1. A. Lamikiz, L. N.López de Lacalle, and A. Celaya, “Machine Tool Performance and Precision,”Machine Tools for High Performance Machining, pp. 219-260, Springer, London, 2009 2. G. Tlusty, J. Tlusty,E. J. Arnold, Manufacturing Processes and Equipment, Pearson Education, US, 2000 3. Y. Altintas and J. Peng, “Design and Analysis of a Modular CNC System,” Computers in Industry, Vol. 13, No. 4, pp. 305-316, March 1990 4. G. H. Lim,“Tool-Wear Monitoring in Machine Turning,” Journal of Materials Processing Technology, Vol. 51, No. 1-4, pp. 25-36, April 1995 5. D. E.Dimla Sr., “The Correlation of Vibration Signal Features to Cutting Tool Wear in a Metal Turning Operation,” International Journal of Advanced Manufacturing Technology, Vol. 19, No.10, pp. 705-713, June 2002 6. T. H. Nayfeh, O. K. Eyada,J. C.Duke Jr., “An Integrated Ultrasonic Sensor for Monitoring Gradual Wear On-line during Turning operations,” International Journal of Machine Tools and Manufacture, Vol. 35, No. 10, pp. 1385-1395, October 1995 7. N. H.Abu-Zahra and G. Yu, “Gradual Wear Monitoring of Turning Inserts using Wavelet Analysis of Ultrasound Waves,” International Journal of Machine Tools and Manufacture, Vol. 43, No. 4, pp. 337-343, March 2003 8. C. Scheffer, H. Engelbrecht,P. S. Heyns, “A Comparative Evaluation of Neural Networks and Hidden Markov Models for Monitoring Tool Wear,” Neural Computing and Applications, Vol. 14, No. 4, pp. 325-336, December 2005 9. J. R. Quinlan, “Induction of Decision Trees,” Machine Learning, Vol. 1, pp. 81-106, March 1986 10. V. Sugumaran, V. Muralidharan,K. I. Ramachandran, “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, February 2007 11. V. Inturi, G. R. Sabareesh, K. Supradeepan,P. K. Penumakala, “Integrated Condition Monitoring Scheme for Bearing Fault Diagnosis of a Wind Turbine Gearbox,” Journal of Vibration and Control, Vol. 25, No. 12, June 2019. 12. F. Li, G. Meng, L. Ye,P. Chen, “Wavelet Transform-based Higher-order Statistics for Fault Diagnosis in Rolling Element Bearings,” Journal of Vibration and Control, Vol. 14, No. 11, pp. 1691-1709, November 2008 13. K. Jemielniak and O. Otman, “Tool Failure Detection based on Analysis of Acoustic Emission Signals,” Journal of Material Processing Technology, Vol. 76, No.1-3, pp. 192-197, April 1998 14. D. Choi, W. T. Kwon,C. N. Chu, “Real-time Monitoring of Tool Fracture in Turning using Sensor Fusion,” International Journal of Advanced Manufacturing Technology, Vol. 15, No. 5, pp. 305-310, May 1999 15. G. Vetrichelvan, S. Sundaram, S. S. Kumaran,P. Velmurugan, “An Investigation of Tool Wear using Acoustic Emission and Genetic Algorithm,” Journal of Vibration and Control, Vol. 21, No. 15, pp. 3061-3066, November 2015 16. X. Li, “A Brief Review: Acoustic Emission Method for Tool Wear Monitoring during Turning,” International Journal of Machine Tools and Manufacture, Vol. 42, No. 2, pp. 157-165, January 2002 17. N. R. Sakthivel, V. Sugumaran,B. B. Nair, “Comparison of Decision Tree-Fuzzy and Rough Set-Fuzzy Methods for Fault Categorization of Mono-Block Centrifugal Pump,” Mechanical Systems and Signal Processing, Vol. 24, No. 6, pp. 1887-1906, February 2010 18. E. Jantunen, “A Summary of Methods Applied to Tool Condition Monitoring in Drilling,” International Journal of Machine Tools and Manufacture, Vol. 42, No. 