Skip to main content
SpringerLink
Log in

A dynamic temperature condition monitoring method by vibration signal in grinding process

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

Abstract

The dynamic temperature condition during grinding processing is one of the key factors for the surface quality, such as micro structuring change and softened deformation on the workpiece’s surface. Many researchers have studied the grinding temperature field. However, few studies focus on the correlation between the temperature and vibration signal. In this paper, the correlation between the temperature and vibration signal was discussed; then, a novel method to monitor the grinding temperature by the vibration signal analysis was proposed. A simplified damped spring-mass model, moving heat flux model, was developed, based on which simulation study on the generative vibration and temperature field under dry grinding was performed. A dry grinding experiment on AISI1045 was conducted, and relative error of the experimental temperature and simulation was 7.2%. The short-time Fourier transform (STFT) and the principal component analyses (PCA) are used for vibration signal feature extraction. Then, the correlation coefficient between the grinding temperature and the vibration signal under different cutting depths was 0.877, and under different feeding speeds was 0.917, demonstrating the consistency. The experimental result conforms to the theoretical analysis, and it indicates the feasibility of this method.

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
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Data availability

All data and models generated or used during the study appear in the submitted article.

Code availability

Not applicable

References

  1. Takashima Y, Kawabata T, Yamada S, Minami F (2017) Observation of micro-cracks beneath fracture surface during dynamic crack propagation. Theor Appl Fract Mech 92:S0167844217302124

    Article  Google Scholar 

  2. Deng Y, Xiu S (2017) Research on microstructure evolution of austenitization in grinding hardening by cellular automata simulation and experiment. Int J Adv Manuf Technol 93:2599–2612

    Article  Google Scholar 

  3. Azarhoushang B, Daneshi A, Lee D (2017) Evaluation of thermal damages and residual stresses in dry grinding by structured wheels. J Clean Prod 142:1922–1930

    Article  Google Scholar 

  4. Xu X, Malkin S (2001) Comparison of Methods to Measure Grinding Temperatures. J Manuf Sci Eng 123:191–195

    Article  Google Scholar 

  5. Zhu D, Li B, Ding H (2013) An improved grinding temperature model considering grain geometry and distribution. Int J Adv Manuf Technol 67:1393–1406

    Article  Google Scholar 

  6. Yao C, Wang T, Wei X, Huang X, Ren J (2014) Experimental study on grinding force and grinding temperature of Aermet 100 steel in surface grinding. J Mater Process Technol 214:2191–2199

    Article  CAS  Google Scholar 

  7. Chen ZZ, Xu JH, Ding WF, Ma CY, Fu YC (2015) Grinding temperature during high-efficiency grinding Inconel 718 using porous CBN wheel with multilayer defined grain distribution. Int J Adv Manuf Technol 77:165–172

    Article  Google Scholar 

  8. Liu D, Gang W, Nie Z, Rong YK (2016) An in-situ infrared temperature-measurement method with back focusing on surface for creep-feed grinding. Measurement 94:645–652

    Article  Google Scholar 

  9. Hwang J, Kompella S, Chandrasekar S, Farris TN (2003) Measurement of Temperature Field in Surface Grinding Using Infra-Red (IR) Imaging System. J Tribol 125

  10. Li B, Li C, Zhang Y, Wang Y, Yang M, Jia D et al (2017) Numerical and experimental research on the grinding temperature of minimum quantity lubrication cooling of different workpiece materials using vegetable oil-based nanofluids. Int J Adv Manuf Technol 93:1971–1988

    Article  Google Scholar 

  11. Marinescu I, Rowe B, Dimitrov B, Inasaki I (2004) Tribology of Abrasive Machining Processes. J Manuf Sci Eng 126:vii–x

    Article  Google Scholar 

  12. Parente MPL, Natal Jorge RM, Aguiar Vieira A, Monteiro Baptista A (2012) Experimental and numerical study of the temperature field during creep feed grinding. Int J Adv Manuf Technol 61:127–134

    Article  Google Scholar 

  13. Yin G, Gong Y, Li Y, Wang F (2018) Investigation of the grinding temperature and subsurface quality of a novel point grinding wheel. Int J Adv Manuf Technol 97:1565–1581

    Article  Google Scholar 

  14. Liu C, Ding W, Li Z, Yang C (2017) Prediction of high-speed grinding temperature of titanium matrix composites using BP neural network based on PSO algorithm. Int J Adv Manuf Technol 89:2277–2285

    Article  Google Scholar 

  15. Yang M, Li C, Luo L, Li R, Long Y (2021) Predictive model of convective heat transfer coefficient in bone micro-grinding using nanofluid aerosol cooling. Int Commun Heat Mass Transf 125:105317

    Article  CAS  Google Scholar 

  16. Yang M, Li C, Zhang Y, Wang Y, Li B, Jia D et al (2017) Research on microscale skull grinding temperature field under different cooling conditions. Appl Therm Eng 126:525–537

    Article  Google Scholar 

  17. Cao Y, Ding W, Zhao B, Wen X, Li S, Wang J (2022) Effect of intermittent cutting behavior on the ultrasonic vibration-assisted grinding performance of Inconel718 nickel-based superalloy. Precis Eng 78:248–260

