Feature level fusion of vibration and acoustic emission signals in tool condition monitoring using machine learning classifiers
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Published
Nov 19, 2020
P. Krishnakumar
K. Rameshkumar
K. I. Ramachandran
Abstract
To implement the tool condition monitoring system in a metal cutting process, it is necessary to have sensors which will be able to detect the tool conditions to initiate remedial action. There are different signals for monitoring the cutting process which may require different sensors and signal processing techniques. Each of these signals is capable of providing information about the process at different reliability level. To arrive a good, reliable and robust decision, it is necessary to integrate the features of the different signals captured by the sensors. In this paper, an attempt is made to fuse the features of acoustic emission and vibration signals captured in a precision high speed machining center for monitoring the tool conditions. Tool conditions are classified using machine learning classifiers. The classification efficiency of machine learning algorithms are studied in time-domain, frequencydomain and time-frequency domain by feature level fusion of features extracted from vibration and acoustic emission signature.
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Keywords
Tool condition monitoring, vibration, acoustic emission, feature level fusion, machine learning algorithms
References
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Hutton, D. V., and Hu, F. (1999). Acoustic emission monitoring of tool wear in end-milling using timedomain averaging. Journal of manufacturing science and engineering, 121(1), 8-12
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Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1988). Learning representations by back-propagating errors. Cognitive modeling, 5(3), 1.
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Saimurugan, M., Nithesh, R.(2016) : Intelligent fault diagnosis model for rotating machinery based on fusion of sound signals, International Journal of Prognostics and Health Management, 018 , 2153-2648.
Salgado, D. R., Alonso, F. J., Cambero, I., and Marcelo, A. (2009). In-process surface roughness prediction system using cutting vibrations in turning. The International Journal of Advanced Manufacturing Technology, 43(1-2), 40-51.
Shi, D., and Gindy, N. N. (2007). Tool wear predictive model based on least squares support vector machines. Mechanical Systems and Signal Processing, 21(4), 1799-1814.
Sick, B. (2002). On-line and indirect tool wear monitoring in turning with artificial neural networks: a review of more than a decade of research. Mechanical Systems and Signal Processing, 16(4), 487-546.
Cho, S., Binsaeid, S., and Asfour, S. (2009): Design of multi sensor fusion-based tool condition monitoring system in end milling, International Journal of Advanced Manufacturing Technology, 46(5-8), 681-694.
Stavropoulos, P., Chantzis, D., Doukas, C., Papacharalampopoulos, A., and Chryssolouris, G. (2013). Monitoring and control of manufacturing processes: A review. Procedia CIRP, 8, 421-425.
Teti, R., Jemielniak, K., O’Donnell, G., and Dornfeld, D. (2010). Advanced monitoring of machining operations. CIRPAnnals-Manufacturing Technology, 59(2), 717-739.
Banerjee, T.P. and Das, S. (2012): Multi-sensor data fusion using support vector machine for motor fault detection, Information Sciences, 217, 96–107.
Wang, P., Meng, Q., Zhao, J., Li, J., and Wang, X. (2011). Prediction of machine tool condition using support vector machine. In Journal of Physics: Conference Series (Vol. 305, No. 1, p. 012113). IOP Publishing.
Wang, W.H., Wong, Y.S., Hong, G.S., and Zhu, K.P. (2007): Sensor fusion for on-line tool condition monitoring in milling, International Journal of Production Research, 45(21), 5095-5116.
Zhang, C., Yao, X., Zhang, J., and Jin, H. (2016). Tool Condition Monitoring and Remaining Useful Life Prognostic Based on a Wireless Sensor in Dry Milling Operations. Sensors, 16(6), 795.
Zhong, W., Zhao, D., and Wang, X. (2010). A comparative study on dry milling and little quantity lubricant milling based on vibration signals. International Journal of Machine Tools and Manufacture, 50(12), 1057-1064.
Aliustaoglu, C., Ertunc, H. M., Ocak, H. (2009): Tool wear condition monitoring using a sensor fusion model based on fuzzy inference system, Mechanical Systems and Signal Processing, 23, 539-546.
