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On-line evolutionary identification technology for milling chatter of thin walled parts based on the incremental-sparse K-means and the online sequential extreme learning machine

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Abstract

In the milling process of thin-walled parts, chatter is very easy to occur, which has a very adverse impact on the surface quality and machining efficiency of the workpiece. In order to solve the problem of low accuracy of milling state identification caused by few initial samples and dynamic changes in the milling process, a hybrid online evolutionary chatter identification model combining unsupervised learning and supervised learning was proposed. First of all, aiming at the problem that traditional K-means algorithm is difficult to adapt to online dynamic clustering of milling chatter, an online incremental-sparse K-means algorithm (ISK-Means) was proposed, and the dynamic incremental-sparse strategy of K-means was designed. Second, aiming at the problem that the online sequential extreme learning machine (OS-ELM) algorithm directly adds its predicted samples to the training sample set during the incremental learning process, and the pseudo-samples in the training sample set would lead to the degradation of the OS-ELM model, a hybrid online evolutionary chatter identification model combining the ISK-means and the OS-ELM was proposed, and the online identification and evolution strategy was designed. Finally, the experimental results show that the ISK-means algorithm can greatly improve the clustering efficiency and is suitable for milling chatter online dynamic clustering. Meanwhile, compared with the existing model, the recognition accuracy of the hybrid online evolutionary chatter recognition model combined with ISK-means algorithm and OS-ELM algorithm is improved by 1.31%. This is of great significance for the online control of subsequent chatter.

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The raw/processed data required to reproduce these findings cannot be shared for the time being. Data will be made available upon request.

References

  1. Wang ZX, Liu XL, Li MY, Wang LH, Liang SY, Yu FH (2022) Multi-point contact stability prediction considering force-induced deformation effect in milling thin-walled parts. J Mech Eng 58(17):309–320. https://doi.org/10.3901/JME.2022.17.309

    Article  Google Scholar 

  2. Quintana G, Ciurana J (2011) Chatter in machining processes: a review. Int J Mach Tools Manuf 51(5):363–376. https://doi.org/10.1016/j.ijmachtools.2011.01.001

    Article  Google Scholar 

  3. Lamraoui M, Barakat M, Thomas M, Badaoui ME (2015) Chatter detection in milling machines by neural network classification and feature selection. J Vib Control 21(7):1251–1266. https://doi.org/10.1177/1077546313493919

    Article  Google Scholar 

  4. Wang L, Pan J, Shao Y, Zeng Q, Ding X (2021) Two new kurtosis-based similarity evaluation indicators for grinding chatter diagnosis under non-stationary working conditions. Measurement 176(2):109215. https://doi.org/10.1016/j.measurement.2021.109215

    Article  Google Scholar 

  5. Wang C, Zhang X, Chen X (2022) Real time FFT identification based time-varying chatter frequency mitigation in thin-wall workpiece milling. Int J Adv Manuf Technol 119(11–12):7403–7413. https://doi.org/10.1007/s00170-022-08755-7

    Article  Google Scholar 

  6. Perrelli M, Cosco F, Gagliardi F, Mundo D (2022) In-process chatter detection using signal analysis in frequency and time-frequency domain. Machines 10(1):24. https://doi.org/10.3390/machines10010024

    Article  Google Scholar 

  7. Wang Y, Zhang M, Tang X, Peng F, Yan R (2021) A kmap optimized VMD-SVM model for milling chatter detection with an industrial robot. J Intell Manuf 33(5):1483–1502. https://doi.org/10.1007/s10845-021-01736-9

    Article  Google Scholar 

  8. Yang K, Wang GF, Dong Y, Zhang QB, Sang LL (2019) Early chatter identification based on an optimized variational mode decomposition. Mech Syst Signal Process 115:238–254. https://doi.org/10.1016/j.ymssp.2018.05.052

    Article  Google Scholar 

  9. Liu XL, Wang ZX, Li MY, Yue CX, Liang SY, Wang LH (2021) Feature extraction of milling chatter based on optimized variational mode decomposition and multi-scale permutation entropy. Int J Adv Manuf Technol 114:2849–2862. https://doi.org/10.1007/s00170-021-07027-0

