Tool Wear Classification Using Decision Treesin Stone Drilling Applications: A Preliminary Study
Process parameters of stone drilling with a small diameter twist drill were used to predict tool wear by means of a machine learning decision tree algorithm. The model links tool wear with features extracted from the force sensor and the main and feed drive current sensors signals recorded under dif...
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| Veröffentlicht in: | Procedia engineering Jg. 69; S. 1326 - 1335 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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2014
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| Abstract | Process parameters of stone drilling with a small diameter twist drill were used to predict tool wear by means of a machine learning decision tree algorithm. The model links tool wear with features extracted from the force sensor and the main and feed drive current sensors signals recorded under different cutting conditions and different tool wear states. Signal features extracted from both the time and frequency domain were used as input parameters for construction of a decision tree which classifies the tool state into sharp or worn. The model was refined by selecting only the feature sources most important for classification. The best model achieves 90% accuracy in classification and relies only on features of the current signals, which simplifies its implementation in a CNC system for industrial applications. |
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| AbstractList | Process parameters of stone drilling with a small diameter twist drill were used to predict tool wear by means of a machine learning decision tree algorithm. The model links tool wear with features extracted from the force sensor and the main and feed drive current sensors signals recorded under different cutting conditions and different tool wear states. Signal features extracted from both the time and frequency domain were used as input parameters for construction of a decision tree which classifies the tool state into sharp or worn. The model was refined by selecting only the feature sources most important for classification. The best model achieves 90% accuracy in classification and relies only on features of the current signals, which simplifies its implementation in a CNC system for industrial applications. |
| Author | Udiljak, Toma Klaic, Miho Staroveski, Tomislav |
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| Cites_doi | 10.1016/j.culher.2009.11.004 10.1016/S0890-6955(02)00040-8 10.1016/S0890-6955(03)00023-3 10.1127/1860-1804/2007/0158-0665 10.1016/j.ijmachtools.2005.05.012 10.1023/A:1008280620621 10.1016/S0890-6955(00)00112-7 10.1016/j.cirp.2010.05.010 10.1115/1.3439211 10.1016/S0890-6955(00)00111-5 10.1016/j.ijmachtools.2009.08.002 10.1016/0043-1648(94)06539-X 10.1515/rbm-2000-5478 10.1007/978-3-642-18638-7_6 10.1016/j.jmapro.2012.09.019 10.1016/S0890-6955(97)00066-7 10.2507/22nd.daaam.proceedings.012 10.1515/rbm-2004-5855 10.1016/j.ijmachtools.2009.10.004 |
