On the automatic real-time detection of chatter onset in turning
VOSNIAKOS George-Christopher, ANASTASIADIS Apostolos
Abstract. A low-cost device has been implemented in order to detect the onset of chatter in turning operations. This involves a microcontroller, which receives an acceleration signal from the workpiece being turned, converts it to digital form, processes it in real time, identifies specific features and monitors them also in real time. As a first step, Fast Fourier Transform of the acceleration signal for a reference tool path that involves no chatter identifies high energy peaks at particular frequencies, which depend on the eigen-frequencies of the dynamic system machine tool – cutting tool – workpiece. Then, at the onset of chatter a specific pattern of additional high energy peaks appears in the spectrum 0-2.5 kHz. Signal processing and comparison with the reference take less than 200 ms. Experiments on a CNC lathe proved that the approach is effective and transferable beyond longitudinal turning.
Keywords
Turning, Chatter, Fast Fourier Transform, Microcontroller, Monitoring
Published online 12/10/2024, 8 pages
Copyright © 2024 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: VOSNIAKOS George-Christopher, ANASTASIADIS Apostolos, On the automatic real-time detection of chatter onset in turning, Materials Research Proceedings, Vol. 46, pp 159-166, 2024
DOI: https://doi.org/10.21741/9781644903377-21
The article was published as article 21 of the book Innovative Manufacturing Engineering and Energy
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
References
[1] S. Katiyar, M. Jaiswal, R. Pratap Narain, S. Singh, Y. Shrivastava, A short review on investigation and suppression of tool chatter in turning operation, Mater Today Proc 51 (2021) 1206–1210. https://doi.org/10.1016/j.matpr.2021.07.208
[2] M. Eynian, Y. Altintas, Chatter Stability of General Turning Operations With Process Damping, J Manuf Sci Eng 131 (2009) 1–10. https://doi.org/10.1115/1.3159047
[3] G. Urbikain, D. Olvera, L.N.L. de Lacalle, A. Beranoagirre, Alex Elías-Zuñiga, Prediction methods and experimental techniques for chatter avoidance in turning systems: A review, Applied Sciences 9 (2019) 4718. https://doi.org/10.3390/app9214718
[4] C. Yue, H. Gao, X. Liu, S.Y. Liang, L. Wang, A review of chatter vibration research in milling, Chinese Journal of Aeronautics 32 (2019) 215–242. https://doi.org/10.1016/j.cja.2018.11.007
[5] A.H. Tian, C.B. Fu, X.Y. Su, H.T. Yau, Lathe tool chatter vibration diagnostic using general regression neural network based on Chua’s circuit and fractional-order Lorenz master/slave chaotic system, Journal of Low Frequency Noise Vibration and Active Control 38 (2019) 953–966. https://doi.org/10.1177/1461348418815414
[6] M. Postel, D. Aslan, K. Wegener, Y. Altintas, Monitoring of vibrations and cutting forces with spindle mounted vibration sensors, CIRP Annals 68 (2019) 413–416. https://doi.org/10.1016/j.cirp.2019.03.019
[7] T.L. Schmitz, Chatter recognition by a statistical evaluation of the synchronously sampled audio signal, J Sound Vib 262 (2003) 721–730. https://doi.org/10.1016/S0022-460X(03)00119-6
[8] Z. Yao, D. Mei, Z. Chen, On-line chatter detection and identification based on wavelet and support vector machine, J Mater Process Technol 210 (2010) 713–719. https://doi.org/10.1016/j.jmatprotec.2009.11.007
[9] X. Huang, M. Hu, Y. Zhang, C. Lv, Probabilistic analysis of chatter stability in turning, International Journal of Advanced Manufacturing Technology 87 (2016) 3225–3232. https://doi.org/10.1007/s00170-016-8672-7
[10] V. Nguyen, S. Melkote, A. Deshamudre, M. Khanna, D. Walker, Comparison of on-line chatter detection algorithms in turning, in: International Symposium on Flexible Automation, ISFA 2016, Cleveland, USA, 2016: pp. 87–94. https://doi.org/10.1109/ISFA.2016.7790141
[11] S. Yamato, T. Hirano, Y. Yamada, R. Koike, Y. Kakinuma, Sensor-less on-line chatter detection in turning process based on phase monitoring using power factor theory, Precis Eng 51 (2018) 103–116. https://doi.org/10.1016/j.precisioneng.2017.07.017
[12] A. Jimenez Cortadi, I. Irigoien, F. Boto, B. Sierra, A. Suarez, D. Galar, A statistical data-based approach to instability detection and wear prediction in radial turning processes, Eksploatacja i Niezawodnosc 20 (2018) 405–412. https://doi.org/10.17531/ein.2018.3.8
[13] S. Kim, K. Ahmadi, Estimation of vibration stability in turning using operational modal analysis, Mech Syst Signal Process 130 (2019) 315–332. https://doi.org/10.1016/j.ymssp.2019.04.057
[14] C.L. Zhang, X. Yue, Y.T. Jiang, W. Zheng, A hybrid approach of ANN and HMM for cutting chatter monitoring, Adv Mat Res 97 (2010) 3225–3232. https://doi.org/10.4028/www.scientific.net/AMR.97-101.3225
[15] F.A. Khasawneh, E. Munch, J.A. Perea, Chatter Classification in Turning using Machine Learning and Topological Data Analysis, IFAC Papers Online 51 (2018) 195–200. https://doi.org/10.1016/j.ifacol.2018.07.222
[16] H. Cherukuri, E. Perez-Bernabeu, M.A. Selles, T.L. Schmitz, A neural network approach for chatter prediction in turning, Procedia Manuf 34 (2019) 885–892. https://doi.org/10.1016/j.promfg.2019.06.159
[17] S. Kumar, B. Singh, Prediction of tool chatter in turning using RSM and ANN, Mater Today Proc 5 (2018) 23806–23815. https://doi.org/10.1016/j.matpr.2018.10.172
[18] Y. Shrivastava, B. Singh, A. Sharma, Identification of Chatter in Turning Operation using WD and EMD, Mater Today Proc 5 (2018) 23917–23926. https://doi.org/10.1016/j.matpr.2018.10.184
