Investigation on the static and cyclic anisotropic mechanical behavior of polychloroprene rubber (CR) reinforced with tungsten nano-particles. (August 2020) - Choi, Joeun; Quagliato, Luca; Shin, Junghoon; Kim, Naksoo
Highlights: Static and fatigue investigations of the anisotropic behavior of W-CR (polychloroprene rubber) material. Reduced energy potential function definition for material behavior. Anisotropic fatigue model definition based on experimental results. Experimental and numerical validation for complex stress state. Abstract: The research presented in this paper aims to provide an insight of tungsten (W) nano-particles reinforced polychloroprene rubber (CR) material (W-CR material) developed for nuclear decommissioning applications and fabricated in sheets by means of rolling process. Static and cyclic anisotropic mechanical properties have been investigated by means of laboratory experiments and finite element simulations, where a fourth-order anisotropic hyperelastic energy potential function has been defined and employed to describe the mechanical behavior of the W-CR material rolled sheet under different loading directions. From the results of the analysis, it has been identified that in contrast to the previous researches, the transversal direction and not the rolling one presents the highest load-carrying capability. As concerns the cyclic material properties, a fatigue life estimation model based on a modified ε-N equation has been developed to define an orientation-dependent cycles-to-failure curve, comprehensive of the results of the three tested directions (0°, 45°, and 90°), showing a correlation factor equal to 92.9%. To verify the reliability of the developed energy potential function, implemented in the finite element model, and of the defined fatigue life estimation model, complex shape specimens have been cut along 0° (rolling), 45°, and 90° directions. The maximum deviation between experimental and numerical load-stroke curve integrals have been calculated in 2.34% whereas the fatigue life prediction in 17.2%, proving the reliability of the implemented numerical and modeling frame.