Chatter Identification in Milling of Titanium Alloy Using Machine Learning Approaches with Non-Linear Features of Cutting Force and Vibration Signatures
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Published
Mar 12, 2024
Viswajith S Nair
Rameshkumar K
Saravanamurugan S
https://orcid.org/0000-0002-0316-4022
Rameshkumar K
Saravanamurugan S
Abstract
The generation of chatter during machining operations is extremely detrimental to the cutting tool life and the surface quality of the workpiece. The present study aims to identify chatter conditions during the end milling of Ti6Al4V alloy. Experimental modal analysis is carried out, and stability lobe diagrams (SLDs) are developed to identify machining parameters under stable and chatter conditions. Experiments are conducted to acquire cutting force and vibration signatures corresponding to machining conditions selected from the SLD. Non-linear chatter features, such as Approximate Entropy, Holder Exponent, and Lyapunov Exponent extracted from the sensor signatures, are used to build Machine Learning (ML) models to identify chatter using Decision Trees (DTs), Support Vector Machines (SVMs) and DT-based Ensembles. A feature-level fusion approach is adopted to improve the classification performance of the ML models. The DT-based Adaboost model trained using dominant non-linear features classifies chatter with an accuracy of 96.8%. The non-linear features extracted from the sensor signatures offer a direct indication of the chatter and are found to be effective in identifying the machining chatter with good accuracy.
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Keywords
chatter, stability lobe, signal processing, non-linear feature, machine learning
References
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Chu, W. L., & Xie, M. J. (2021). Support-vector-machine-based sound and vibration signal processing for monitoring milling operations. Proceedings of the 2021 International Workshop on Modern Science and Technology, 2021, 29–35. https://doi.org/10.19000/0002000099
Chu, W. L., Xie, M. J., Chang, Q. W., & Yau, H. T. (2022). Research on the Recognition of Machining Conditions Based on Sound and Vibration Signals of a CNC Milling Machine. IEEE Sensors Journal, 22(7), 6364–6377. https://doi.org/10.1109/JSEN.2022.3150751
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/bf00994018
Dong, X., Yu, Z., Cao, W., Shi, Y., & Ma, Q. (2020). A survey on ensemble learning. Frontiers of Computer Science, 14(2), 241–258. https://doi.org/10.1007/S11704-019-8208-Z/METRICS
Echelard, A., & Lévy-Véhel, J. (2008). Wavelet denoising based on local regularity information. European Signal Processing Conference. 10.1006/acha.2000.0299
Freund, Y., & Schapire, R. E. (1996). Experiments with a New Boosting Algorithm. Proceedings of the 13th International Conference on Machine Learning, 148–156. https://doi.org/10.1.1.133.1040
G Welsch, R Boyer, E. C. (1994). Materials Properties Handbook: Titanium Alloys - ASM International.
Gredelj, S. (2021). The Methodology of Distinguish Between Random and Chaotic Machine Tool Oscillations. IOP Conference Series: Materials Science and Engineering, 1208(1), 012009. https://doi.org/10.1088/1757-899x/1208/1/012009
Guleria, V., Kumar, V., & Singh, P. K. (2022). Prediction of surface roughness in turning using vibration features selected by largest Lyapunov exponent based ICEEMDAN decomposition. Measurement: Journal of the International Measurement Confederation, 202, 111812. https://doi.org/10.1016/j.measurement.2022.111812
Gunatilaka, A. H., & 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. https://doi.org/10.1109/34.927459
Hall, D. L., & Llinas, J. (1997). An introduction to multisensor data fusion. Proceedings of the IEEE, 85(1), 6–23. https://doi.org/10.1109/5.554205
Hamida, Y., Naser, E., Karayel, D., & Kutlu, M. Ç. (2020). Investigation of Chaoticity of Vibrations in Machining. Journal of Smart Systems Research, 1(1), 18–24. https://dergipark.org.tr/en/pub/joinssr/issue/64433/979663
Hart, P., Stork, D., & Duda, R. (2000). Pattern classification (2nd ed). John Wiley & Sons: Hoboken.
