Carbide Coated Insert Health Monitoring Using Machine Learning Approach through Vibration Analysis
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Published
Nov 16, 2020
Navneet Bohara
Jegadeeshwaran. R
Sakthivel G
Abstract
Growth in the manufacturing sector demands extensive production with precision, accuracy, tolerance, and quality. These essential factors need to be ensured for any kind of job. The listed factors stated above depend upon the condition of the tool used for manufacturing. A lot of methods have been proposed for the tool condition monitoring, based on the data acquired through acquisition techniques. Despite the continuous intensive scientific research for more than a decade, the development of tool condition monitoring is an on-going attempt. The proposed method deals with monitoring the health condition of the carbide inserts using vibration analysis. The statistical information extracted from the vibration signals was analyzed using machine learning approach in order to predict the tool condition.
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Keywords
Carbide inserts, machine learning, vibration analysis, statistical features, Confusion matrix
References
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Bai Yang, Lihua Guo, Lianwen Jin, & Qinghua Huang. (2009). A novel feature extraction method using pyramid histogram of orientation gradients for smile recognition. IEEE International Conference on Image Processing, November 7 – 12, Cairo, Egypt.
Bernhard Sick. (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, vol. 16, no. 4, pp. 487-546.
Buntine, W. & Niblett, T. (1992), A Further Comparison of Splitting Rules for Decision-Tree Induction, Machine Learning, Vol. 8, pp. 75–85.
Chen Shang-Liang, & Jen Y.W. (2000). Data fusion neural network for tool condition monitoring in CNC milling machining. International Journal of Machine Tools and Manufacture. vol. 40, no. 3, pp. 381-400.
Chao Jin, Agusmian P Ompusunggu, Zongchang Liu, Hossein D Ardakani, Fredrik Petre & Jay Lee. (2015). Envelope Analysis on Vibration Signals for Stator Winding Fault Early Detection in 3-Phase Induction Motors, International Journal of Prognostics and Health Management, vol. 6, no. 1, pp. 1-12.
Das S., Chattopadhyay, A. B., & Murthy A.S.R. (1996). Force Parameters for On-line Tool Wear Estimation: A Neural Network Approach, Neural Networks, vol. 9, no. 9, pp. 1639-1645
Das S., Roy R., & Chttopadhyay, A. B. (1996). Evaluation of wear of turning carbide inserts using neural networks. International Journal of Machine tool manufacturing. vol. 36, no. 7, pp. 789 -797.
Dash, M. & Liu, H. (1997). Feature selection for classification. Intelligent data analysis. vol. 1, pp. 131-156.
Dimla, E. Dimla, Snr. (2000), Sensor signals for tool wear monitoring in metal cutting operations - a review of methods, International Journal of Machine Tools and Manufacture, vol. 40, no. 8, pp. 1073-1098,
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Elangovan, M., Sakthivel, N. R., Saravanamurugan, S., Nair B. B., & Sugumaran, V. (2015). Machine Learning Approach to the Prediction of Surface Roughness Using Statistical Features of Vibration Signal Acquired in Turning Procedia Computer Science, vol. 50, pp. 282-288.
Elangovan, M., S. Babu Devasenapati, & Ramachandran, K. I. (2009). Condition Monitoring of a Single Point Cutting Tool Using Support Vector Machine, International Conference on Advances in Mechanical and Building Sciences (ICAMB-2009). Vellore Institute of Technology, Vellore.
Gavin C. Cawley & Talbot L. C. (2010). On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation, Journal of Machine Learning Research, vol. 11, pp. 2079 - 2107.
Gavin C. Cawley & Talbot L. C. (2007). Preventing over-fitting during model selection via Bayesian regularisation of the hyper-parameters. Journal of Machine Learning Research, vol. 8, pp. 841–861.
Ghosh, N, Ravi, Y. B., Patra, A., Mukhopadhyay, S., Paul S., Mohanty, A. R., & Chattopadhyay, A. B. (2005). Estimation of tool wear during CNC milling using neural network-based sensor fusion. Mechanical Systems and Signal Processing, vol. 21, no. 1, pp. 466-479.
