1972
DOI: 10.5254/1.3542885
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Influence of Hysteresis on Tensile and Fatigue Failure in Rubbers

Abstract: The failure criterion developed by Harwood et al. between energy input to break and hysteresis at break for amorphous rubbers has been related to the fatigue and cut growth properties of the rubber which are based on the tearing energy theory. It is found that the constant K in the hysteresis failure criterion is a function of the cut growth constant G and the inherent flaw size C0. The effect of adding fine particulate fillers to amorphous rubbers on the hysteresis and fatigue properties is considered and sho… Show more

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Cited by 5 publications
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“…76 Relationships between hysteresis and fatigue properties have been observed by many researchers. [77][78][79][80][81][82][83] Lake and Thomas 84 have suggested that if a material exhibits no hysteresis whatsoever, only sudden fracture is possible. Further, hysteresis controls the sensitivity of the crack growth rate with respect to tear energy: the higher the hysteresis, the lower the slope (power-law exponent) of the energy release rate-crack growth relationship.…”
Section: Constitutive Behaviormentioning
confidence: 99%
“…76 Relationships between hysteresis and fatigue properties have been observed by many researchers. [77][78][79][80][81][82][83] Lake and Thomas 84 have suggested that if a material exhibits no hysteresis whatsoever, only sudden fracture is possible. Further, hysteresis controls the sensitivity of the crack growth rate with respect to tear energy: the higher the hysteresis, the lower the slope (power-law exponent) of the energy release rate-crack growth relationship.…”
Section: Constitutive Behaviormentioning
confidence: 99%
“…Nowadays, the M-L distribution has been applied as a novelty statistical tool to describe non-exponential statistical phenomena in diverse fields [ 18 , 19 ], such as bridge fatigue life assessment [ 12 ] and modeling of an anomalous diffusion with hereditary effects for the importance of the M-L function in the fractional calculus [ 20 , 21 ]. We choose the M-L distribution as a tool to describe the distribution of fatigue data, since it has an apparent hereditary effect and power decay or heavy-tailed traits [ 22 , 23 , 24 , 25 , 26 , 27 ].…”
Section: Introductionmentioning
confidence: 99%