Skip to main content
SpringerLink
Log in

Online roundness prediction of grinding workpiece based on vibration signals and support vector machine

  • Application
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

This paper proposes an online prediction method for the roundness of grinding workpieces based on vibration signals. Vibration sensors are used to collect vibration signals during grinding, and wavelet packet denoising is used to preprocess original signals to obtain effective vibration signals. Then use time domain analysis and frequency domain analysis to extract features and normalize them to form feature vectors. The roundness of the finished workpiece is measured using a shape-measuring instrument and integrated with the feature vectors to generate a usable data set. The support vector machine (SVM) algorithm is implemented using A Library for Support Vector Machines (LIBSVM), and a prediction model is constructed. Use the data set to train the model and evaluate the accuracy of the model to verify the effectiveness of the model. The results show that the prediction accuracy of the prediction method can reach 92.86%, and it can better predict whether the roundness is qualified.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data availability

All the data are obtained by experiments and are authentic.

Code availability

The code we wrote can support the normal operation of the system.

References

  1. Kopac J, Krajnik P (2006) High-performance grinding—a review. J Mater Process Technol 175:278–284. https://doi.org/10.1016/j.jmatprotec.2005.04.010

    Article  Google Scholar 

  2. Lu Y, Liu C, Wang KI-K, Huang H, Xu X (2020) Digital twin-driven smart manufacturing: connotation, reference model, applications and research issues. Robot Comput-Integr Manuf 61:101837. https://doi.org/10.1016/j.rcim.2019.101837

    Article  Google Scholar 

  3. Brinksmeier E, TÖnshoff HK, Czenkusch C, Heinzel C (1998) Modelling and optimization of grinding processes. J Intell Manuf 9:303–314. https://doi.org/10.1023/A:1008908724050

    Article  Google Scholar 

  4. Brinksmeier E, Aurich JC, Govekar E, Heinzel C, Hoffmeister H-W, Klocke F, Peters J, Rentsch R, Stephenson DJ, Uhlmann E, Weinert K, Wittmann M (2006) Advances in modeling and simulation of grinding processes. CIRP Ann 55:667–696. https://doi.org/10.1016/j.cirp.2006.10.003

    Article  Google Scholar 

  5. Kwak J-S, Ha M-K (2004) Intelligent diagnostic technique of machining state for grinding. Int J Adv Manuf Technol 23:436–443. https://doi.org/10.1007/s00170-003-1899-0

    Article  Google Scholar 

  6. Lezanski P (2001) An intelligent system for grinding wheel condition monitoring. J Mater Process Technol 109:258–263. https://doi.org/10.1016/S0924-0136(00)00808-6

    Article  Google Scholar 

  7. Cheng C, Li J, Liu Y, Nie M, Wang W (2019) Deep convolutional neural network-based in-process tool condition monitoring in abrasive belt grinding. Comput Ind 106:1–13. https://doi.org/10.1016/j.compind.2018.12.002

    Article  Google Scholar 

  8. Yang Z, Yu Z (2012) Grinding wheel wear monitoring based on wavelet analysis and support vector machine. Int J Adv Manuf Technol 62:107–121. https://doi.org/10.1007/s00170-011-3797-1

    Article  Google Scholar 

  9. Hassui A, Diniz AE (2003) Correlating surface roughness and vibration on plunge cylindrical grinding of steel. Int J Mach Tools Manuf 43:855–862. https://doi.org/10.1016/S0890-6955(03)00049-X

    Article  Google Scholar 

  10. Yao Z, Mei D, Chen Z (2010) On-line chatter detection and identification based on wavelet and support vector machine. J Mater Process Technol 210:713–719. https://doi.org/10.1016/j.jmatprotec.2009.11.007

    Article  Google Scholar 

  11. Thomazella R, Lopes WN, Aguiar PR, Alexandre FA, Fiocchi AA, Bianchi EC (2019) Digital signal processing for self-vibration monitoring in grinding: a new approach based on the time-frequency analysis of vibration signals. Measurement 145:71–83. https://doi.org/10.1016/j.measurement.2019.05.079