Hall P. & Robinson A. P. (2009). Reducing the variability of crossvalidation for smoothing parameter choice. Biometrika, vol. 96, No. 1, pp. 175–186.
Jegadeeshwaran, R. & Sugumaran, V. (2015). Health Monitoring of a Hydraulic Brake System Using Nested Dichotomy Classifier–A Machine Learning approach, International Journal of Prognostics and Health Management, vol. 10, no.1(14), pp. 1-10.
Jegadeeshwaran, R., & Sugumaran, V., (2013). Comparative study of decision tree classifier and best first tree classifier for fault diagnosis of automobile hydraulic brake system using statistical features, Measurement, vol. 46, no.9, pp. 3247-3260.
Jaydeep M. Karandikar, Ali Abbas& Tony L. Schmitz. (2013). Remaining useful tool life predictions in turning using Bayesian inference, International Journal of Prognostics and Health Management, vol. 4, no. 2, pp. 1-11.
Joseph, C. Chen, & Wei-liang, Chen. (1999). A tool breakage detection system using an accelerometer sensor, Journal of Intelligent Manufacturing, vol. 10, no.2, pp. 187-197.
Kulkarni S. R., Lugosi G., & Venkatesh S. S. (1998). Learning pattern classification — a survey. IEEE Transactions on Information Theory, vol. 44, No. 6, pp. 2178–2206.
Kurada, S., & Bradley, C., (1997). A review of machine vision sensors for tool condition monitoring. Computers in Industry, vol. 34, no. 1, pp. 55–72.
Li H.Z., Zeng .H., & Chen X.Q., (2006). An experimental study of tool wear and cutting force variation in the end milling of Inconel 718 with coated carbide inserts, Journal of Materials Processing Technology, vol. 180, no. 1-2, pp. 296-304,
Lu Xiaoguang, & Anil, K. Jain., (2006). Automatic feature extraction for multiview 3D face recognition. International Conference on Automatic Face and Gesture Recognition, April 2006, Southampton, UK.
Painuli Sanidhya., Elangovan, M., & Sugumaran, V. (2014). Tool condition monitoring using K-star algorithm, Expert Systems with Applications, vol. 41, no. 6, pp. 2638-2643.
Qi Y., Minka T. P., Picard R. W., & Ghahramani Z. Predictive automatic relevance determination by expectation propagation. In Proceedings of the Twenty First International Conference on Machine Learning (ICML-04), July 2004, Banff, Alberta, Canada.
Saimurugan, M., Ramachandran, K. I., Sugumaran, V., & Sakthivel N. R., (2011). Multi-component fault diagnosis of a rotational mechanical system based on decision tree and support vector machine, Expert Systems with Applications, vol. 38, no. 4, pp. 3819-3826.
Silva, R. G ., Reuben, R. L., & Wilcox, S. J. (1998). Tool wear monitoring of turning operation by neural network and expert system classification of a feature set generated from multiple sensors. Mechanical System and Signal Process, vol. 12, no.2, pp. 319–332.
Song F., Guo Z., & Mei D., (2010). Feature selection using principal component analysis. International Conference on System Science, Engineering Design and Manufacturing Informatization, Yichang, China. pp. 27-30.
Subasi, Abdulhamit. (2007). EEG signal classification using wavelet feature extraction and a mixture of the expert model. Expert Systems with Applications, vol. 32, no. 4, pp. 1084 - 1093.
Sugumaran, V., Muralidharan, V. & Ramachandran, K. I. (2007). Feature Selection using Decision Tree and classification through Proximal Support Vector Machine for fault diagnostics of roller bearing, Mechanical Systems and Signal Processing, vol. 21, no. 2, pp. 930-942.
Witten, Ian, H., & Eibe Frank. (2005). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann Publishers.
Xiaorui Tong, Hossein D. Ardakani, David Siegel, Ellen Gamel & Jay Lee. (2017). A Novel Methodology for Fault Identification of Multi-stage Manufacturing Process Using Product Quality Measurement, International Journal of Prognostics and Health Management, vol. 8, no. 1, pp. 1-10.
