction with Neural-network Taguchi System , 2009 .

[14]  Yoke San Wong,et al.  Effective training data selection in tool condition monitoring system , 2006 .

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