9, pp. 997-1010, July 2002 19. A. G. Rehorn, J. Jiang,P. E. Orban, “State-of-the-Art in Methods and Results in Tool Condition Monitoring: a Review,” The International Journal of Advanced Manufacturing Technology, Vol. 26, pp. 693-710, October 2005 20. Y. Sahin and A. R. Motorcu, “Surface Roughness Model for Machining Mild Steel with Coated Carbide Tool,” Material and Design, Vol. 26, No. 4, pp. 321-326, June 2005 21. W. Jiang, A. S. More, W. D. Brown,A. P. Malshe, “A CBN-TiN Composite Coating for Carbide Inserts: Coating Characterization and Its Application for Finish Hard Turning,” Surface and Coatings Technology, Vol. 201, No. 6, pp. 2443-2449, December 2006 22. F. M. Aneiro, R. T. Coelho,L. C. Brandao, “Turning Hardened Steel using Coated Carbide at High Cutting Speeds,” Journal of the Brazilian Society of Mechanical Sciences and Engineering, Vol. 30, No. 2, pp. 104-109, June 2008 23. R. Yigit, E. Celik,F. Findik, “Performance of Multilayer Coated Carbide Tools When Turning Cast Iron,” Turkish Journal of Engineering and Environmental Sciences, Vol. 33, No. 3, pp. 147-157, August 2009 24. M. S. Shewale, S. S. Mulik, S. P. Deshpande,A. D. Patange, “A Novel Health Monitoring System,” inProceedings of the 2nd International Conference on Data Engineering and Communication Technology, Advances in Intelligent Systems and Computing, pp. 461-468, Singapore, 2019 25. S. P. Nalavade, A. D. Patange, C. L. Prabhune,S. S. Mulik, “Development of 12 Channel Temperature Acquisition System for Heat Exchanger using MAX6675 and Arduino Interface,” inProceedings of the 1st International Conference on Innovative Design, Analysis and Development Practices in Aerospace and Automotive Engineering, pp. 119-125, Singapore, 2019 26. A. D.Patange and R. Jegadeeshwaran, “Milling Cutter Condition Monitoring using Machine Learning Approach,” inProceedings of the 1st International Conference on Mechanical Power Transmission, pp. 1-5, Chennai, India, July 2019 27. M. Ringnér, “What Is Principal Component Analysis?” Nature Biotechnology, Vol. 26, pp. 303-304, March 2008 28. R. Bro and A. K. Smilde, “Principal Component Analysis,” Analytical Methods, Vol. 6, No. 9, pp. 2812-2831, March 2014 29. B. Moore, “Principal Component Analysis in Linear Systems: Controllability, Observability, and Model Reduction,” IEEE Transactions on Automatic Control, Vol. 26, No. 1, pp. 17-32, February 1981 30. I. T.Jolliffe and J. Cadima, “Principal Component Analysis: A Review and Recent Developments,” Philosophical Transactions of Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 374, No. 2065, April 2016 31. M. Zieba, S. K. Tomczak,J. M. Tomczak, “Ensemble Boosted Trees with Synthetic Features Generation in Application to Bankruptcy Prediction,” Expert Systems with Applications, Vol. 58, pp. 93-101, October 2016 32. R. K. Agrawal, F. Muchahary,M. M. Tripathi, “Ensemble of Relevance Vector Machines and Boosted Trees for Electricity Price Forecasting,” Applied Energy, Vol. 250, pp. 540-548, September 2019 33. G. Martínez-Muñoz and A. Suárez, “Using Boosting to Prune Bagging Ensembles,” Pattern Recognition Letters, Vol. 28, No. 1, pp. 156-165, January 2007 34. Y. Freund and R. E. Schapire, “A Short Introduction to Boosting,” Journal of Japanese Society for Artificial Intelligence, Vol. 14, No. 5, pp. 771-780, September 1999 |