Strength of Materials

, Volume 50, Issue 1, pp 151–156 | Cite as

Study on the Failure Mechanism of the Polymorphic Mixture for Remanufactured Machinery Parts

  • C. H. Liu
  • W. Y. Li
  • W. Z. Rao
  • K. He

The polymorphic mixture failure mode for multiple heterogeneity of remanufactured (RM) machinery parts makes it difficult to assess their lifetime. The Weibull distribution failure model of RM parts (substrate, coating layer, bonding surface and sudden failure) is constructed with failure time statistics of the parts in service, the latter is used to characterize the failure patterns of RM parts. In view of the multiple heterogeneity of RM parts, the Kaplan–Meier type decoupling method is used to analyze four sets of failure statistics, and each state of the Weibull failure function of the above parts is solved. It reveals the in-service failure mechanism of polymorphic mixtures for multiple heterogeneity of RM machinery parts. The validity and feasibility of the model are verified by the case study. Research results provide the theoretical basis for the design and preparation of a RM alloy powder and the improvement of RM technology. Moreover, the method for lifetime prediction and failure time evaluation of RM parts is proposed and validated.


remanufacturing Weibull distribution failure time uncertainty alloy powder 



This research was jointly supported by the Research Project (No. 2016jb08) and Support Program (No. SZXYQNL2017005) of Suzhou University, Key Project of Natural Science Research in Universities of Anhui Province of China (No. KJ2017A438), Chinese Postdoctoral Science Foundation (No. 2017M611574), and Humanities and Social Science Research Project of the Ministry of Education of China (No. 17YJC630082).


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • C. H. Liu
    • 1
    • 2
  • W. Y. Li
    • 1
  • W. Z. Rao
    • 2
  • K. He
    • 1
  1. 1.School of Mechanical and Electronic EngineeringSuzhou UniversitySuzhouChina
  2. 2.Sino-US Global Logistics InstituteShanghai Jiao Tong UniversityShanghaiChina

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