Arun, A., Rameshkumar, K., Unnikrishnan, D., and Sumesh, A. (2018). Tool condition monitoring of cylindrical grinding process using acoustic emission sensor. ICMMM-2017. Materials Today: Proceedings.
Bhuiyan, M. S. H., Choudhury, I. A., Yusoff, N., and Dawal, S. Z. M. (2016). Application of acoustic emission sensor to investigate the frequency of tool wear and plastic deformation in tool condition monitoring. Measurement, 32, 208-217.
Carbonell, J. G., Michalski, R. S., and Mitchell, T. M. (1983). An overview of machine learning. In Machine learning (pp. 3-23). Springer Berlin Heidelberg.
Cortes, C., and Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
Dimla, D. E. (2002). The correlation of vibration signal features to cutting tool wear in a metal turning operation. The International Journal of Advanced Manufacturing Technology, 19(10), 705-713.
Dornfeld, D. A. (1991). Monitoring of the machining process by means of acoustic emission sensors. In Acoustic Emission: Current Practice and Future Directions. ASTM International.
Elangovan, M., Devasenapati, S. B., Sakthivel, N. R., & Ramachandran, K. I. (2011-b). Evaluation of expert system for condition monitoring of a single point cutting tool using principle component analysis and decision tree algorithm. Expert Systems with Applications, 38(4), 4450-4459.
Elangovan, M., Ramachandran, K. I., and Sugumaran, V. (2010). Studies on Bayes classifier for condition monitoring of single point carbide tipped tool based on statistical and histogram features. Expert Systems with Applications, 37(3), 2059-2065.
Elangovan, M., Sugumaran, V., Ramachandran, K. I., and Ravikumar, S. (2011-a). Effect of SVM kernel functions on classification of vibration signals of a single point cutting tool. Expert Systems with Applications, 38(12), 15202-15207.
Gunatilaka, A. H., and Baertlein, B. A. (2001). Feature-level and decision-level fusion of noncoincidently sampled sensors for land mine detection. IEEE transactions on pattern analysis and machine intelligence, 23(6), 577-589.
Hall, D. L., & Llinas, J. (1997): An introduction to multisensor data fusion. Proceedings of the IEEE, 85(1), 6-23.
Han, X., and Wu, T. (2013). Analysis of acoustic emission in precision and high-efficiency grinding technology. The International Journal of Advanced Manufacturing Technology, 67(9-12), 1997-2006.
Hase, A., Wada, M., Koga, T., and Mishina, H. (2014). The relationship between acoustic emission signals and cutting phenomena in turning process. The International Journal of Advanced Manufacturing Technology, 70(5- 8), 947-955.
Hutton, D. V., and Hu, F. (1999). Acoustic emission monitoring of tool wear in end-milling using timedomain averaging. Journal of manufacturing science and engineering, 121(1), 8-12
Inasaki, I. (1998). Application of acoustic emission sensor for monitoring machining processes. Ultrasonics, 36(1), 273-281.
Karpuschewski, B., Wehmeier, M., and Inasaki, I. (2000). Grinding monitoring system based on power and acoustic emission sensors. CIRP Annals-Manufacturing Technology, 49(1), 235-240.
Krishnakumar, P., Rameshkumar, K., and Ramachandran, K. I. (2015). Tool wear condition prediction using vibration signals in high speed machining (HSM) of titanium (Ti- 6Al-4V) alloy. Procedia Computer Science, 50, 270-275.
Krishnakumar, P., Rameshkumar, K., and Ramachandran, K. I. (2018). Machine learning based tool condition classification using acoustic and vibration data in high speed milling process using wavelet features. Intelligent Decision Technologies, 12 265–282.
Zhu,K., Wong,Y.S., and Hong, G.S. (2009): Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results, International Journal of Machine Tools and Manufacture, 49(7-8), 537–553.