    Article  Google Scholar 

  12. Mahata S, Shakya P, Babu NR, Prakasam PK (2020) In-process characterization of surface finish in cylindrical grinding process using vibration and power signals. Procedia CIRP 88:335–340. https://doi.org/10.1016/j.procir.2020.05.058

    Article  Google Scholar 

  13. Mahata S, Shakya P, Babu NR (2021) A robust condition monitoring methodology for grinding wheel wear identification using Hilbert Huang transform. Precis Eng 70:77–91. https://doi.org/10.1016/j.precisioneng.2021.01.009

    Article  Google Scholar 

  14. Subrahmanya N, Shin YC (2008) Automated sensor selection and fusion for monitoring and diagnostics of plunge grinding. J Manuf Sci Eng 130 https://doi.org/10.1115/1.2927439

  15. Hassui A, Diniz AE, Oliveira JFG, Felipe J, Gomes JJF (1998) Experimental evaluation on grinding wheel wear through vibration and acoustic emission. Wear 217:7–14. https://doi.org/10.1016/S0043-1648(98)00166-5

    Article  Google Scholar 

  16. Yeh L-J, Lai G-J (1995) A study of the monitoring and suppression system for turning slender workpieces. Proc Inst Mech Eng Part B J Eng Manuf 209:227–236. https://doi.org/10.1243/PIME_PROC_1995_209_077_02

    Article  Google Scholar 

  17. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297. https://doi.org/10.1007/BF00994018

    Article  MATH  Google Scholar 

  18. Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2:121–167. https://doi.org/10.1023/A:1009715923555

    Article  Google Scholar 

  19. Ekici S (2009) Classification of power system disturbances using support vector machines. Expert Syst Appl 36:9859–9868. https://doi.org/10.1016/j.eswa.2009.02.002

    Article  Google Scholar 

  20. Widodo A, Yang B-S (2007) Support vector machine in machine condition monitoring and fault diagnosis. Mech Syst Signal Process 21:2560–2574. https://doi.org/10.1016/j.ymssp.2006.12.007

    Article  Google Scholar 

  21. Liakos KG, Busato P, Moshou D, Pearson S, Bochtis D (2018) Machine learning in agriculture: a review. Sensors 18:2674. https://doi.org/10.3390/s18082674

    Article  Google Scholar 

  22. Chang C-C, Lin C-J (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:27:1-27:27. https://doi.org/10.1145/1961189.1961199

    Article  Google Scholar 

Download references

Funding

This work was supported by the Key R&D Program of Zhejiang Province (2020C01033).

Author information

Authors and Affiliations

  1. State Key Laboratory of Fluid Power & Mechatronic Systems, College of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, People’s Republic of China

    Ning Chu, Weimin Kang, Xinhua Yao & Jianzhong Fu

  2. Digital Intelligence Innovation Center, Wanxiang Qianchao Co., Ltd., Hangzhou, 311215, People’s Republic of China

    Ning Chu

  3. Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province, College of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, People’s Republic of China

    Weimin Kang, Xinhua Yao & Jianzhong Fu

Authors
  1. Ning Chu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weimin Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xinhua Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jianzhong Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study. Chu Ning and Kang Weimin formulated the experimental plan, processed the data, and verified the prediction model. Kang Weimin wrote the first draft of the paper, Fu Jianzhong and Yao Xinhua determined the research direction and experimental purpose of the paper, and revised the paper. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Weimin Kang or Jianzhong Fu.

Ethics declarations

Ethics approval

Not applicable.

Consent to participate

All authors read and approved the final manuscript.

Consent for publication

All authors agree to publish in The International Journal of Advanced Manufacturing Technology.

Competing interests

The authors declare no interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chu, N., Kang, W., Yao, X. et al. Online roundness prediction of grinding workpiece based on vibration signals and support vector machine. Int J Adv Manuf Technol 126, 2733–2743 (2023). https://doi.org/10.1007/s00170-023-11206-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-023-11206-6

Keywords

Search

Navigation