Curing process monitoring of polymeric composites with Gramian angular field and transfer learning-boosted convolutional neural networks

Continuous and accurate monitoring of the degree of curing (DoC) is essential for ensuring the structural integrity of fabricated composites during service. Although machine learning (ML) has shown effectiveness in DoC monitoring, its generalization and extendibility are limited when applied to other curing-related scenarios not included in the previous learning process. To break through this bottleneck, we propose a novel DoC monitoring approach that utilizes transfer learning (TL)-boosted convolutional neural networks alongside Gramian angular field-based imaging processing. The effectiveness of the proposed approach is validated through experiments on metal/polymeric composite co-bonded structures and carbon fiber reinforced polymers using raw sensor data separately collected through the electromechanical impedance and fiber Bragg grating (FBG) measurements. Four indicators, accuracy, precision, recall, and F1-score are introduced to evaluate the performance of generalization and extendibility of the proposed approach. The indicator scores of the proposed approach exceed 0.9900 and outperform other conventional ML algorithms on the FBG dataset of the target domain, demonstrating the effectiveness of the proposed approach in reusing the pre-trained base model on the composite curing monitoring issues.

Automated summary

Continuous and accurate monitoring of curing degree is crucial for ensuring the structural integrity of composites. A novel approach utilizing transfer learning and imaging processing is proposed, which outperforms conventional machine learning algorithms in terms of generalization and extendibility.