Ascertaining higher MRR at chatter free milling using spline-based local mean decomposition and artificial neural network based hybrid approach

Document Type : Article

Authors

Department of Mechanical Engineering Jaypee University of Engineering and Technology, Guna (M.P.) 473226 India

Abstract

Chatter is a type of self-induced vibration that reflects fluctuations in both frequency and energy dispersion during the milling process, inevitably resulting in substandard part quality and diminished material removal rates. It is essential to employ a robust chatter detection method to anticipate its emergence in the early stages. This study introduces an efficient Product Function (PF) based multi-mode signal processing technique, specifically the spline-based local mean decomposition (SBLMD). This method is applied to decompose sound signals acquired through experimentation into a series of effective PF’s. Subsequently, selected PFs are employed to reconstruct a new chatter signal that is information-rich. Additionally, prediction models based on Artificial Neural Networks (ANN) are established to predict Chatter Indicator (CI) and Material Removal Rate (MRR) using three different activation algorithms: Tan Sigmoid (TANSIG), Log Sigmoid (LOGSIG), and Purely Linear (PURELIN). Statistical comparisons have been conducted in order to obtain the optimal activation algorithm and found out that data set trained with LOGSIG gives minimal error. Moreover, an optimal range of input parameters has been selected pertaining to minimum chatter and maximum MRR. Confirmation tests on the obtained set of parameters have been carried out in order to analyse and authenticate the proposed technique.

