Optimal Integration of Supervised Tensor Decomposition and Ensemble Learning for In Situ Quality Evaluation in Friction Stir Blind Riveting

This article develops a novel in situ nondestructive quality evaluation for friction stir blind riveting in joining lightweight materials. This method is able to solve the small sample size problem that is commonly occurred in manufacturing experiments. The proposed method achieves an optimal integration of the tensor decomposition and ensemble learning by utilizing the mutual benefits. On the one hand, diversified feature matrices are extracted via tensor decomposition to maximize the ensemble learning performance. On the other hand, regularized tensor decomposition results deviate with different regularization parameter values and ensemble learning is able to determine the optimal parameter value via a heuristic algorithm, which stabilizes the tensor decomposition results. This optimal integration is built by developing a novel diversity-based feature generation and selection approach: 1) a diversity measure is defined to evaluate the extracted features; 2) a heuristic adaptive algorithm is developed with ensemble learning to determine the optimal regularization parameter for integration; and 3) the optimal features are selected via clustering to maximize the diversity measure, which is expected to strengthen ensemble learning performance for better evaluation results. Numerical studies and case studies are performed to demonstrate the effectiveness of the proposed method as well as its superiority over the existing methods.