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

Relationship between dynamic fatigue crack propagation properties and viscoelasticity of natural rubber/silicone rubber composites

“…where A is the pre-exponential factor, and a reects the sensitivity of dc/dN to the change of G. 3 Furthermore, Persson and Brener have pointed out that the energy dissipation around the crack G(v) consists of two parts: 4 GðvÞ ¼ G 0 ½1 þ f ðv; TÞ ¼ G 0 þ G 0 f ðv; TÞ ¼ G 0 þ G 0 f ða T vÞ (3) As shown in Fig. 1, G 0 occurs in the innermost area of the crack tip, and it is a highly non-linear process, which includes forming of cavities, breaking of chemical bonds, pulling out of molecular chains and stringing; G 0 f(v, T) is the bulk dissipation of linear viscoelastic region in front of the crack tip, which makes the major contribution to G(v), where f(v, T) is a function of crack growth rate v and temperature T. And according to the time-temperature equivalence principle, T could be replaced by the Williams-Landel-Ferry (WLF) coefficient a T , 5 so that G(v) could be presented as the function of time (frequency) without temperature:…”

Impact of the occurrence probability of carbon black agglomerates on the measured and predicted durability of filled elastomers

“…The size of the agglomerate and the probability of its occurrence impact the predicted lifetime. [ 2–6 ] The computer tomography (CT) and fatigue tests on three types of dumbbells had clearly shown that the large agglomerates are less frequent in dumbbells with small volumes. [ 3, 5 ] As a result, dumbbells with small volumes have a longer lifetime than dumbbells with greater volumes.…”

“…The prediction concept is based on the distribution of a given number of agglomerates with a given probability of occurrence in the mesh of the FE model. [ 2–4 ] This approach is known as the Monte Carlo method. The size of the agglomerates and their probability of occurrence are determined from CT scans and adjusted by the Weibull function.…”

Effective strain range-based prediction and analysis of the fatigue life of pipette shaft seal ring

“…In addition, some scholars analyze the effects of material laws, load conditions and reunion distribution on elastomer life prediction. For example, Yaagoubi et al [12][13][14], according to the particle distribution and crack expansion characteristics in the sample, propose a method for predicting the life of filled elastomers based on particle distribution and J-integral evaluation. Furthermore, Yaagoubi et al [15][16][17][18] assessed tear energy through pants samples and predicted tear energy in pattern III for filled elastomers.…”