Lifetime prediction of filled elastomers based on particle distribution and the J-integral evaluation

Research output: Contribution to journalArticleResearchpeer review

Authors

  • Mohammed El Yaagoubi
  • Daniel Juhre
  • Jens Meier
  • Nils Kröger
  • Thomas Alshuth
  • Ulrich Giese

External Research Organisations

  • German Institute of Rubber Technology (DIK e.V.)
  • Otto-von-Guericke University Magdeburg
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Details

Original languageEnglish
Pages (from-to)341-354
Number of pages14
JournalInternational journal of fatigue
Volume112
Early online date22 Mar 2018
Publication statusPublished - Jul 2018
Externally publishedYes

Abstract

This work presents the lifetime prediction of elastomer component based on the particle distribution in the specimen, the crack growth properties and the appropriate crack criterion. A lifetime prediction using numerical simulation saves both cost and time and can be very helpful in the development phase of a product. On the basis of the obtained results, lifetime can be estimated and thus an appropriate measure such as geometry change or load adjustment can be adopted. The prediction is demonstrated using two kinds of elastomer samples: EPDM and NR under different load amplitudes. Lifetime prediction is accomplished by Monte Carlo simulation, which is based on the correlation between the value of J-integral and total energy density of the single-edge notched tension (SENT) sample. Due to the choice of an inelastic material law the J-integral is evaluated after establishment of stable load cycles as the stable value for a J-integral contour curve as a function of the contour curve distance from the crack tip. For lifetime prediction, a Python script was implemented for the commercial FEA system Abaqus as a postprocessing routine. The script is based on local lifetime evaluation, where evaluation is done element by element followed up by an accumulation. In addition, physical experiments are used for counting the particles in the materials, to characterise the crack growth and also to measure lifetime.

Keywords

    Fatigue of filled elastomers, J-integral, Lifetime prediction, Model of Rubber Phenomenology, Particle distribution

ASJC Scopus subject areas

Cite this

Lifetime prediction of filled elastomers based on particle distribution and the J-integral evaluation. / El Yaagoubi, Mohammed; Juhre, Daniel; Meier, Jens et al.
In: International journal of fatigue, Vol. 112, 07.2018, p. 341-354.

Research output: Contribution to journalArticleResearchpeer review

El Yaagoubi M, Juhre D, Meier J, Kröger N, Alshuth T, Giese U. Lifetime prediction of filled elastomers based on particle distribution and the J-integral evaluation. International journal of fatigue. 2018 Jul;112:341-354. Epub 2018 Mar 22. doi: 10.1016/j.ijfatigue.2018.03.024
El Yaagoubi, Mohammed ; Juhre, Daniel ; Meier, Jens et al. / Lifetime prediction of filled elastomers based on particle distribution and the J-integral evaluation. In: International journal of fatigue. 2018 ; Vol. 112. pp. 341-354.
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abstract = "This work presents the lifetime prediction of elastomer component based on the particle distribution in the specimen, the crack growth properties and the appropriate crack criterion. A lifetime prediction using numerical simulation saves both cost and time and can be very helpful in the development phase of a product. On the basis of the obtained results, lifetime can be estimated and thus an appropriate measure such as geometry change or load adjustment can be adopted. The prediction is demonstrated using two kinds of elastomer samples: EPDM and NR under different load amplitudes. Lifetime prediction is accomplished by Monte Carlo simulation, which is based on the correlation between the value of J-integral and total energy density of the single-edge notched tension (SENT) sample. Due to the choice of an inelastic material law the J-integral is evaluated after establishment of stable load cycles as the stable value for a J-integral contour curve as a function of the contour curve distance from the crack tip. For lifetime prediction, a Python script was implemented for the commercial FEA system Abaqus as a postprocessing routine. The script is based on local lifetime evaluation, where evaluation is done element by element followed up by an accumulation. In addition, physical experiments are used for counting the particles in the materials, to characterise the crack growth and also to measure lifetime.",
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