Diffusion of moisture through composites is one of the main environmental causes of their deterioration and loss of service life. This paper deals with water diffusion in the unidirectional continuous carbon fiber reinforced polyamide 6 composites by experimental measurement, theoretical analysis, and numerical simulation. Immersion experiment is respectively conducted in distilled water at 25°C, 70°C, and 95°C for the pure polyamide 6 resin and the carbon fiber/polyamide 6 composite. Then, the theoretical Fickian and Langmuir models are employed to fit the gravimetric data of the specimens. Subsequently, water diffusion in the composite is also simulated using finite element method. Moreover, to capture the real distribution of fibers in the matrix, the random algorithm is developed to generate the computational composite models with randomly dispersed fibers. Finally, the comparison between the experimental, theoretical, and numerical results is made to assess the applicability of theoretical models and the influences of fiber distribution and interphase on the effective water diffusion coefficient of composite.

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The internal void defects induced during the manufacturing process of polymer‐matrix composites can significantly degrade the mechanical properties of the composite, particularly the interlaminar shear strength (ILSS). In this study, we developed an innovative integrated methodology based on a deep learning semantic segmentation algorithm, named DeepLabV3+, and a theoretically driven equation to automatically identify voids in optical images and investigate the relationship between the microscopic voids and macroscopic ILSS parameters of the composite laminates. Results suggest that for the best fine‐tuned DeepLabV3+ framework, the corresponding mean pixel accuracy and intersection over union scores on the testing set were 99.84% and 90.82%, respectively, thereby indicating the potential of the generalized trained model. In addition, detailed experiments revealed that the proposed method can successfully obtain the ILSS values of laminates with different void contents. In addition, the ILSS values of the carbon/epoxy laminates decreased by approximately 27% with an increase in the void content from 0.07% to 3.14%.

Voids are comment defects generated during the manufacturing process and highly sensitive to moisture in the hygrothermal environment, which has deleterious effects on the mechanical performances. However, the combined impact of void content and water-absorbed content on mechanical properties is not clear. Based on the random sequential adsorption algorithm, a microscale unit cell with random distribution of fibers, interfaces and voids was established. The quantitative effects of voids content on strength and modulus under the loading of transverse tension, compression and shear were investigated by introducing a degradation factor dependent on water content into the constitutive model, and the different failure mechanisms before and after hygrothermal aging were revealed. Conclusively, before hygrothermal aging, voids induce the decrease in mechanical properties due to stress concentration, and every 1% increase in the void content results in a 6.4% decrease in transverse tensile strength. However, matrix degradation due to the absorbed water content after hygrothermal aging is the dominant factor, and the corresponding rate is 3.86%.

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