Microstructure characterization of fiber composite laminate using eXplainable Artificial Intelligence

Composite materials, such as Carbon Fiber Reinforced Plastics (CFRP), are widely used across industries due to their exceptional mechanical properties and lightweight nature. With increasing interest in hydrogen storage applications, a comprehensive understanding of CFRP’s microstructure is crucial. However, there remains a critical gap in effectively linking microstructural characteristics to multiscale simulation models for improved tank design. This study presents a detailed analysis of CFRP microstructure using micro-section image analysis. Advanced artificial intelligence techniques, including transfer learning with VGG16 and XGBoost, are employed to detect fibers and matrix-rich regions. Notably, the model achieves an impressive Area Under the Receiver Operating Characteristic (AUC) of 0.89, providing strong quantitative validation. Furthermore, the experimentally determined fiber volume fraction of 0.59 closely aligns with the predicted value of 0.589, demonstrating the model’s accuracy. The investigation extends to analyzing fiber distribution using statistical features such as Voronoi polygons and kernel density estimation. Through dimensionality reduction via t-SNE and clustering, distinct patterns in CFRP’s microstructure are identified, offering deeper insights into its complexity. Finally, an explainable artificial intelligence (XAI) framework is implemented using a surrogate model to map the decision-making process to microstructure characteristics. The surrogate model achieves an F1-score of 0.90, reinforcing the reliability of the approach. This research provides valuable insights into the distinct categories in CFRP’s microstructure, particularly relevant for utilization in multiscale numerical methods.