    Article  Google Scholar 

  18. Cao Y, Yin J, Ding W, Xu J (2021) Alumina abrasive wheel wear in ultrasonic vibration-assisted creep-feed grinding of Inconel 718 nickel-based superalloy. J Mater Process Technol 297:117241

    Article  CAS  Google Scholar 

  19. Cao Y, Zhu Y, Nan Li H, Wang C, Su H, Yin Z et al (2020) Development and performance of a novel ultrasonic vibration plate sonotrode for grinding. J Manuf Process 57:174–186

    Article  Google Scholar 

  20. Miao Q, Ding WF, Xu JH, Cao LJ, Wang HC, Yin Z et al (2021) Creep feed grinding induced gradient microstructures in the superficial layer of turbine blade root of single crystal nickel-based superalloy. Int J Extreme Manuf 3

  21. Cao Y, Zhu Y, Ding W, Qiu Y, Wang L, Xu J (2022) Vibration coupling effects and machining behavior of ultrasonic vibration plate device for creep-feed grinding of Inconel 718 nickel-based superalloy. Chin J Aeronaut 35:332–345

    Article  Google Scholar 

  22. 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

    Article  Google Scholar 

  23. Liu Z, Payre G (2007) Stability analysis of doubly regenerative cylindrical grinding process. J Sound Vib 301:950–962

    Article  Google Scholar 

  24. Siddhpura M, Paurobally R (2012) A review of chatter vibration research in turning. Int J Mach Tools Manuf 60:27–47

    Article  Google Scholar 

  25. Heitz T, He N, Chen N, Zhao G, Li L (2022) A review on dynamics in micro-milling. Int J Adv Manuf Technol 122:3467–3491

    Article  Google Scholar 

  26. Balkrishna, Rao and Yung and Shin (1999) A comprehensive dynamic cutting force model for chatter prediction in turning. Int J Mach Tool Manu 39 

  27. Khasawneh FA, Munch E (2016) Chatter detection in turning using persistent homology. Mech Syst Signal Process 70

  28. He D, Ni Z, Wang X (2022) Online grinding chatter detection based on minimum entropy deconvolution and autocorrelation function. Int J Adv Manuf Technol 120:6175–6185

    Article  Google Scholar 

  29. Liu M, Li C, Zhang Y, Yang M, Gao T, Cui X et al (2023) Analysis of grain tribology and improved grinding temperature model based on discrete heat source. Tribol Int 180:108196

    Article  Google Scholar 

  30. Deng Y, Xiu S, Shi X, Sun C, Wang Y (2017) Study on the effect mechanisms of pre-stress on residual stress and surface roughness in PSHG. Int J Adv Manuf Technol 88:3243–3256

    Article  Google Scholar 

  31. Mishra VK, Salonitis K (2013) Empirical Estimation of Grinding Specific Forces and Energy Based on a Modified Werner Grinding Model. Procedia CIRP 8:287–292

    Article  Google Scholar 

  32. Sun C, Duan J, Lan D, Liu Z, Xiu S (2018) Prediction about ground hardening layers distribution on grinding chatter by contact stiffness. Arch Civ Mech Eng 18:1626–1642

    Article  Google Scholar 

  33. Huang X, Ren Y, Zheng B, Deng Z, Zhou Z (2016) Experiment research on grind-hardening of AISI5140 steel based on thermal compensation. J Mech Sci Technol 30:3819–3827

    Article  Google Scholar 

  34. Kong X, Xiu S, Sun C, Yao Y, Zou X, Zhao Y (2022) Study on the relevance of strengthened layer and vibration signal in grinding-strengthening process. Int J Adv Manuf Technol 121:7963–7982

    Article  Google Scholar 

  35. Yan Y, Xu J, Wang W (2012) Nonlinear chatter with large amplitude in a cylindrical plunge grinding process. Nonlinear Dyn 69:1781–1793

    Article  MathSciNet  Google Scholar 

Download references

Funding

This project is supported by the National Natural Science Foundation of China (Grant No. 52175383)

Author information

Authors and Affiliations

  1. School of Mechanical Engineering and Automation, Northeastern University, Heping Region Wenhua Road, Shenyang, 110819, China

    Xiangna Kong, Hong Yuan, Xiannan Zou, Yingbo Zhao & Shichao Xiu

  2. School of Mechanical Engineering, Liaoning Institute of Science and Technology, Xihu Region Xianghuai Road, Benxi, 117004, China

    Xiangna Kong

Authors
  1. Xiangna Kong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hong Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiannan Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yingbo Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shichao Xiu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Xiangna Kong and Shichao Xiu contributed to the conception of the study; Cong Sun and Hong Yuan designed the experimental plan; Xiannan Zou and Yingbo Zhao processed and analyzed the experimental data; Xiangna Kong wrote the original draft and Shichao Xiu supervised the project and reviewed and edited the article.

Corresponding author

Correspondence to Shichao Xiu.

Ethics declarations

Ethics approval

Not applicable

Consent to participate

Not applicable

Consent for publication

Not applicable

Competing interests

The authors declare no competing 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

Kong, X., Yuan, H., Zou, X. et al. A dynamic temperature condition monitoring method by vibration signal in grinding process. Int J Adv Manuf Technol 131, 2497–2507 (2024). https://doi.org/10.1007/s00170-023-11797-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-023-11797-0

Keywords

Search

Navigation