Application of Bayesian Family Classifiers for Cutting Tool Inserts Health Monitoring on CNC Milling
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Published
Mar 24, 2021
Abhishek D. Patange
R Jegadeeshwaran
Abstract
The customized usage of tool inserts plays an imperative role in the economics of machining operations. Eventually, any in-process defects in the cutting tool lead to deterioration of complete machining activity. Such defects are untraceable by the conventional practices of condition monitoring. The characterization of such in-process tool defects needs to be addressed smartly. This would also assist the requirement of ‘self-monitoring’ in Industry 4.0. In this context, induction of supervised Machine Learning (ML) classifiers to design empirical classification models for tool condition monitoring is presented herein. The variation in faulty and fault-free tool condition is collected in terms of vibrations during the face milling process on CNC (Computer Numerically Controlled) machine tool. The statistical approach is incorporated to extract attributes and the dimensionality of the attributes is reduced using the J48 decision tree algorithm. The various conditions of tool inserts are then classified using two supervised algorithms viz. Bayes Net and Naïve Bayes from the Bayesian family.
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Keywords
condition monitoring, machine learning, vibration analysis, Accelerometer, Tool insert, CNC milling, time domain, descriptive statistics
References
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Balla S. P., Sarcar M. M. M., Satish Ben B. (2010) Development of a system for monitoring tool condition using acousto-optic emission signal in face turning–an experimental approach, International Journal of Advanced Manufacturing Technology, 51, 57–67.
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Elangovan M. (2011). 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, 4450–4459.
Elangovan M., Sugumaran V., Ramachandran K. I. (2011) Effect of SVM kernel functions on classification of vibration signals of a single point cutting tool, Expert Systems with Applications, 38 (12), 15202–15207.
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Karandikar J., Tom M., Sam T., Tony S. (2015) Tools wear monitoring using naive bayes classifiers. International Journal of Advanced Manufacturing Technology, 77, 1613–1626.
Kom Guide (2016) Technical Manual Drilling, Threading, Reaming, Milling, Komet Group, 1–325.
Bermingham M. J., Palanisamy S., Dargusch M. S. (2012) Understanding the Tool Wear Mechanism during Thermally Assisted Machining Ti–6Al–4V, International Journal of Machine Tools and Manufacture; 62, 76–87.
Rubeo M. A., Schmitz T. L. (2016) Global stability predictions for flexible workpiece milling using time- domain simulation, Journal of Manufacturing Systems, 40, 8–14.
Liu R, Kothuru A, Zhang S. (2020) Calibration-based tool condition monitoring for repetitive machining operations, Journal of Manufacturing Systems, 54, 285–293.
Aralikatti S. S., Ravikumar K. N., et al. (2020) Comparative Study on Tool Fault Diagnosis Methods Using Vibration Signals and Cutting Force Signals by Machine Learning Technique, Structural Durability & Health Monitoring, 14(2), 128–145.
Alamelu M. T. M. and Jegadeeshwaran R. (2019) Vibration based brake health monitoring using wavelet features: A machine learning approach, Journal of Vibration and Control, 1–17.
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Friedman N., Geiger D. (1997) Bayesian network classifier, Machine Learning, 29, 131–163.
Wiggins M., Saad A., Litt B. (2008) Evolving a Bayesian classifier for ECG-based age classification in medical applications, Applied Soft Computing, 8 (1), 599–608.
Hastie T., Tibshirani R. and Friedman J. (2017) High- dimensional problems, in The Elements of Statistical Learning, Springer-Verlag, 649-694
Roth J. T., Djurdjanovic D., Yang, X., Mears, L., Kurfess, T. (2010) Quality and Inspection of Machining Operations: Tool Condition Monitoring, Journal of Manufacturing Science and Engineering ASME, 132(4), 1–15.
Engin S. and Altintas Y. (2001) Mechanics and dynamics of general milling cutter, Part II: Inserted cutters, International Journal of Machine Tools and Manufacture, 41(15), 2213–2231.
Gidi Drori. (2015) Proper Cutting Tool Choice is Vital to Productivity. Production Machining: Gardner Business Media Incorporation.1–4.
