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Random process model of mechanical property degradation in carbon fiber-reinforced plastics under thermo-oxidative aging

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The mechanical properties of carbon fiber-reinforced plastics used in aerospace are vulnerable to degradation under thermo-oxidative aging conditions. However, it is hard to establish a mechanical property prediction model for… Click to show full abstract

The mechanical properties of carbon fiber-reinforced plastics used in aerospace are vulnerable to degradation under thermo-oxidative aging conditions. However, it is hard to establish a mechanical property prediction model for carbon fiber-reinforced plastics from thermo-oxidative aging mechanism point of view since the thermo-oxidative aging degradation processes are very complex. A mathematical model was proposed based on the theory of stochastic processes for predicting mechanical property degradation of carbon fiber-reinforced plastics under thermo-oxidative aging conditions in the present work. However, the predicted values calculated by the “random process model” were not in good agreement with experimental data. And then a “modified random process model” (namely a wider random process model) was established through Box–Cox transformation for random process model. The verification of the evaluation model showed that the modified random process model can nicely describe the mechanical performance degradation of carbon fiber-reinforced plastics with the increasing of aging time under certain aging temperatures. As the modified random process model was established without limiting the reinforced structure of carbon fiber-reinforced plastics, the described method provides an opportunity to rapidly predict the mechanical properties and the lifetime of any carbon fiber-reinforced plastics by testing the mechanical properties of carbon fiber-reinforced plastics before and after aging for a short period of time.

Keywords: carbon fiber; fiber reinforced; reinforced plastics; model; random process

Journal Title: Journal of Composite Materials
Year Published: 2017

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