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| References | M. Pamplona, M. Kocher, L. Aires-Barros, “Drilling resistance: overview and outlook”, Zeitschrift der Deutschen Gesellschaft für Geowissenschaften, Vol. 158, no. 3, pp. 665-679, 2007. I. Abu-Mahfouz, “Drilling wear detection and classification using vibration signals and artificial neural network”, International Journal of Machine Tools and Manufacture, Vol. 43, no. 7, pp. 707-720, 2003. R. Teti, K. Jemielniak, G. O’Donnell, D. Dornfeld, “Advanced monitoring of machining operations”, CIRP Annals - Manufacturing Technology, Vol. 59, no. 2, pp. 717-739, 2010. X. Wang, P.Y. Kwon, C. Sturtevant, D. Kim, J. Lantrip, “Tool wear of coated drills in drilling CFRP”, Journal of Manufacturing Processes, Vol. 15, no. 1, pp. 127-135, 2013. G. Exadaktylos, P. Tiano, C. Flareto, “Validation of a model of rotary drilling of rocks with the drilling force measurement system”, International Journal for Restoration of Buildings and Monuments, Vol. 6, no. 3, pp. 307-340, 2000. H.M. Ertunc, K.A. Loparo, H. Ocak, “Tool wear condition monitoring in drilling operations using hidden Markov models (HMMs)”, International Journal of Machine Tools and Manufacture, Vol. 41, no. 9, pp. 1363-1384, 2001. J. D. Rodrigues, D. Costa, “A New Method for Data Correction in Drill Resistance Tests for the Effect of Drill Bit Wear”, International Journal for Restoration, Vol. 10, no. 3, pp. 1-18, 2004. T. Staroveski, D. Brezak, T. Udiljak, D. Majetic, “Experimental Machine Tool for Process Monitoring and Control Systems Research”, in 22nd Daaam International Symposium, Annals OfDaaam International 2011, Vienna, 2011, pp. 0023-0024. L. A. Franco-Gasca, G. Herrera-Ruiz, R. Peniche-Vera, R. de J. Romero-Troncoso, W. Leal-Tafolla, “Sensorless tool failure monitoring system for drilling machines”, International Journal of Machine Tools and Manufacture, Vol. 46, no. 3-4, pp. 381-386, 2006. E. Jantunen, “A summary of methods applied to tool condition monitoring in drilling”, Journal of Machine Tools and Manufacture, Vol. 42, no. 9, pp. 997-1010, 2002. K. Subramanian, N.H. Cook, “Sensing of drill wear and prediction of drill life (I)”, Journal of Engineering for Industry, Transactions of the ASME, Vol. 99, no. 2, pp. 295-301, 1977. A. Bartulović, “Stone drilling”, Diploma thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2013. J. Ellson, E.R. Gansner, E. Koutsofios, S.C. North, G. Woodhull, “Graphviz and Dynagraph — Static and Dynamic Graph Drawing Tools”, in Graph Drawing Software., Springer Berlin Heidelberg, 2004, ch. Mathematics and Visualization, pp. 127-148. L. Xiaoli, “On-line detection of the breakage of small diameter drills using current signature wavelet transform”, International Journal of Machine Tools and Manufacture, Vol. 39, no. 1, pp. 157-164, 1999. T. Staroveski, “Wear Modelling of Medical Drill”, PhD thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, Zagreb, 2013. I. Kononenko, E. Simec, M. Robnik-Sikonja, “Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF”, Applied Intelligence, Vol. 7, no. 1, pp. 39-55, 1997. I.H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. San Francisco, CA, USA, Morgan Kaufmann, 1999. S.C. Lin, C.J. Ting, “Tool wear monitoring in drilling using force signals”, Wear, Vol. 180, no. 1-2, pp. 53-60, 1995. H.M. Ertunc, K.A. Loparo, “A decision fusion algorithm for tool wear condition monitoring in drilling”, International Journal of Machine Tools and Manufacture, Vol. 41, no. 9, pp. 1347-1362, 2001. A. Faraz, D. Biermann, K. Weinert, “Cutting edge rounding: An innovative tool wear criterion in drilling CFRP composite laminates”, International Journal of Machine Tools and Manufacture, Vol. 49, no. 15, pp. 1185-1196, 2009. M. Pamplona, M. Kocher, R. Snethlage, “Halite – A new calibration material for microdrilling resistance measurements”, Journal of cultural heritage, Vol. 11, no. 2, pp. 180-184, 2010. D. Iliescu, D. Gehin, M.E. Gutierrez, F. Girot, “Modeling and tool wear in drilling of CFRP”, International Journal of Machine Tools and Manufacture, Vol. 50, no. 2, pp. 204-213, 2010. 