No abstract

Fatigue failure of rubber‐like materials has been often previously modeled with classical power‐law approaches, however with the new generation of physics‐based fatigue models, choosing the proper model depends on the material, loading condition and the computational cost users can afford. In view of the high number of validated fatigue models, it is challenging for engineers to choose a reliable fatigue model for a specific application. In service condition, reliability of elastomeric components is influenced by a variety of factors, ranging from environmental service condition to mechanical loads and compound properties. In sensitive applications, assuring the long‐term reliable performance of elastomers subjected to multiaxial variable loading is necessary to ensure the durability of the system. The purpose of this article is to review different stressors that contribute to the fatigue failure of elastomers and the associated modeling approaches used to assess their strengths and weaknesses. Additionally, this article summarizes the effect of thermal oxidation, moisture, hydrolysis, and radiation on long‐term aging of the fatigue properties of rubber, which has been studied over the last 50 years.

No abstract

Elastomers have been an active field of research during the past years for their extensive use in the industry. They are characterized by their unique hyper-elastic behavior, which can recover from a large amount of deformation. Constitutive models in rubber-like elasticity are essentially classified into 1) phenomenological and 2) physical-based models. The phenomenological models are based on direct experimental observation. Contrarily, Physical-based models come with a mathematical origin and employ physical hypotheses to describe a network of polymeric chains. As outlined in the literature, the accuracy of structure-based models has fallen behind the phenomenological models, despite their physical justification. The reasons for this unexpected fact lie in the simplified and superfluous assumptions that exist in the derivation of a closed-form expression for formulating material behavior. Moreover, physical-based models are calibrated through a nonlinear optimization schemes that, in essence, come with uncertainty. This uncertainty can be more affected by the complexity of the model. In this paper, we exploit an inverse approach to improve the accuracy of physically motivated models. To this end, We hold the virgin postulation in these models by considering the strain energy function in terms of two state variables that have been hypothesized to correspond to the kinetics of polymeric chains. Accordingly, we utilize B-spline interpolation to shift the unknown core functions of the strain energy potential to the macro-scale. Following this, we define a loss function based on the experimental data which is the macroscopic stress and use the simple linear least-square technique to determine these functions. In this way, we converge to a linear system of equations that is easy to solve and gives the best possible fit with data. Hence, In this numerical framework, we avoid unnecessary assumptions as far as possible and also nonlinear optimization for constitutive modeling. Finally, we compare the performance of the proposed methods with well-known models in the literature.