Lamraoui, M., Thomas, M., El Badaoui, M., and Girardin, F. (2014). Indicators for monitoring chatter in milling based on instantaneous angular speeds. Mechanical Systems and Signal Processing, 44(1), 72-85.
Lauro, C. H., Brandão, L. C., Baldo, D., Reis, R. A., and Davim, J. P. (2014). Monitoring and processing signal applied in machining processes–A review. Measurement, 58, 73-86.
Marinescu, I., and Axinte, D. A. (2008). A critical analysis of effectiveness of acoustic emission signals to detect tool and work piece malfunctions in milling operations. International Journal of Machine Tools and Manufacture, 48(10), 1148-1160. 17.
Quinlan, J. R. (1993). C4. 5: Programming for machine learning. Morgan Kauffmann, 38.
Ravindra, H. V., Srinivasa, Y. G., and Krishnamurthy, R. (1997). Acoustic emission for tool condition monitoring in metal cutting. Wear, 212(1), 78-84.
Roth, J. T., Djurdjanovic, D., Yang, X., Mears, L., and Kurfess, T. (2010). Quality and inspection of machining operations: tool condition monitoring. Journal of Manufacturing Science and Engineering, 132(4),041015.
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1988). Learning representations by back-propagating errors. Cognitive modeling, 5(3), 1.
Russell, S. J., Norvig, P., Canny, J. F., Malik, J. M., and Edwards, D. D. (2003). Artificial intelligence: a modern approach (Vol. 2). Upper Saddle River: Prentice hall.
Saimurugan, M., Nithesh, R.(2016) : Intelligent fault diagnosis model for rotating machinery based on fusion of sound signals, International Journal of Prognostics and Health Management, 018 , 2153-2648.
Salgado, D. R., Alonso, F. J., Cambero, I., and Marcelo, A. (2009). In-process surface roughness prediction system using cutting vibrations in turning. The International Journal of Advanced Manufacturing Technology, 43(1-2), 40-51.
Shi, D., and Gindy, N. N. (2007). Tool wear predictive model based on least squares support vector machines. Mechanical Systems and Signal Processing, 21(4), 1799-1814.
Sick, B. (2002). On-line and indirect tool wear monitoring in turning with artificial neural networks: a review of more than a decade of research. Mechanical Systems and Signal Processing, 16(4), 487-546.
Cho, S., Binsaeid, S., and Asfour, S. (2009): Design of multi sensor fusion-based tool condition monitoring system in end milling, International Journal of Advanced Manufacturing Technology, 46(5-8), 681-694.
Stavropoulos, P., Chantzis, D., Doukas, C., Papacharalampopoulos, A., and Chryssolouris, G. (2013). Monitoring and control of manufacturing processes: A review. Procedia CIRP, 8, 421-425.
Teti, R., Jemielniak, K., O’Donnell, G., and Dornfeld, D. (2010). Advanced monitoring of machining operations. CIRPAnnals-Manufacturing Technology, 59(2), 717-739.
Banerjee, T.P. and Das, S. (2012): Multi-sensor data fusion using support vector machine for motor fault detection, Information Sciences, 217, 96–107.
Wang, P., Meng, Q., Zhao, J., Li, J., and Wang, X. (2011). Prediction of machine tool condition using support vector machine. In Journal of Physics: Conference Series (Vol. 305, No. 1, p. 012113). IOP Publishing.
Wang, W.H., Wong, Y.S., Hong, G.S., and Zhu, K.P. (2007): Sensor fusion for on-line tool condition monitoring in milling, International Journal of Production Research, 45(21), 5095-5116.
Zhang, C., Yao, X., Zhang, J., and Jin, H. (2016). Tool Condition Monitoring and Remaining Useful Life Prognostic Based on a Wireless Sensor in Dry Milling Operations. Sensors, 16(6), 795.
Zhong, W., Zhao, D., and Wang, X. (2010). A comparative study on dry milling and little quantity lubricant milling based on vibration signals. International Journal of Machine Tools and Manufacture, 50(12), 1057-1064.
Section
Technical Papers