10.1016/j.proeng.2014.03.125_bib0060 10.1016/j.proeng.2014.03.125_bib0070 10.1016/j.proeng.2014.03.125_bib0080 10.1016/j.proeng.2014.03.125_bib0090 10.1016/j.proeng.2014.03.125_bib0105 10.1016/j.proeng.2014.03.125_bib0005 10.1016/j.proeng.2014.03.125_bib0015 10.1016/j.proeng.2014.03.125_bib0025 10.1016/j.proeng.2014.03.125_bib0020 10.1016/j.proeng.2014.03.125_bib0075 10.1016/j.proeng.2014.03.125_bib0030 10.1016/j.proeng.2014.03.125_bib0085 10.1016/j.proeng.2014.03.125_bib0040 10.1016/j.proeng.2014.03.125_bib0095 10.1016/j.proeng.2014.03.125_bib0050 10.1016/j.proeng.2014.03.125_bib0035 10.1016/j.proeng.2014.03.125_bib0045 10.1016/j.proeng.2014.03.125_bib0100 10.1016/j.proeng.2014.03.125_bib0055 10.1016/j.proeng.2014.03.125_bib0110 10.1016/j.proeng.2014.03.125_bib0010 10.1016/j.proeng.2014.03.125_bib0065 |
| References_xml | – reference: X. Wang, P.Y. Kwon, C. Sturtevant, D. Kim, J. Lantrip, “Tool wear of coated drills in drilling CFRP”, Journal of Manufacturing Processes, Vol. 15, no. 1, pp. 127-135, 2013. – reference: A. Faraz, D. Biermann, K. Weinert, “Cutting edge rounding: An innovative tool wear criterion in drilling CFRP composite laminates”, International Journal of Machine Tools and Manufacture, Vol. 49, no. 15, pp. 1185-1196, 2009. – reference: I.H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. San Francisco, CA, USA, Morgan Kaufmann, 1999. – reference: H.M. Ertunc, K.A. Loparo, H. Ocak, “Tool wear condition monitoring in drilling operations using hidden Markov models (HMMs)”, International Journal of Machine Tools and Manufacture, Vol. 41, no. 9, pp. 1363-1384, 2001. – reference: H.M. Ertunc, K.A. Loparo, “A decision fusion algorithm for tool wear condition monitoring in drilling”, International Journal of Machine Tools and Manufacture, Vol. 41, no. 9, pp. 1347-1362, 2001. – reference: G. Exadaktylos, P. Tiano, C. Flareto, “Validation of a model of rotary drilling of rocks with the drilling force measurement system”, International Journal for Restoration of Buildings and Monuments, Vol. 6, no. 3, pp. 307-340, 2000. – reference: E. Jantunen, “A summary of methods applied to tool condition monitoring in drilling”, Journal of Machine Tools and Manufacture, Vol. 42, no. 9, pp. 997-1010, 2002. – reference: M. Pamplona, M. Kocher, L. Aires-Barros, “Drilling resistance: overview and outlook”, Zeitschrift der Deutschen Gesellschaft für Geowissenschaften, Vol. 158, no. 3, pp. 665-679, 2007. – reference: L. Xiaoli, “On-line detection of the breakage of small diameter drills using current signature wavelet transform”, International Journal of Machine Tools and Manufacture, Vol. 39, no. 1, pp. 157-164, 1999. – reference: I. Abu-Mahfouz, “Drilling wear detection and classification using vibration signals and artificial neural network”, International Journal of Machine Tools and Manufacture, Vol. 43, no. 7, pp. 707-720, 2003. – reference: R. Teti, K. Jemielniak, G. O’Donnell, D. Dornfeld, “Advanced monitoring of machining operations”, CIRP Annals - Manufacturing Technology, Vol. 59, no. 2, pp. 717-739, 2010. – reference: K. Subramanian, N.H. Cook, “Sensing of drill wear and prediction of drill life (I)”, Journal of Engineering for Industry, Transactions of the ASME, Vol. 99, no. 2, pp. 295-301, 1977. – reference: I. Kononenko, E. Simec, M. Robnik-Sikonja, “Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF”, Applied Intelligence, Vol. 7, no. 1, pp. 39-55, 1997. – reference: J. Ellson, E.R. Gansner, E. Koutsofios, S.C. North, G. Woodhull, “Graphviz