Skip to main content
SpringerLink
Log in

Compilation of load spectrum of machining center spindle and application in fatigue life prediction

  • Published:
Journal of Mechanical Science and Technology Aims and scope Submit manuscript

Abstract

The load spectrum of machining center (MC) is the data basis for fatigue life prediction. A novel compiling method of dynamic cutting load spectrum of MC spindle is proposed, and then applied to the fatigue life prediction. Typical process parameters were determined based on the data collected in the user field by establishing the characteristic load distribution, and dynamic cutting load was measured using the load test platform. Mean-frequency and amplitude-frequency matrices of the load were obtained by the rainflow counting method, and mixture Weibull distribution (MWD) was used to establish the mean and amplitude distribution. Thus, the two-dimensional dynamic cutting load spectrum of spindle was compiled. The eight-level program load spectrum was established, and then applied to the spindle fatigue life prediction. The accuracy of load spectrum is improved because of the MWD, instead of single distribution, and the complete load spectrum compilation process also improves the life prediction accuracy.

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

Similar content being viewed by others

References

  1. M. Gagnon et al., Influence of load spectrum assumptions on the expected reliability of hydroelectric turbines: A case study, Structural Safety, 50 (5) (2014) 1–8.

    Article  Google Scholar 

  2. J. J. Xiong and R. A. Shenoi, A reliability-based data treatment system for actual load history, Fatigue & Fracture of Engineering Materials & Structures, 28 (10) (2005) 875–889.

    Article  Google Scholar 

  3. J. Wang, Y. Li and Z. Jiang, Non-parametric load extrapolation based on load extension for semi-axle of wheel loader, Advances in Mechanical Engineering, 9 (3) (2017) 1687 8140 1769 0073.

    Google Scholar 

  4. J. Wang et al., Establishment method of a mixture model and its practical application for transmission gears in an engineering vehicle, Chinese J. of Mechanical Engineering, 25 (5) (2012) 1001–1010.

    Article  Google Scholar 

  5. J. H. Kim, S. B. Lee and S. G. Hong, Fatigue crack growth behavior of Al7050-T7451 attachment lugs under flight spectrum variation, Theoretical & Applied Fracture Mechanics, 40 (2) (2003) 135–144.

    Article  Google Scholar 

  6. M. M. Topaç, S. Ercan and N. S. Kuralay, Fatigue life prediction of a heavy vehicle steel wheel under radial loads by using finite element analysis, Engineering Failure Analysis, 20 (3) (2012) 67–79.

    Article  Google Scholar 

  7. J. A. Epaarachchi and P. D. Clausen, The development of a fatigue loading spectrum for small wind turbine blades, J. of Wind Engineering & Industrial Aerodynamics, 94 (4) (2006) 207–223.

    Article  Google Scholar 

  8. S. Jeyakumar and T. Ramachandran, Prediction of cutting force, tool wear and surface roughness of Al6061/SiC composite for end milling operations using RSM, J. of Mechanical Science and Technology, 27 (9) (2013) 2813–2822.

    Article  Google Scholar 

  9. E. A. Elsayed, Overview of reliability testing, IEEE Transactions on Reliability, 61 (2) (2012) 1–11.

    Article  Google Scholar 

  10. S. Beretta et al., Structural integrity analysis of a tram-way: Load spectra and material damage, Wear, 258 (7-8) (2005) 1255–1264.

    Article  Google Scholar 

  11. X. He et al., The fleet life reliability analysis under the 90 severe load spectrum, Engineering Failure Analysis, 18 (1) (2011) 394–402.

    Article  Google Scholar 

  12. C. Zhu et al., Dynamic analysis of the drive train of a wind turbine based upon the measured load spectrum, J. of Mechanical Science and Technology, 28 (6) (2014) 2033–2040.

    Article  Google Scholar 

  13. P. Heuler and H. Klätschke, Generation and use of standardised load spectra and load-time histories, International Journal of Fatigue, 27 (8) (2005) 974–990.

    Article  MATH  Google Scholar 

  14. Y. Wang et al., Load spectra of CNC machine tools, Quality & Reliability Engineering International, 16 (3) (2000) 229–234.

    Article  Google Scholar 

  15. K. J. Karisallen and J. Morrison, A simple load-drop equation for estimating crack extension during fracture toughness tests, Engineering Fracture Mechanics, 47 (4) (1994) 583–589.

    Article  Google Scholar 

  16. B. Singh et al., A generalized log-normal distribution and its goodness of fit to censored data, Computational Statistics, 27 (1) (2012) 51–67.

    Article  MathSciNet  MATH  Google Scholar 

  17. M. Nagode and M. Fajdiga, A general multi-modal probability density function suitable for the rainflow ranges of stationary random processes, International J. of Fatigue, 20 (3) (1998) 211–223.