Yen, Gary, G., & Lin, K. C. (2000). Wavelet packet feature extraction for vibration monitoring. IEEE transactions on industrial electronics, vol. 47, no.3, pp. 650-667.
Bai Yang, Lihua Guo, Lianwen Jin, & Qinghua Huang. (2009). A novel feature extraction method using pyramid histogram of orientation gradients for smile recognition. IEEE International Conference on Image Processing, November 7 – 12, Cairo, Egypt.
Bernhard Sick. (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, vol. 16, no. 4, pp. 487-546.
Buntine, W. & Niblett, T. (1992), A Further Comparison of Splitting Rules for Decision-Tree Induction, Machine Learning, Vol. 8, pp. 75–85.
Chen Shang-Liang, & Jen Y.W. (2000). Data fusion neural network for tool condition monitoring in CNC milling machining. International Journal of Machine Tools and Manufacture. vol. 40, no. 3, pp. 381-400.
Chao Jin, Agusmian P Ompusunggu, Zongchang Liu, Hossein D Ardakani, Fredrik Petre & Jay Lee. (2015). Envelope Analysis on Vibration Signals for Stator Winding Fault Early Detection in 3-Phase Induction Motors, International Journal of Prognostics and Health Management, vol. 6, no. 1, pp. 1-12.
Das S., Chattopadhyay, A. B., & Murthy A.S.R. (1996). Force Parameters for On-line Tool Wear Estimation: A Neural Network Approach, Neural Networks, vol. 9, no. 9, pp. 1639-1645
Das S., Roy R., & Chttopadhyay, A. B. (1996). Evaluation of wear of turning carbide inserts using neural networks. International Journal of Machine tool manufacturing. vol. 36, no. 7, pp. 789 -797.
Dash, M. & Liu, H. (1997). Feature selection for classification. Intelligent data analysis. vol. 1, pp. 131-156.
Dimla, E. Dimla, Snr. (2000), Sensor signals for tool wear monitoring in metal cutting operations - a review of methods, International Journal of Machine Tools and Manufacture, vol. 40, no. 8, pp. 1073-1098,
Elangovan, M. S., Babu Devasenapati, S., Sakthivel, N. R. & Ramachandran, K. I. (2011). Evaluation of an expert system for condition monitoring of a single point cutting tool using principle component analysis and decision tree algorithm. Expert Systems with Applications, vol. 38, no. 4, pp. 4450-4459.
Elangovan, M., Sakthivel, N. R., Saravanamurugan, S., Nair B. B., & Sugumaran, V. (2015). Machine Learning Approach to the Prediction of Surface Roughness Using Statistical Features of Vibration Signal Acquired in Turning Procedia Computer Science, vol. 50, pp. 282-288.
Elangovan, M., S. Babu Devasenapati, & Ramachandran, K. I. (2009). Condition Monitoring of a Single Point Cutting Tool Using Support Vector Machine, International Conference on Advances in Mechanical and Building Sciences (ICAMB-2009). Vellore Institute of Technology, Vellore.
Gavin C. Cawley & Talbot L. C. (2010). On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation, Journal of Machine Learning Research, vol. 11, pp. 2079 - 2107.
Gavin C. Cawley & Talbot L. C. (2007). Preventing over-fitting during model selection via Bayesian regularisation of the hyper-parameters. Journal of Machine Learning Research, vol. 8, pp. 841–861.
Ghosh, N, Ravi, Y. B., Patra, A., Mukhopadhyay, S., Paul S., Mohanty, A. R., & Chattopadhyay, A. B. (2005). Estimation of tool wear during CNC milling using neural network-based sensor fusion. Mechanical Systems and Signal Processing, vol. 21, no. 1, pp. 466-479.
Hall P. & Robinson A. P. (2009). Reducing the variability of crossvalidation for smoothing parameter choice. Biometrika, vol. 96, No. 1, pp. 175–186.