and Dynagraph — Static and Dynamic Graph Drawing Tools”, in Graph Drawing Software., Springer Berlin Heidelberg, 2004, ch. Mathematics and Visualization, pp. 127-148. – reference: T. Staroveski, “Wear Modelling of Medical Drill”, PhD thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, Zagreb, 2013. – reference: L. A. Franco-Gasca, G. Herrera-Ruiz, R. Peniche-Vera, R. de J. Romero-Troncoso, W. Leal-Tafolla, “Sensorless tool failure monitoring system for drilling machines”, International Journal of Machine Tools and Manufacture, Vol. 46, no. 3-4, pp. 381-386, 2006. – reference: M. Pamplona, M. Kocher, R. Snethlage, “Halite – A new calibration material for microdrilling resistance measurements”, Journal of cultural heritage, Vol. 11, no. 2, pp. 180-184, 2010. – reference: A. Bartulović, “Stone drilling”, Diploma thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2013. – reference: S.C. Lin, C.J. Ting, “Tool wear monitoring in drilling using force signals”, Wear, Vol. 180, no. 1-2, pp. 53-60, 1995. – reference: D. Iliescu, D. Gehin, M.E. Gutierrez, F. Girot, “Modeling and tool wear in drilling of CFRP”, International Journal of Machine Tools and Manufacture, Vol. 50, no. 2, pp. 204-213, 2010. – reference: T. Staroveski, D. Brezak, T. Udiljak, D. Majetic, “Experimental Machine Tool for Process Monitoring and Control Systems Research”, in 22nd Daaam International Symposium, Annals OfDaaam International 2011, Vienna, 2011, pp. 0023-0024. – reference: J. D. Rodrigues, D. Costa, “A New Method for Data Correction in Drill Resistance Tests for the Effect of Drill Bit Wear”, International Journal for Restoration, Vol. 10, no. 3, pp. 1-18, 2004. – ident: 10.1016/j.proeng.2014.03.125_bib0070 doi: 10.1016/j.culher.2009.11.004 – ident: 10.1016/j.proeng.2014.03.125_bib0010 doi: 10.1016/S0890-6955(02)00040-8 – ident: 10.1016/j.proeng.2014.03.125_bib0020 doi: 10.1016/S0890-6955(03)00023-3 – ident: 10.1016/j.proeng.2014.03.125_bib0095 – ident: 10.1016/j.proeng.2014.03.125_bib0080 doi: 10.1127/1860-1804/2007/0158-0665 – ident: 10.1016/j.proeng.2014.03.125_bib0100 – ident: 10.1016/j.proeng.2014.03.125_bib0035 doi: 10.1016/j.ijmachtools.2005.05.012 – ident: 10.1016/j.proeng.2014.03.125_bib0105 doi: 10.1023/A:1008280620621 – ident: 10.1016/j.proeng.2014.03.125_bib0030 doi: 10.1016/S0890-6955(00)00112-7 – ident: 10.1016/j.proeng.2014.03.125_bib0005 doi: 10.1016/j.cirp.2010.05.010 – ident: 10.1016/j.proeng.2014.03.125_bib0085 – ident: 10.1016/j.proeng.2014.03.125_bib0045 doi: 10.1115/1.3439211 – ident: 10.1016/j.proeng.2014.03.125_bib0015 doi: 10.1016/S0890-6955(00)00111-5 – ident: 10.1016/j.proeng.2014.03.125_bib0025 doi: 10.1016/j.ijmachtools.2009.08.002 – ident: 10.1016/j.proeng.2014.03.125_bib0040 doi: 10.1016/0043-1648(94)06539-X – ident: 10.1016/j.proeng.2014.03.125_bib0065 doi: 10.1515/rbm-2000-5478 – ident: 10.1016/j.proeng.2014.03.125_bib0110 doi: 10.1007/978-3-642-18638-7_6 – ident: 10.1016/j.proeng.2014.03.125_bib0055 doi: 10.1016/j.jmapro.2012.09.019 – ident: 10.1016/j.proeng.2014.03.125_bib0050 doi: 10.1016/S0890-6955(97)00066-7 – ident: 10.1016/j.proeng.2014.03.125_bib0090 doi: 10.2507/22nd.daaam.proceedings.012 – ident: 10.1016/j.proeng.2014.03.125_bib0075 doi: 10.1515/rbm-2004-5855 – ident: 10.1016/j.proeng.2014.03.125_bib0060 doi: 10.1016/j.ijmachtools.2009.10.004 |
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| Title | Tool Wear Classification Using Decision Treesin Stone Drilling Applications: A Preliminary Study |
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