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.

中文翻译：

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基于3D视觉和集成学习的磁脉冲压接电缆接头质量预测


磁脉冲压接 (MPC) 解决了电缆接头应用中传统液压压接的局限性。然而，缺乏可靠的检测方法给 MPC 制造带来了重大挑战。本研究提出了一种集成 3D 视觉和集成学习的新方法，以实现 MPC 关节的无损质量评估。通过分析压接产品的几何特征，设计了专门的3D视觉算法来提取几何特征。随机样本共识 (RANSAC) 确保了较低的测量误差：端子为 0.5%，电缆为 1.1%。坐标变换简化了特征计算，计算效率提高了18.6%。为了提高数据集质量，设计了预处理管道，其中结合了相关分析、箱线图、主成分分析 (PCA) 和基于密度的噪声应用空间聚类 (DBSCAN)。它有效地处理了不相关、冗余和异常信息。与原始数据集相比，训练均方误差（MSE）从 1.790 下降到 0.290。此外，通过彻底的模型选择和超参数微调，确定了四个高精度候选模型。其中，针对多层感知器 (MLP) 的设计挑战，开发了一种策略来寻找最佳架构，从而形成 3 个隐藏层，每个隐藏层 16 个节点的配置。该策略通过限制隐藏层来减少设计变异性，并通过全批量训练确保稳定的梯度更新。使用集成学习（特别是堆叠）进一步集成候选模型。最终模型的平均绝对误差 (MAE) 为 0。348 kN，平均绝对百分比误差（MAPE）为5%，精度较高。结果表明，所提出的方法在压接质量预测、提高制造效率和可靠性方面具有巨大潜力。