Quality prediction for magnetic pulse crimping cable joints based on 3D vision and ensemble learning

Abstract

Magnetic pulse crimping (MPC) addresses the limitations of conventional hydraulic crimping in cable joint applications. However, the lack of dependable detection methods presents a significant challenge in MPC manufacturing. This study proposed a novel approach integrating 3D vision and ensemble learning to achieve a non-destructive quality assessment of MPC joints. By analyzing the geometric characteristics of crimping products, a specialized 3D vision algorithm was devised to extract geometric features. The random sample consensus (RANSAC) ensured low measurement errors: 0.5 % for terminals and 1.1 % for cables. Coordinate transformation simplified the feature calculation, resulting in an 18.6 % improvement in computational efficiency. To enhance dataset quality, a preprocessing pipeline was designed, incorporating correlation analysis, boxplots, principal component analysis (PCA), and density-based spatial clustering of applications with noise (DBSCAN). It handled irrelevant, redundant, and outlier information effectively. Compared to the original dataset, the training mean squared error (MSE) decreased from 1.790 to 0.290. Additionally, four high-accuracy candidate models were identified via thorough model selection and hyperparameter fine-tuning. Among them, for the design challenge of multilayer perceptron (MLP), a strategy was developed to find an optimal architecture, resulting in a configuration of 3 hidden layers with 16 nodes each. This strategy reduced design variability by constraining hidden layers and ensured stable gradient updates through full-batch training. The candidate models were further integrated using ensemble learning, specifically stacking. The final model achieved a mean absolute error (MAE) of 0.348 kN, and its mean absolute percentage error (MAPE) was 5 %, demonstrating higher accuracy. The results demonstrate the significant potential of the proposed approach in crimping quality prediction, enhancing manufacturing efficiency and reliability.