Daneshmand L. K. and Pak H. A. (1986) Adaptive Control System using economic performance index in turning, Journal of dynamic systems measurement and control, 108, 215–222.
Dan Land, Mathew J. (1990) Tool wear and failure monitoring techniques or turning – a review, International Journal of Machine Tool Manufacture, 30, 579–598.
Maj R, Modica F., Bianchi G. (2006) Machine Tools – Mechatronic Analysis, Proc. Institute of Mechanical Engineers, 220 (B), 345–353.
Mohanraj T., Shankar S., Rajasekar R., Sakthivel N. R., Pramanik A. (2020) Tool condition monitoring techniques in milling process – A review, Journal of Materials Research Technology, 9(1), 1032–1042.
Dimla D. and Lister P. (2000) On-line metal cutting tool condition monitoring: force and vibration analyses, International Journal of Machine Tools Manufactures, 40, 739–768.
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, 705–713.
Zhou Y., Xue W. (2018) Review of tool condition monitoring methods in milling processes, The International Journal of Advanced Manufacturing Technology, 96, 2509–2523.
Hakan Arslan, Ali Osman Er, Sadettin Orhan, Ersan Aslan. (2016) Tool Condition Monitoring in Turning Using Statistical Parameters of Vibration Signal, International, Journal of Acoustics and Vibration, 21(4), 371–378.
Besmir Cuka and Dong-Won Kim. (2017) Fuzzy logic based tool condition monitoring for end-milling, Robotics and Computer-Integrated Manufacturing, 47(C), 22–36.
Yuqing Zhou, Wei Xue. (2018) A Multi-sensor Fusion Method for Tool Condition Monitoring in Milling, Sensors, 18 (11), 38–66.
Zhang X. Y., Lu X., Wang S., Wang W., Li W. D. (2018) A multi-sensor based online tool condition monitoring system for milling process, Proceedia CIRP, 72, 1136– 1141.
Pauline Ong, Woon Kiow Lee, Raymond Jit, Hoo Lau. (2019) Tool condition monitoring in CNC end milling using wavelet neural network based on machine vision, The International Journal of Advanced Manufacturing Technology, 104, 1369–1379.
Torabi A. J., Er M. J., Li X., Lim B. S., Peen G. O. (2016) Application of Clustering Methods for Online Tool Condition Monitoring and Fault Diagnosis in High-Speed Milling Processes, IEEE Systems, 10 (2), 721–732.
Shiba K. (2003) Development of a miniature abrasion- detecting device for a small precision lathe, Sensors Actuators: A Physics, 109, 137–142.
Siddhpura M., Siddhpura A., Bhave S. (2008). Vibration as a parameter for monitoring the health of precision machine tools, International conference on frontiers in design and manufacturing engineering.
Balla S. P., Sarcar M. M. M., Satish Ben B. (2010) Development of a system for monitoring tool condition using acousto-optic emission signal in face turning–an experimental approach, International Journal of Advanced Manufacturing Technology, 51, 57–67.
Narayanan R. V., Namboothiri V. (2010) Flank wear detection of cutting tool inserts in turning operation: application of nonlinear time series analysis, Soft Computation Fusion Found Methodology Application, 14, 913–919.
Ming L., Jiawei M., Dinghua Z. (2016) Time-domain modeling of a cutter exiting a workpiece in the slot milling process, Chinese Journal of Aeronautics, 29, 1852–1858.
Surendar S., Elangovan M. (2017) Comparison of Surface Roughness Prediction with Regression and Tree Based Regressions during Boring Operation, Indonesian Journal of Electrical Engineering and Computer Science, 7(3), 887–892.
Elangovan M., Ramachandran K. I., 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- Elsevier, 37, 2059–2065.
Sambayi P. M. K. (2012) Drill wear monitoring using instantaneous angular speed: A comparison with conventional technology used in drill monitoring systems, Masters Theses-University of Pretoria.
Jegadeeshwaran R., Sugumaran V. (2015) Brake fault diagnosis using Clonal Selection Classification Algorithm (CSCA): A statistical learning approach, Engineering Science and Technology, an International Journal, 18, 14–23.
