Identification of voids and interlaminar shear strengths of polymer-matrix composites by optical microscopy experiment and deep learning methodology

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%.

Automated summary

The study utilized a comprehensive approach combining deep learning algorithm and theoretically driven equation to automatically identify and analyze microscopic voids in composite materials, investigating their relationship with interlaminar shear strength. Experimental results demonstrated that the proposed method can accurately obtain ILSS values for laminates with different void contents.