In this paper, an in situ piezoelectric-fiber hybrid sensor network was developed to monitor the life-cycle of carbon fiber-reinforced plastics (CFRPs), from the manufacturing phase to the life in service. The piezoelectric lead-zirconate titanate (PZT) sensors were inserted inside the composite structures during the manufacturing process to monitor important curing parameters, including the storage modulus of resin and the progress of the reaction (POR). The strain that is related to the storage modulus and the state of resin was measured by embedded fiber Bragg grating (FBG) sensors, and the gelation moment identified by the FBG sensors was very close to those determined by dynamic mechanical analysis (DMA) and POR. After curing, experiments were conducted on the fabricated CFRP specimen to investigate the damage identification capability of the embedded piezoelectric sensor network. Furthermore, a modified probability diagnostic imaging (PDI) algorithm with a dynamically adaptive shape factor and fusion frequency was proposed to indicate the damage location in the tested sample and to greatly improve the position precision. The experimental results demonstrated that the average relative distance error (RDE) of the modified PDI method was 68.48% and 46.97% lower than those of the conventional PDI method and the PDI method, respectively, with an averaged shape factor and fusion frequency, indicating the effectiveness and applicability of the proposed damage imaging method. It is obvious that the whole life-cycle of CFRPs can be effectively monitored by the piezoelectric-fiber hybrid sensor network.

Monitoring of real-time flow and defects in the vacuum-assisted resin infusion (VARI) process can provide important guidelines for full impregnation of dry reinforcement. A weak fiber Bragg grating array was employed to obtain quasi-distributed monitoring results in real-time. Sensitivity testing of different kinds of coated optical fiber sensors (OFs) was carried out first, and the polyacrylate-coated OF showed a greater wavelength-shift response than the polyimide-coated one. Then, two- and three-dimensional flow monitoring tests were carried out. During the resin-filling stage, three trends of strain curve were identpified in relation to the different placement setups of embedded OFs, the resin flow direction, and the different vacuum-bagging methods. The monitoring criteria were analyzed and the results were compared with the visual inspection, showing good agreement and indicating the ability of the fiber Bragg grating array. Finally, defects including dry spots and voids were introduced and reflected in the maximum changed strains of FBGs due to the smaller stress relaxation, indicating the potential to characterize the local flow state and permeabilities experimentally based on these quasi-distributed sensing methods.

In our work, the three-dimensional flow in stacks of biaxial E-glass fiber non-crimp fabrics (NCF) and unidirectional carbon fiber layers was monitored by the etched optical fiber sensors embedded inside the fabric stack. The influence of embedding sensors with a diameter of 200 µm on the flow was experimentally illustrated and discussed. The out-of-plane permeability of the two fabric materials was estimated with a radial infusion model using the in-plane permeability values as the prior knowledge. The assumed radius of the semi-ellipsoidal flow was derived and showed a good agreement with the results obtained by the best fit of the exact solution, which provides an approximate solution that simplifies the calculation process. The asymptotic form and the modified semi-ellipsoidal inlet radius method for the case of a non-negligible radius of injection tube compared to the fabric sample thickness were verified. The uncertainties related to the unknown flow pattern near the inlet were also demonstrated. The presented results facilitate the further development and standardization of out-of-plane permeability measurement methods with embedded sensors.

The electromechanical impedance model of the piezoelectric ceramics in a free state can be used for screening and quality control in the structural health monitoring community, but the derivation process of the existing model is usually complicated. This paper describes a novel theoretical derivation methodology based on the assumption of zero-stress on the free boundary of the one-dimensional transducer, which can simplify the derivation of the model to a large extent. To assess the accuracy of the model, a signal processing method based on frequency shifting transformation and the Pearson correlation coefficient is also proposed to calculate the similarity between theoretically predicted and experimentally measured data. Two different piezoelectric ceramics were used in experiments to verify the effectiveness of the model. Experimental results convincingly demonstrate that the assumption proposed in this paper possesses good feasibility for one-dimensional thin-walled piezoelectric ceramics and the model has excellent precision.

The compaction of a fabric reinforcement in a Universal Testing Machine (UTM) allows to determine the achievable fiber volume fraction across a wide range of pressures, a valuable information for composite manufacturing. As seen in the first international compressibility benchmark, inaccuracies in the fabric stack thickness measurement, the approach to compliance correction and the non-parallelism between compaction plates resulted in highly inaccurate compression curves. In this paper, the different variability sources affecting indirect thickness methods, based on the machine displacement, and direct methods with laser sensors are presented and its impact on the accuracy is estimated. In conclusion, both thickness measurement methods produced similar results; however, the thickness measured by direct methods experienced more variability due to minor changes in the rig’s displacement or the orientations between plates, combined with other sources of variability such as external interferences or vibrations from the compaction plate which led to variations in measurement precision throughout the tests.