Madhusudana C. K., Kumar H., Narendranath S. (2016) Condition monitoring of face milling tool using K-star algorithm and histogram features of vibration signal, Engineering Science and Technology, An International Journal-Elsevier, 19, 1543–1551.
Navneet Bohara, Jegadeeshwaran R., Sakthivel G., (2017) Carbide Coated Insert Health Monitoring Using Machine Learning Approach through Vibration Analysis, International Journal of Prognostics and Health Management, 24, 1–14.
Patange A. D., Jegadeeshwaran R. (2019) Milling cutter condition monitoring using machine learning approach, IOP Conference Series: Material Science and Engineering, 624:012030
Painuli S., Elangovan M. and Sugumaran V. (2014) Tool condition monitoring using K-star algorithm, Expert Systems with Applications, 41, 2638–2643.
Elangovan M. (2011). 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, 4450–4459.
Elangovan M., Sugumaran V., Ramachandran K. I. (2011) Effect of SVM kernel functions on classification of vibration signals of a single point cutting tool, Expert Systems with Applications, 38 (12), 15202–15207.
Shewale M. S., Mulik S. S., Deshmukh S. P., Patange A. D. (2018), A novel health monitoring system, Advances in Intelligent Systems and Computing Springer Singapore Proc. of 2nd Inter. Conf. on Data Engineering and Communication Technology, 828, 461–468.
Nalavade S. P., Patange A. D., Prabhune C. L. and Mulik S. S., (2018) Development of 12 Channel Temperature Acquisition System for Heat Exchanger Using MAX6675 and Arduino Interface, Lecture Notes in Mechanical Engineering, Springer, 1, 119-125
Olga Fink, Qin Wang, Markus Svensen, Pierre Dersin, Wan-Jui Lee, Melanie Ducoffe (2020) Potential challenges and future directions for deep learning in prognostics and health management applications, Engineering Applications of Artificial Intelligence, 92, 103678
Karandikar J., Tom M., Sam T., Tony S. (2015) Tools wear monitoring using naive bayes classifiers. International Journal of Advanced Manufacturing Technology, 77, 1613–1626.
Kom Guide (2016) Technical Manual Drilling, Threading, Reaming, Milling, Komet Group, 1–325.
Bermingham M. J., Palanisamy S., Dargusch M. S. (2012) Understanding the Tool Wear Mechanism during Thermally Assisted Machining Ti–6Al–4V, International Journal of Machine Tools and Manufacture; 62, 76–87.
Rubeo M. A., Schmitz T. L. (2016) Global stability predictions for flexible workpiece milling using time- domain simulation, Journal of Manufacturing Systems, 40, 8–14.
Liu R, Kothuru A, Zhang S. (2020) Calibration-based tool condition monitoring for repetitive machining operations, Journal of Manufacturing Systems, 54, 285–293.
Aralikatti S. S., Ravikumar K. N., et al. (2020) Comparative Study on Tool Fault Diagnosis Methods Using Vibration Signals and Cutting Force Signals by Machine Learning Technique, Structural Durability & Health Monitoring, 14(2), 128–145.
Alamelu M. T. M. and Jegadeeshwaran R. (2019) Vibration based brake health monitoring using wavelet features: A machine learning approach, Journal of Vibration and Control, 1–17.
Kingsford C., Salzberg S. L. (2008) What are decision trees? Nature Biotechnology, 26, 1011–1013
Jankowski D., Jackowski K., (2014) Evolutionary Algorithm for Decision Tree Induction, 13th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM) - Ho Chi Minh City, Vietnam, 23–32.
Friedman N., Geiger D. (1997) Bayesian network classifier, Machine Learning, 29, 131–163.
Wiggins M., Saad A., Litt B. (2008) Evolving a Bayesian classifier for ECG-based age classification in medical applications, Applied Soft Computing, 8 (1), 599–608.
Hastie T., Tibshirani R. and Friedman J. (2017) High- dimensional problems, in The Elements of Statistical Learning, Springer-Verlag, 649-694
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