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Kounta, C. A. K. A., Arnaud, L., Kamsu-Foguem, B., & Tangara, F. (2022). Review of AI-based methods for chatter detection in machining based on bibliometric analysis. The International Journal of Advanced Manufacturing Technology 2022 122:5, 122(5), 2161–2186. https://doi.org/10.1007/S00170-022-10059-9
Krishnakumar, P., Rameshkumar, K., & Ramachandran, K. I. (2018). Feature level fusion of vibration and acoustic emission signals in tool condition monitoring using machine learning classifiers. International Journal of Prognostics and Health Management, 9(1), 1–15. https://doi.org/10.36001/ijphm.2018.v9i1.2694
Landis, J. R., & Koch, G. G. (1977). The Measurement of Observer Agreement for Categorical Data. Biometrics, 33(1), 159. https://doi.org/10.2307/2529310
Liu, W., Wang, P., & You, Y. (2022). Ensemble-Based Semi-Supervised Learning for Milling Chatter Detection. Machines, 10(11), 1013. https://doi.org/10.3390/machines10111013
Livingston, E. H. (2004). Who was student and why do we care so much about his t-test? Journal of Surgical Research, 118(1), 58–65. https://doi.org/10.1016/j.jss.2004.02.003
Mohanraj, T., Yerchuru, J., Krishnan, H., Nithin Aravind, R. S., & Yameni, R. (2021). Development of tool condition monitoring system in end milling process using wavelet features and Hoelder’s exponent with machine learning algorithms. Measurement: Journal of the International Measurement Confederation, 173, 108671. https://doi.org/10.1016/j.measurement.2020.108671
Nair, V., Krishnaswamy, R., & S., S. (2022). Chatter identification in turning of difficult-to-machine materials using moving window standard deviation and decision tree algorithm. Journal of Ceramic Processing Research, 23(4), 503–510. https://doi.org/10.36410/jcpr.2022.23.4.503
Navarro-Devia, J. H., Chen, Y., Dao, D. V., & Li, H. (2023). Chatter detection in milling processes—a review on signal processing and condition classification. The International Journal of Advanced Manufacturing Technology, 125(9–10), 3943–3980. https://doi.org/10.1007/s00170-023-10969-2
Pérez-Canales, D., Álvarez-Ramírez, J., Jáuregui-Correa, J. C., Vela-Martínez, L., & Herrera-Ruiz, G. (2011). Identification of dynamic instabilities in machining process using the approximate entropy method. International Journal of Machine Tools and Manufacture, 51(6), 556–564. https://doi.org/10.1016/J.IJMACHTOOLS.2011.02.004
Pincus, S. M. (1991). Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences of the United States of America, 88(6), 2297–2301. https://doi.org/10.1073/pnas.88.6.2297
Pour, M. (2018). Determining surface roughness of machining process types using a hybrid algorithm based on time series analysis and wavelet transform. The International Journal of Advanced Manufacturing Technology 2018 97:5, 97(5), 2603–2619. https://doi.org/10.1007/S00170-018-2070-2
Rameshkumar, K., Sriram, R., Saimurugan, M., & Krishnakumar, P. (2022). Establishing Statistical Correlation between Sensor Signature Features and Lubricant Solid Particle Contamination in a Spur Gearbox. IEEE Access, 10, 106230–106247. https://doi.org/10.1109/ACCESS.2022.3210983
Rosenstein, M. T., Collins, J. J., & De Luca, C. J. (1993). A practical method for calculating largest Lyapunov exponents from small data sets. Physica D: Nonlinear Phenomena, 65(1–2), 117–134. https://doi.org/10.1016/0167-2789(93)90009-P
Salzberg, S. L. (1994). C4.5: Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993. Machine Learning, 16(3), 235–240. https://doi.org/10.1007/bf00993309
Schmitz, T. L., & Smith, K. S. (2009). Machining dynamics: Frequency response to improved productivity. In Machining Dynamics: Frequency Response to Improved Productivity. Springer US. https://doi.org/10.1007/978-0-387-09645-2
Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing and Management, 45(4), 427–437. https://doi.org/10.1016/j.ipm.2009.03.002
Tobias, S. A. (1961). Machine tool vibration research. International Journal of Machine Tool Design and Research, 1(1–2), 1–14. https://doi.org/10.1016/0020-7357(61)90040-3
Tran, M. Q., Liu, M. K., & Tran, Q. V. (2021). Analysis of Milling Chatter Vibration Based on Force Signal in Time Domain. Lecture Notes in Networks and Systems, 178, 192–199. https://doi.org/10.1007/978-3-030-64719-3_22
Wan, S., Li, X., Yin, Y., & Hong, J. (2021). Milling chatter detection by multi-feature fusion and Adaboost-SVM. Mechanical Systems and Signal Processing, 156, 107671. https://doi.org/10.1016/j.ymssp.2021.107671
Zhao, M., Yue, C., & Liu, X. (2023). Research on milling chatter identification of thin-walled parts based on incremental learning and multi-signal fusion. International Journal of Advanced Manufacturing Technology, 125(9–10), 3925–3941. https://doi.org/10.1007/s00170-023-10944-x
Zhou, C., Guo, K., & Sun, J. (2021). Sound singularity analysis for milling tool condition monitoring towards sustainable manufacturing. Mechanical Systems and Signal Processing, 157, 107738. https://doi.org/10.1016/J.YMSSP.2021.107738
Zhou, C., Guo, K., Sun, J., Yang, B., Liu, J., Song, G., Sun, C., & Jiang, Z. (2020). Tool condition monitoring in milling using a force singularity analysis approach. The International Journal of Advanced Manufacturing Technology 2019 107:3, 107(3), 1785–1792. https://doi.org/10.1007/S00170-019-04664-4
Zhou, C., Yang, B., Guo, K., Liu, J., Sun, J., Song, G., Zhu, S., Sun, C., & Jiang, Z. (2020). Vibration singularity analysis for milling tool condition monitoring. International Journal of Mechanical Sciences, 166, 105254. https://doi.org/10.1016/J.IJMECSCI.2019.105254
Altintas, Y., & Ber, A. (2001). Manufacturing Automation: Metal Cutting Mechanics, Machine Tool Vibrations, and CNC Design. Applied Mechanics Reviews, 54(5), B84–B84. https://doi.org/10.1115/1.1399383
Altintaş, Y., & Budak, E. (1995). Analytical Prediction of Stability Lobes in Milling. CIRP Annals, 44(1), 357–362. https://doi.org/10.1016/S0007-8506(07)62342-7
Caesarendra, W., Kosasih, B., Tieu, K., & Moodie, C. A. S. (2013). An application of nonlinear feature extraction - A case study for low speed slewing bearing condition monitoring and prognosis. 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics: Mechatronics for Human Wellbeing, AIM 2013, 1713–1718. https://doi.org/10.1109/AIM.2013.6584344
Chu, W. L., & Xie, M. J. (2021). Support-vector-machine-based sound and vibration signal processing for monitoring milling operations. Proceedings of the 2021 International Workshop on Modern Science and Technology, 2021, 29–35. https://doi.org/10.19000/0002000099
Chu, W. L., Xie, M. J., Chang, Q. W., & Yau, H. T. (2022). Research on the Recognition of Machining Conditions Based on Sound and Vibration Signals of a CNC Milling Machine. IEEE Sensors Journal, 22(7), 6364–6377. https://doi.org/10.1109/JSEN.2022.3150751
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/bf00994018
Dong, X., Yu, Z., Cao, W., Shi, Y., & Ma, Q. (2020). A survey on ensemble learning. Frontiers of Computer Science, 14(2), 241–258. https://doi.org/10.1007/S11704-019-8208-Z/METRICS
Echelard, A., & Lévy-Véhel, J. (2008). Wavelet denoising based on local regularity information. European Signal Processing Conference. 10.1006/acha.2000.0299
Freund, Y., & Schapire, R. E. (1996). Experiments with a New Boosting Algorithm. Proceedings of the 13th International Conference on Machine Learning, 148–156. https://doi.org/10.1.1.133.1040
G Welsch, R Boyer, E. C. (1994). Materials Properties Handbook: Titanium Alloys - ASM International.