    Article  Google Scholar 

  18. Z. Yang et al., Bayesian method to solve the early failure period of numerical control machine tool, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 232 (9) (2018) 1677–1686.

    Article  Google Scholar 

  19. D. H. Timm, S. M. Tisdale and R. E. Turochy, Axle load spectra characterization by mixed distribution modeling, J. of Transportation Engineering, 131 (2) (2005) 83–88.

    Article  Google Scholar 

  20. P. Johannesson, Extrapolation of load histories and spectra, Fatigue & Fracture of Engineering Materials & Structures, 29 (3) (2006) 209–217.

    Article  Google Scholar 

  21. C. H. Zhang, Fourier methods for estimating mixing densities and distributions, Annals of Statistics, 18 (2) (1990) 806–831.

    Article  MathSciNet  MATH  Google Scholar 

  22. J. Li et al., Fatigue life analysis and experimental verification of coronary stent, Heart and Vessels, 25 (4) (2010) 333–337.

    Article  Google Scholar 

  23. J. Yu et al., Development of a program-loading spectrum for the accelerated durability test of lower control arm, J. of Testing and Evaluation, 44 (3) (2015) 1307–1318.

    Google Scholar 

  24. D. W. Scott, Sturges’ rule, Wiley Interdisciplinary Reviews Computational Statistics, 1 (3) (2009) 303–306.

    Article  Google Scholar 

  25. J. H. G. Ender, On compressive sensing applied to radar, Signal Processing, 90 (5) (2010) 1402–1414.

    Article  MATH  Google Scholar 

  26. J. Schijve, Fatigue of Structures and Materials, Springer Science & Business Media (2001) 144–172.

    Google Scholar 

  27. M. M. Topaç, H. Günal and N. S. Kuralay, Fatigue failure prediction of a rear axle housing prototype by using finite element analysis, Engineering Failure Analysis, 16 (5) (2009) 1474–1482.

    Article  Google Scholar 

  28. J. E. Shigley, Shigley’s Mechanical Engineering Design, Tata McGraw-Hill Education (2011).

    Google Scholar 

  29. W. D. Pilkey and D. F. Pilkey, Peterson’s Stress Concentration Factors, 3rd Ed., New York: Wiley (2008) 38.

    Google Scholar 

  30. D. H. Hwang and S. S. Cho, Mean stress effects in fretting fatigue life estimation method using fatigue damage gradient correction factor, J. of Mechanical Science and Technology, 31 (9) (2017) 4195–4202.

    Article  Google Scholar 

  31. H. Taghizadeh, T. N. Chakherlou and A. B. Aghdam, Prediction of fatigue life in cold expanded Al-alloy 2024-T3 plates used in double shear lap joints, J. of Mechanical Science and Technology, 27 (5) (2013) 1415–1425.

    Article  Google Scholar 

  32. P. Lipinski, A. Barbas and A. S. Bonnet, Fatigue behavior of thin-walled grade 2 titanium samples processed by selective laser melting, Application to life prediction of porous titanium implants, J. of the Mechanical Behavior of Biomedical Materials, 28 (4) (2013) 274–290.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Mechanical and Aerospace Engineering, Jilin University, Changchun, Jilin, 130022, China

    Guofa Li, Shengxu Wang, Jialong He & Chuanyang Zhou

  2. College of Computer Science and Technology, Jilin University, Changchun, Jilin, 130022, China

    Jialong He

  3. Beijing Hangxing Machinery Manufacture Limited Corporation, Beijing, 100013, China

    Kai Wu

Authors
  1. Guofa Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shengxu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jialong He
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kai Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chuanyang Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jialong He.

Additional information

Recommended by Associate Editor Yongho Jeon

Guofa Li received the Ph.D. in Mechanical Engineering from Jilin University Changchun, China (2002). He is a Professor in the School of Mechanical Science and Engineering, Jilin University. His research interests include kinetic analysis of high-speed press, load spectrum and reliability modeling of CNC machine tools.

Shengxu Wang is a doctoral candidate in Mechanical Science and Engineering, Jilin University, Changchun, China. His research interest are load spectrum and reliability technology of CNC equipment.

Jialong He received the Ph.D. in Mechanical Engineering from Jilin University, Changchun, China. He is a Lecturer in the School of Mechanical Science and Engineering, Jilin University. He focuses on load spectrum, reliability technology of CNC equipment, and industrial big data and intelligent manufacturing.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, G., Wang, S., He, J. et al. Compilation of load spectrum of machining center spindle and application in fatigue life prediction. J Mech Sci Technol 33, 1603–1613 (2019). https://doi.org/10.1007/s12206-019-0312-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12206-019-0312-3

Keywords

Search

Navigation