A damage identification system of carbon fiber reinforced plastics （CFRP） structures is investigated using fiber Bragg grating （FBG） sensors and back propagation （BP） neural network. FBG sensors are applied to c...A damage identification system of carbon fiber reinforced plastics （CFRP） structures is investigated using fiber Bragg grating （FBG） sensors and back propagation （BP） neural network. FBG sensors are applied to construct the sensing network to detect the structural dynamic response signals generated by active actuation. The damage identification model is built based on the BP neural network. The dynamic signal characteristics extracted by the Fourier transform are the inputs, and the damage states are the outputs of the model. Besides, damages are simulated by placing lumped masses with different weights instead of inducing real damages, which is confirmed to be feasible by finite element analysis （FEA）. At last, the damage identification system is verified on a CFRP plate with 300mm × 300mm experimental area, with the accurate identification of varied damage states. The system provides a practical way for CFRP structural damage identification.展开更多
In this paper, a combined viscoelasticity-viscoplasticity model, coupled with anisotropic damage and moisture effects, is developed for short fiber reinforced polymers (SFRPs) with different fiber contents and subject...In this paper, a combined viscoelasticity-viscoplasticity model, coupled with anisotropic damage and moisture effects, is developed for short fiber reinforced polymers (SFRPs) with different fiber contents and subjected to a variety of strain rates. In our model, a rate-dependent yield surface for the matrix phase is employed to identify initial yielding of the material. When an SFRP is loaded at small deformation before yielding, its viscoelastic behavior can be described using the generalized Maxwell model, while when plasticity occurs, a scalar internal state variable (ISV) is used to capture the hardening behavior caused by the polymeric constituent of the composite. The material degradation due to the moisture absorption of the composite is modeled by employing another type of ISV with different evolution equations. The complicated damage state of the SFRPs is captured by a second rank tensor, which is further decomposed to model the subscale damage mechanisms of micro-voids/cracks nucleation, growth and coalescence. It is concluded that the proposed constitutive model can be used to accurately describe complicated behaviors of SFRPs because the results predicted from the model are in good agreement with the experimental data.展开更多
基金This work was supported by the National Natural Science Foundation of China under Grant Nos. 41472260 and 51373090, the Natural ScienceFoundation of Shandong Province, China under Grant Nos. 2014ZRE27372 and ZR2017BF007, the Fundamental research funds of Shandong University, China under Grant No. 2016JC012, and the Young Scholars Program of Shandong University under Grant No. 2016WLJH30.
文摘A damage identification system of carbon fiber reinforced plastics （CFRP） structures is investigated using fiber Bragg grating （FBG） sensors and back propagation （BP） neural network. FBG sensors are applied to construct the sensing network to detect the structural dynamic response signals generated by active actuation. The damage identification model is built based on the BP neural network. The dynamic signal characteristics extracted by the Fourier transform are the inputs, and the damage states are the outputs of the model. Besides, damages are simulated by placing lumped masses with different weights instead of inducing real damages, which is confirmed to be feasible by finite element analysis （FEA）. At last, the damage identification system is verified on a CFRP plate with 300mm × 300mm experimental area, with the accurate identification of varied damage states. The system provides a practical way for CFRP structural damage identification.
文摘In this paper, a combined viscoelasticity-viscoplasticity model, coupled with anisotropic damage and moisture effects, is developed for short fiber reinforced polymers (SFRPs) with different fiber contents and subjected to a variety of strain rates. In our model, a rate-dependent yield surface for the matrix phase is employed to identify initial yielding of the material. When an SFRP is loaded at small deformation before yielding, its viscoelastic behavior can be described using the generalized Maxwell model, while when plasticity occurs, a scalar internal state variable (ISV) is used to capture the hardening behavior caused by the polymeric constituent of the composite. The material degradation due to the moisture absorption of the composite is modeled by employing another type of ISV with different evolution equations. The complicated damage state of the SFRPs is captured by a second rank tensor, which is further decomposed to model the subscale damage mechanisms of micro-voids/cracks nucleation, growth and coalescence. It is concluded that the proposed constitutive model can be used to accurately describe complicated behaviors of SFRPs because the results predicted from the model are in good agreement with the experimental data.