Gredelj, S. (2021). The Methodology of Distinguish Between Random and Chaotic Machine Tool Oscillations. IOP Conference Series: Materials Science and Engineering, 1208(1), 012009. https://doi.org/10.1088/1757-899x/1208/1/012009
Guleria, V., Kumar, V., & Singh, P. K. (2022). Prediction of surface roughness in turning using vibration features selected by largest Lyapunov exponent based ICEEMDAN decomposition. Measurement: Journal of the International Measurement Confederation, 202, 111812. https://doi.org/10.1016/j.measurement.2022.111812
Gunatilaka, A. H., & 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. https://doi.org/10.1109/34.927459
Hall, D. L., & Llinas, J. (1997). An introduction to multisensor data fusion. Proceedings of the IEEE, 85(1), 6–23. https://doi.org/10.1109/5.554205
Hamida, Y., Naser, E., Karayel, D., & Kutlu, M. Ç. (2020). Investigation of Chaoticity of Vibrations in Machining. Journal of Smart Systems Research, 1(1), 18–24. https://dergipark.org.tr/en/pub/joinssr/issue/64433/979663
Hart, P., Stork, D., & Duda, R. (2000). Pattern classification (2nd ed). John Wiley & Sons: Hoboken.
International Standards Organization (ISO) (1996). Geometrical Product Specifications (GPS) - Surface texture: Profile method - Rules and procedures for the assessment of surface texture. In ISO, ISO4288:1996(e). vol. ISO/IEC Directives Part 2, I. O. f. S. (ISO), (p. 14). Genève, Switzerland: International Standards Organization.
Kounta, C. A. K. A., Arnaud, L., Kamsu-Foguem, B., & Tangara, F. (2022). Review of AI-based methods for chatter detection in machining based on bibliometric analysis. The International Journal of Advanced Manufacturing Technology 2022 122:5, 122(5), 2161–2186. https://doi.org/10.1007/S00170-022-10059-9
Krishnakumar, P., Rameshkumar, K., & Ramachandran, K. I. (2018). Feature level fusion of vibration and acoustic emission signals in tool condition monitoring using machine learning classifiers. International Journal of Prognostics and Health Management, 9(1), 1–15. https://doi.org/10.36001/ijphm.2018.v9i1.2694
Landis, J. R., & Koch, G. G. (1977). The Measurement of Observer Agreement for Categorical Data. Biometrics, 33(1), 159. https://doi.org/10.2307/2529310
Liu, W., Wang, P., & You, Y. (2022). Ensemble-Based Semi-Supervised Learning for Milling Chatter Detection. Machines, 10(11), 1013. https://doi.org/10.3390/machines10111013
Livingston, E. H. (2004). Who was student and why do we care so much about his t-test? Journal of Surgical Research, 118(1), 58–65. https://doi.org/10.1016/j.jss.2004.02.003
Mohanraj, T., Yerchuru, J., Krishnan, H., Nithin Aravind, R. S., & Yameni, R. (2021). Development of tool condition monitoring system in end milling process using wavelet features and Hoelder’s exponent with machine learning algorithms. Measurement: Journal of the International Measurement Confederation, 173, 108671. https://doi.org/10.1016/j.measurement.2020.108671
Nair, V., Krishnaswamy, R., & S., S. (2022). Chatter identification in turning of difficult-to-machine materials using moving window standard deviation and decision tree algorithm. Journal of Ceramic Processing Research, 23(4), 503–510. https://doi.org/10.36410/jcpr.2022.23.4.503
Navarro-Devia, J. H., Chen, Y., Dao, D. V., & Li, H. (2023). Chatter detection in milling processes—a review on signal processing and condition classification. The International Journal of Advanced Manufacturing Technology, 125(9–10), 3943–3980. https://doi.org/10.1007/s00170-023-10969-2