Jegadeeshwaran, R. & Sugumaran, V. (2015). Health Monitoring of a Hydraulic Brake System Using Nested Dichotomy Classifier–A Machine Learning approach, International Journal of Prognostics and Health Management, vol. 10, no.1(14), pp. 1-10.
Jegadeeshwaran, R., & Sugumaran, V., (2013). Comparative study of decision tree classifier and best first tree classifier for fault diagnosis of automobile hydraulic brake system using statistical features, Measurement, vol. 46, no.9, pp. 3247-3260.
Jaydeep M. Karandikar, Ali Abbas& Tony L. Schmitz. (2013). Remaining useful tool life predictions in turning using Bayesian inference, International Journal of Prognostics and Health Management, vol. 4, no. 2, pp. 1-11.
Joseph, C. Chen, & Wei-liang, Chen. (1999). A tool breakage detection system using an accelerometer sensor, Journal of Intelligent Manufacturing, vol. 10, no.2, pp. 187-197.
Kulkarni S. R., Lugosi G., & Venkatesh S. S. (1998). Learning pattern classification — a survey. IEEE Transactions on Information Theory, vol. 44, No. 6, pp. 2178–2206.
Kurada, S., & Bradley, C., (1997). A review of machine vision sensors for tool condition monitoring. Computers in Industry, vol. 34, no. 1, pp. 55–72.
Li H.Z., Zeng .H., & Chen X.Q., (2006). An experimental study of tool wear and cutting force variation in the end milling of Inconel 718 with coated carbide inserts, Journal of Materials Processing Technology, vol. 180, no. 1-2, pp. 296-304,
Lu Xiaoguang, & Anil, K. Jain., (2006). Automatic feature extraction for multiview 3D face recognition. International Conference on Automatic Face and Gesture Recognition, April 2006, Southampton, UK.
Painuli Sanidhya., Elangovan, M., & Sugumaran, V. (2014). Tool condition monitoring using K-star algorithm, Expert Systems with Applications, vol. 41, no. 6, pp. 2638-2643.
Qi Y., Minka T. P., Picard R. W., & Ghahramani Z. Predictive automatic relevance determination by expectation propagation. In Proceedings of the Twenty First International Conference on Machine Learning (ICML-04), July 2004, Banff, Alberta, Canada.
Saimurugan, M., Ramachandran, K. I., Sugumaran, V., & Sakthivel N. R., (2011). Multi-component fault diagnosis of a rotational mechanical system based on decision tree and support vector machine, Expert Systems with Applications, vol. 38, no. 4, pp. 3819-3826.
Silva, R. G ., Reuben, R. L., & Wilcox, S. J. (1998). Tool wear monitoring of turning operation by neural network and expert system classification of a feature set generated from multiple sensors. Mechanical System and Signal Process, vol. 12, no.2, pp. 319–332.
Song F., Guo Z., & Mei D., (2010). Feature selection using principal component analysis. International Conference on System Science, Engineering Design and Manufacturing Informatization, Yichang, China. pp. 27-30.
Subasi, Abdulhamit. (2007). EEG signal classification using wavelet feature extraction and a mixture of the expert model. Expert Systems with Applications, vol. 32, no. 4, pp. 1084 - 1093.
Sugumaran, V., Muralidharan, V. & Ramachandran, K. I. (2007). Feature Selection using Decision Tree and classification through Proximal Support Vector Machine for fault diagnostics of roller bearing, Mechanical Systems and Signal Processing, vol. 21, no. 2, pp. 930-942.
Witten, Ian, H., & Eibe Frank. (2005). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann Publishers.
Xiaorui Tong, Hossein D. Ardakani, David Siegel, Ellen Gamel & Jay Lee. (2017). A Novel Methodology for Fault Identification of Multi-stage Manufacturing Process Using Product Quality Measurement, International Journal of Prognostics and Health Management, vol. 8, no. 1, pp. 1-10.
Yen, Gary, G., & Lin, K. C. (2000). Wavelet packet feature extraction for vibration monitoring. IEEE transactions on industrial electronics, vol. 47, no.3, pp. 650-667.
Section
Technical Papers