Keywords

Main Subjects

References

References: 
1. Muhammad, R., Ahmed, N., Maqsood, S., et al. "Influence of tool material on forces, temperature, and surface quality of Ti-15333 alloy in CT and UAT", Sci. Iran., 26(5), pp. 2805-2816 (2019). https://doi.org/10.24200/sci.2018.20692.
2. Tao, J., Qin, C., Xiao, D., et al. "Timely chatter identification for robotic drilling using a local maximum synchrosqueezing-based method", J. Intell. Manuf., 31(5), pp. 1243-1255 (2020). https://doi.org/10.1007/s10845-019-01509-5.
3. Tran, M.Q., Liu, M.K., and Elsisi, M. "Effective multisensor data fusion for chatter detection in milling process", ISA Trans., 125, pp. 514-527 (2022). https://doi.org/10.1016/j.isatra.2021.07.005
4. Cuka, B. and Kim, D.W. "Fuzzy logic based tool condition monitoring for end-milling", Robot. Comput. Integr. Manuf., 47(December), pp. 22-36 (2017). https://doi.org/10.1016/j.rcim.2016.12.009.
5. Mishra, R. and Singh, B. "SBLMD-ANN-MOPSObased hybrid approach for determining optimum parameter in CNC milling", Soft Comput., 27(11), pp. 7299-7320 (2023). https://doi.org/10.1007/s00500- 023-07944-0.
6. Daldal, N., Comert, Z., and Polat, K. "Automatic determination of digital modulation types with different noises using Convolutional Neural Network based on time-frequency information", Appl. Soft Comput. J., 86, p. 105834 (2020). https://doi.org/10.1016/j.asoc.2019.105834.
7. Tran, M.Q., Liu, M.K., and Tran, Q.V. "Milling chatter detection using scalogram and deep convolutional neural network", Int. J. Adv. Manuf. Technol., 107(3- 4), pp. 1505-1516 (2020). https://doi.org/10.1007/s00170-019-04807-7.
8. Noori, M., Wang, H., Altabey, W.A., et al. "A modified wavelet energy rate-based damage identification method for steel bridges", Sci. Iran., 25(6B), pp. 3210- 3230 (2018). https://doi.org/10.24200/sci.2018.20736.
9. Yoon, M.C. and Chin, D.H. "Cutting force monitoring in the endmilling operation for chatter detection", Proc. Inst. Mech. Eng. Part B J. Eng. Manuf., 219(6), pp. 455-465 (2005). https://doi.org/10.1243/095440505X32292.
10. Cai, K., Cao, W., Aarniovuori, L., et al. "Classification of power quality disturbances using wignerville distribution and deep convolutional neural networks", IEEE Access, 7, pp. 119099-119109 (2019). https://doi.org/10.1109/ACCESS.2019.2937193.
11. Shrivastava, Y. and Singh, B. "Estimation of stable cutting zone in turning based on empirical mode decomposition and statistical approach", J. Brazilian Soc. Mech. Sci. Eng., 40(2) (2018). https://doi.org/10.1007/s40430-018-0989-8.
12. Zhang, Q., Tu, X., Li, F., et al. "An effective chatter detection method in milling process using morphological empirical wavelet transform", IEEE Trans. Instrum. Meas., 69(8), pp. 5546-5555 (2020). https://doi.org/10.1109/TIM.2019.2958470.
13. Zhang, Z., Li, H., Meng, G., et al. "Chatter detection in milling process based on the energy entropy of VMD and WPD", Int. J. Mach. Tools Manuf., 108, pp. 106-112 (2016). https://doi.org/10.1016/j.ijmachtools.2016.06.002.
14. Liu, J., Wu, B., Wang, Y., et al. "An integrated condition-monitoring method for a milling process using reduced decomposition features", Meas. Sci. Technol., 28(8), p. 085101 (2017). https://doi.org/10.1088/1361-6501/aa6bcc.
15. Liu, Y., Wang, X., Lin, J., et al. "Correlation analysis of motor current and chatter vibration in grinding using complex continuous wavelet coherence", Meas. Sci. Technol., 27(11), p. 115106 (2016). https://doi.org/10.1088/0957-0233/27/11/115106.
16. Ji, Y., Wang, X., Liu, Z., et al. "Early milling chatter identification by improved empirical mode decomposition and multi-indicator synthetic evaluation", J. Sound Vib., 433, pp. 138-159 (2018). https://doi.org/10.1016/j.jsv.2018.07.019.
17. Li, Z., Chen, J., Zi, Y., et al. "Independenceoriented VMD to identify fault feature for wheel set bearing fault diagnosis of high speed locomotive", Mech. Syst. Signal Process., 85, pp. 512-529 (2017). https://doi.org/10.1016/j.ymssp.2016.08.042.
18. Smith, J.S. "The local mean decomposition and its application to EEG perception data", J. R. Soc. Interface, 2(5), pp. 443-454 (2005). https://doi.org/10.1098/rsif.2005.0058.
19. Mishra, R. and Singh, B. "Stability analysis in milling process using spline based local mean decomposition R. Mishra and B. Singh/Scientia Iranica, Transactions B: Mechanical Engineering 31 (2024) 1387-1401 1401(SBLMD) technique and statistical indicators", Meas. J. Int. Meas. Confed., 174, p. 108999 (2021). https://doi.org/10.1016/j.measurement.2021.108999.
20. Mishra, R. and Singh, B. "Extenuating chatter vibration in milling process using a new ensemble approach", J. Vib. Eng. Technol., 10(4), pp. 1235-1252 (2022). https://doi.org/10.1007/s42417-022-00440-z.
21. Vazirizade, S.M., Bakhshi, A., and Bahar, O. "Online nonlinear structural damage detection using Hilbert Huang transform and artificial neural networks", Sci. Iran., 26(3A), pp. 1266-1279 (2019). https://doi.org/10.24200/sci.2019.50657.1808.
22. Devillez, A. and Dudzinski, D. "Tool vibration detection with eddy current sensors in machining process and computation of stability lobes using fuzzy classifiers", Mech. Syst. Signal Process., 21(1), pp. 441-456 (2007). https://doi.org/10.1016/j.ymssp.2005.11.007.
23. Chen, G.S. and Zheng, Q.Z. "Online chatter detection of the end milling based on wavelet packet transform and support vector machine recursive feature elimination", Int. J. Adv. Manuf. Technol., 95(1-4), pp. 775- 784 (2018). https://doi.org/10.1007/s00170-017-1242- 9.
24. Mishra, R., Gupta, P., and Singh, B. "An intelligent approach to extract chatter and metal removal rate features impromptu from milling sound signal", Proc. Inst. Mech. Eng. Part E J. Process Mech. Eng. (2023). https://doi.org/10.1177/09544089231159465.
25. Mohanraj, T. and Tamilvanan, A. "Decision support system for tool condition monitoring in milling process using artificial neural network", J. Eng. Res., 10(4), p. 142-155 (2022). https://doi.org/10.36909/jer.9621.
26. Shaul Hameed, S., Muralidharan, V., and Ane, B.K. "Comparative analysis of fuzzy classifier and ANN with histogram features for defect detection and classification in planetary gearbox", Appl. Soft Comput., 106, p. 107306 (2021). https://doi.org/10.1016/j.asoc.2021.107306.
27. Yanis, M., Mohruni, A.S., Sharif, S., et al. "Application of RSM and ANN in predicting surface roughness for side milling process under environmentally friendly cutting  fluid", J. Phys. Conf. Ser., 1198(4), p. 042016 (2019). https://doi.org/10.1088/1742-6596/1198/4/042016.
28. Jena, S.P. and Parhi, D.R. "Fault detection in cracked structures under moving load through a recurrentneural- networks-based approach", Sci. Iran., 27(4), pp. 1886-1896 (2020). https://doi.org/10.24200/SCI.2019.50363.1657.
29. Salimiasl, A., Erdem, A., and Rafighi, M. "Applying a multi-sensor system to predict and simulate the tool wear using articial neural networks", Sci. Iran., 24(6), pp. 2864-2874 (2017). https://doi.org/10.24200/sci.2017.4247.
30. Altintas, Y. and Budak, E. "Analytical Prediction of Stability Lobes in Milling", CIRP Ann.-Manuf. Technol., 44(1), pp. 357-362 (1995). https://doi.org/10.1016/S0007-8506(07)62342-7.
Scientia Iranica
Volume 31, Issue 16
Transactions on Mechanical Engineering (B)
September and October 2024
Pages 1387-1401

Files

History

  • Receive Date: 08 September 2021
  • Revise Date: 26 November 2023
  • Accept Date: 24 January 2024

Share

How to cite

Statistics