    Article  Google Scholar 

  10. Wang YQ, Bo QL, Liu HB, Hu L, Zhang H (2018) Mirror milling chatter identification using Q-factor and SVM. Int J Adv Manuf Technol 98(5–8):1163–1177. https://doi.org/10.1007/s00170-018-2318-x

    Article  Google Scholar 

  11. Zheng QZ, Chen GS, Jiao AL (2022) Chatter detection in milling process based on the combination of wavelet packet transform and PSO-SVM. Int J Adv Manuf Technol 120(1):1237–1251. https://doi.org/10.1007/s00170-022-08856-3

    Article  Google Scholar 

  12. Krishnan PS, Rameshkumar K, Krishnakumar P (2020) Hidden Markov modelling of high-speed milling (Hsm) process using acoustic emission (Ae) signature for predicting tool conditions. Advances in Materials and Manufacturing Engineering. Lecture Notes in Mechanical Engineering, Springer, Singapore, pp 5731–580. https://doi.org/10.1007/978-981-15-1307-7_65

  13. Han ZY, Jin HY, Fu HY (2016) Modeling of chatter recognition system in CNC milling based on ESPRIT and hidden Markov model. Comput Integr Manuf Syst 22(8):1938–1944. https://doi.org/10.13196/j.cims.2016.08.012

    Article  Google Scholar 

  14. Shrivastava Y, Singh B (2018) Possible way to diminish the effect of chatter in CNC turning based on EMD and ANN approaches. Arab J Sci Eng 43:4571–4591. https://doi.org/10.1007/s13369-017-2993-1

    Article  Google Scholar 

  15. Lamraoui M, Barakat M, Thomasm BME (2015) Chatter detection in milling machines by neural network classification and feature selection. J Vib Control 21(7):1251–1266. https://doi.org/10.1177/1077546313493919

    Article  Google Scholar 

  16. Jeong K, Seong Y, Jeon J, Moon S, Park J (2022) Chatter monitoring of machining center using head stock structural vibration analyzed with a 1D convolutional neural network. Sensors 22(14):5432. https://doi.org/10.3390/s22145432

    Article  Google Scholar 

  17. Liu YM, Ye GW, Zhao ZZ, Zhang Z (2023) Diagnosis model of RV reducer based on EEMD-PSO-ELM. Comput Integr Manuf Syst 29(01):224–235. https://doi.org/10.13196/j.cims.2023.01.019

    Article  Google Scholar 

  18. Ma J, Liang S, Du Z, Chen M (2021) Compound fault diagnosis of rolling bearing based on ALIF-KELM. Math Probl Eng. https://doi.org/10.1155/2021/2636302

    Article  Google Scholar 

  19. Dong W, Zhang S, Hu M, Zhang L, Liu H (2022) Intelligent fault diagnosis of wind turbine gearboxes based on refined generalized multi-scale state joint entropy and robust spectral feature selection. Nonlinear Dyn 107(3):2485–2517. https://doi.org/10.1007/s11071-021-07032-8

    Article  Google Scholar 

  20. Yang Y, Liao QF, Wang J, Wang Y (2022) Application of multi-objective particle swarm optimization based on short-term memory and K-means clustering in multi-modal multi-objective optimization. Eng Appl Artif Intell: Int J Intell Real-Time Autom 112:104866. https://doi.org/10.1016/j.engappai.2022.104866

  21. Bigdeli M, Abu-Siada A (2022) Clustering of transformer condition using frequency response analysis based on K-means and GOA. Electr Power Syst Res 202:107619. https://doi.org/10.1016/j.epsr.2021.107619

    Article  Google Scholar 

  22. Feng C, Xu WQ, Chen LW, Hu Y, Gao S (2015) Turbine blade fault detection based on feature extraction. 12th IEEE International Conference on Electronic Measurement & Instruments (ICEMI),. IEEE, Qingdao, pp 146–152. https://doi.org/10.1109/ICEMI.2015.7494240

