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Journal of Mechanical Science and Technology

, Volume 33, Issue 4, pp 1603–1613 | Cite as

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

  • Guofa Li
  • Shengxu Wang
  • Jialong HeEmail author
  • Kai Wu
  • Chuanyang Zhou
Article
  • 7 Downloads

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.

Keywords

Fatigue life prediction Load spectrum Machining center spindle Mixture Weibull distribution 

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References

  1. [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.CrossRefGoogle Scholar
  2. [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.CrossRefGoogle Scholar
  3. [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. [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.CrossRefGoogle Scholar
  5. [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.CrossRefGoogle Scholar
  6. [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.CrossRefGoogle Scholar
  7. [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.CrossRefGoogle Scholar
  8. [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.CrossRefGoogle Scholar
  9. [9]
    E. A. Elsayed, Overview of reliability testing, IEEE Transactions on Reliability, 61 (2) (2012) 1–11.CrossRefGoogle Scholar
  10. [10]
    S. Beretta et al., Structural integrity analysis of a tram-way: Load spectra and material damage, Wear, 258 (7-8) (2005) 1255–1264.CrossRefGoogle Scholar
  11. [11]
    X. He et al., The fleet life reliability analysis under the 90 severe load spectrum, Engineering Failure Analysis, 18 (1) (2011) 394–402.CrossRefGoogle Scholar
  12. [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.CrossRefGoogle Scholar
  13. [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.CrossRefzbMATHGoogle Scholar
  14. [14]
    Y. Wang et al., Load spectra of CNC machine tools, Quality & Reliability Engineering International, 16 (3) (2000) 229–234.CrossRefGoogle Scholar
  15. [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.CrossRefGoogle Scholar
  16. [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.MathSciNetCrossRefzbMATHGoogle Scholar
  17. [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.CrossRefGoogle Scholar
  18. [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.CrossRefGoogle Scholar
  19. [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.CrossRefGoogle Scholar
  20. [20]
    P. Johannesson, Extrapolation of load histories and spectra, Fatigue & Fracture of Engineering Materials & Structures, 29 (3) (2006) 209–217.CrossRefGoogle Scholar
  21. [21]
    C. H. Zhang, Fourier methods for estimating mixing densities and distributions, Annals of Statistics, 18 (2) (1990) 806–831.MathSciNetCrossRefzbMATHGoogle Scholar
  22. [22]
    J. Li et al., Fatigue life analysis and experimental verification of coronary stent, Heart and Vessels, 25 (4) (2010) 333–337.CrossRefGoogle Scholar
  23. [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. [24]
    D. W. Scott, Sturges’ rule, Wiley Interdisciplinary Reviews Computational Statistics, 1 (3) (2009) 303–306.CrossRefGoogle Scholar
  25. [25]
    J. H. G. Ender, On compressive sensing applied to radar, Signal Processing, 90 (5) (2010) 1402–1414.CrossRefzbMATHGoogle Scholar
  26. [26]
    J. Schijve, Fatigue of Structures and Materials, Springer Science & Business Media (2001) 144–172.Google Scholar
  27. [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.CrossRefGoogle Scholar
  28. [28]
    J. E. Shigley, Shigley’s Mechanical Engineering Design, Tata McGraw-Hill Education (2011).Google Scholar
  29. [29]
    W. D. Pilkey and D. F. Pilkey, Peterson’s Stress Concentration Factors, 3rd Ed., New York: Wiley (2008) 38.Google Scholar
  30. [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.CrossRefGoogle Scholar
  31. [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.CrossRefGoogle Scholar
  32. [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.CrossRefGoogle Scholar

Copyright information

© KSME & Springer 2019

Authors and Affiliations

  • Guofa Li
    • 1
  • Shengxu Wang
    • 1
  • Jialong He
    • 1
    • 2
    Email author
  • Kai Wu
    • 3
  • Chuanyang Zhou
    • 1
  1. 1.School of Mechanical and Aerospace EngineeringJilin UniversityChangchun, JilinChina
  2. 2.College of Computer Science and TechnologyJilin UniversityChangchun, JilinChina
  3. 3.Beijing Hangxing Machinery Manufacture Limited CorporationBeijingChina

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