Fatigue behavior of neat and long glass fiber (LGF) reinforced blends of nylon 66 and isotactic PP
Source abstract
Abstract
Abstract This paper focuses on the study of the fatigue behavior of neat and long glass fiber (LGF) reinforced nylon 66/PP‐blends. The fatigue was characterized using Parislaw plots in the stable crack growth acceleration range. The fatigue crack propagation (FCP) is presented as a function of the crack growth per cycle (da/dN), the amplitude of the stress intensity factor ΔK, and of the strain energy release rate ΔG. It was also of interest to compare the order of performance found in fatigue to that in the static fracture test. The fracture surfaces were characterized with SEM to determine the failure mechanisms. Further, thermographic camera recordings were used to study the size of a “heated” area (ΔT = 2°C) that developed around the crack tip during the cyclic loading of LGF‐PP with different amounts of maleic anhydride grafted PP (PP‐g‐MAH). For the neat materials, a different order of performance was detected under static and cyclic loading. This was explained by the different failure mechanisms observed after static and cyclic fracture that were related to different stress states of the specimens during the fracture process. On the other hand, the LGF‐blends showed a similar order of performance during the static and the fatigue test. This was explained by the observation that similar fiber related failure mechanisms occurred in the composite, both after failure caused by the static and cyclic loading, respectively. For the LGF‐PPs with varying PP‐g‐MAH content, the order of performance in fatigue did not correspond to the size of the “heated area” around the crack tip. This was caused by a change in the composite failure mechanisms, which contributed differently to the size of the “heated area” and to the fatigue performance.
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Research context
OpenAlex topics: Fiber-reinforced polymer composites · Mechanical Behavior of Composites · Natural Fiber Reinforced Composites
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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