  23. Peng Y, Wu T, Cao G, Huang S, Wu H, Kwok N, Peng Z (2017) A hybrid search-tree discriminant technique for multivariate wear debris classification. Wear 392–393:152–158. https://doi.org/10.1016/j.wear.2017.09.022

    Article  Google Scholar 

  24. Arthur D , Vassilvitskii S (2007) K-means++: the advantages of careful seeding. In Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms , New Orleans, Louisiana. Society for Industrial and Applied Mathematics. ACM, USA, pp 1027–1035. https://doi.org/10.1145/1283383.1283494

  25. Dong Z, Men Y, Li Z (2021) Chilling injury segmentation of tomato leaves based on fluorescence images and improved K-means++ clustering. Trans ASABE 64(1):13–22. https://doi.org/10.13031/trans.13212

    Article  Google Scholar 

  26. Mehdizadeh M, Macnish C, Khan RN, Bennamoun M (2011) Semi-supervised neighborhood preserving discriminate embedding: a semi-supervised subspace learning algorithm. Lect Notes Comput Sci 6494:199–212. https://doi.org/10.1007/978-3-642-19318-7_16

    Article  Google Scholar 

  27. Liang NY, Huang GB, Saratchandran P, Sundararajan N (2006) A fast and accurate online sequential learning algorithm for feedforward networks. Trans Neural Netw IEEE 17(6):1411–1423. https://doi.org/10.1109/TNN.2006.880583

    Article  Google Scholar 

  28. Yin G, Zhang YT, Li Z, Cheng LJ (2013) Fault diagnosis based on online sequential extreme learning machine. J Vib Meas Diagn 33(2):325–329. https://doi.org/10.16450/j.cnki.issn.1004-6801.2013.02.015

    Article  Google Scholar 

  29. Xiao SZ, Zhang F, Huang XZ (2022) Online thickness prediction of hot-rolled strip based on ISSA-OSELM. Int J Interact Des Manuf 16(3):1089–1098. https://doi.org/10.1007/s12008-021-00833-6

    Article  Google Scholar 

  30. Ma YP, Niu PF, Yan SS, Li GQ (2018) A modified online sequential extreme learning machine for building circulation fluidized bed boiler’s NOx emission model. Appl Math Comput 334:214–226. https://doi.org/10.1016/j.amc.2018.03.010

    Article  MathSciNet  Google Scholar 

  31. Gu ZY, Pang SW, Zhou WX, Li YC, Li QH (2022) An online data-driven LPV modeling method for turbo-shaft engines. Energies 15(4):1255. https://doi.org/10.3390/en15041255

    Article  Google Scholar 

  32. Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501. https://doi.org/10.1016/j.neucom.2005.12.126

    Article  Google Scholar 

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Funding

This work was financially supported by (1) International (regional) cooperation and exchange program of national Natural Science Foundation of China under Grant No. 51720105009, (2) National Natural Science Foundation of China under Grant No. 52175393, and (3) China Postdoctoral Science Foundation under Grant No. 2023MD734168.

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Authors and Affiliations

  1. Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin, 150080, China

    Zhixue Wang, Caixu Yue, Xianli Liu, Maoyue Li & Boyang Meng

  2. Harbin Vocational & Technical College, Harbin, 150076, China

    Zhixue Wang & Liying Yong

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  1. Zhixue Wang
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  2. Caixu Yue
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  3. Xianli Liu
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Contributions

Zhixue Wang has designed the experiments, analyzed and arranged data, and written the manuscript; Caixu Yue has conducted the experiments, analyzed and arranged data, and written the manuscript; Xianli Liu has organized the project and collected and analyzed data; Maoyue Li has conducted the experiments and collected and analyzed data; Boyang Meng and Liying Yong have reviewed the manuscript.

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Correspondence to Caixu Yue.

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Wang, Z., Yue, C., Liu, X. et al. On-line evolutionary identification technology for milling chatter of thin walled parts based on the incremental-sparse K-means and the online sequential extreme learning machine. Int J Adv Manuf Technol 128, 2001–2011 (2023). https://doi.org/10.1007/s00170-023-12030-8

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