An ensemble approach for enhancing generalization and extendibility of deep learning facilitated by transfer learning: principle and application in curing monitoring

Abstract

Machine learning (ML) and deep learning (DL) have exhibited significant advantages compared to conventional data analysis methods. However, the limitations of poor generalization and extendibility impede the broader application of these methods beyond specific learning tasks. To address this challenge, this study proposes a transfer learning-based ensemble approach called SMART (SM-AR-T). This approach incorporates synthetic minority oversampling technique (SMOTE), average reinforced interpolation (ARI), series data imaging, and fine-tuning. To validate the effectiveness of SMART, we conduct experiments on curing monitoring of polymeric composites and construct a hybrid dataset with highly heterogeneous features. We compare the performance of SMART with exemplary ML algorithms using conventional evaluation indicators, including Accuracy, Precision, Recall, and F1-score. The experimental results demonstrate that the SMART approach exhibits superior generalization capacity and extendibility, achieving indicator scores above 0.9900 in new scenarios. These findings suggest that the proposed SMART approach has the potential to break through the limitations of conventional ML and DL models, enabling wider applications in the industrial sectors.