Pérez-Canales, D., Álvarez-Ramírez, J., Jáuregui-Correa, J. C., Vela-Martínez, L., & Herrera-Ruiz, G. (2011). Identification of dynamic instabilities in machining process using the approximate entropy method. International Journal of Machine Tools and Manufacture, 51(6), 556–564. https://doi.org/10.1016/J.IJMACHTOOLS.2011.02.004
Pincus, S. M. (1991). Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences of the United States of America, 88(6), 2297–2301. https://doi.org/10.1073/pnas.88.6.2297
Pour, M. (2018). Determining surface roughness of machining process types using a hybrid algorithm based on time series analysis and wavelet transform. The International Journal of Advanced Manufacturing Technology 2018 97:5, 97(5), 2603–2619. https://doi.org/10.1007/S00170-018-2070-2
Rameshkumar, K., Sriram, R., Saimurugan, M., & Krishnakumar, P. (2022). Establishing Statistical Correlation between Sensor Signature Features and Lubricant Solid Particle Contamination in a Spur Gearbox. IEEE Access, 10, 106230–106247. https://doi.org/10.1109/ACCESS.2022.3210983
Rosenstein, M. T., Collins, J. J., & De Luca, C. J. (1993). A practical method for calculating largest Lyapunov exponents from small data sets. Physica D: Nonlinear Phenomena, 65(1–2), 117–134. https://doi.org/10.1016/0167-2789(93)90009-P
Salzberg, S. L. (1994). C4.5: Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993. Machine Learning, 16(3), 235–240. https://doi.org/10.1007/bf00993309
Schmitz, T. L., & Smith, K. S. (2009). Machining dynamics: Frequency response to improved productivity. In Machining Dynamics: Frequency Response to Improved Productivity. Springer US. https://doi.org/10.1007/978-0-387-09645-2
Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing and Management, 45(4), 427–437. https://doi.org/10.1016/j.ipm.2009.03.002
Tobias, S. A. (1961). Machine tool vibration research. International Journal of Machine Tool Design and Research, 1(1–2), 1–14. https://doi.org/10.1016/0020-7357(61)90040-3
Tran, M. Q., Liu, M. K., & Tran, Q. V. (2021). Analysis of Milling Chatter Vibration Based on Force Signal in Time Domain. Lecture Notes in Networks and Systems, 178, 192–199. https://doi.org/10.1007/978-3-030-64719-3_22
Wan, S., Li, X., Yin, Y., & Hong, J. (2021). Milling chatter detection by multi-feature fusion and Adaboost-SVM. Mechanical Systems and Signal Processing, 156, 107671. https://doi.org/10.1016/j.ymssp.2021.107671
Zhao, M., Yue, C., & Liu, X. (2023). Research on milling chatter identification of thin-walled parts based on incremental learning and multi-signal fusion. International Journal of Advanced Manufacturing Technology, 125(9–10), 3925–3941. https://doi.org/10.1007/s00170-023-10944-x
Zhou, C., Guo, K., & Sun, J. (2021). Sound singularity analysis for milling tool condition monitoring towards sustainable manufacturing. Mechanical Systems and Signal Processing, 157, 107738. https://doi.org/10.1016/J.YMSSP.2021.107738
Zhou, C., Guo, K., Sun, J., Yang, B., Liu, J., Song, G., Sun, C., & Jiang, Z. (2020). Tool condition monitoring in milling using a force singularity analysis approach. The International Journal of Advanced Manufacturing Technology 2019 107:3, 107(3), 1785–1792. https://doi.org/10.1007/S00170-019-04664-4
Zhou, C., Yang, B., Guo, K., Liu, J., Sun, J., Song, G., Zhu, S., Sun, C., & Jiang, Z. (2020). Vibration singularity analysis for milling tool condition monitoring. International Journal of Mechanical Sciences, 166, 105254. https://doi.org/10.1016/J.IJMECSCI.2019.105254
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