This article presents a classification of Acoustic Emission (AE) signals from AlSi10Mg specimens produced via Selective Laser Melting (SLM). Tensile tests characterized the mechanical properties of specimens printed in different orientations (X, Y, Z, 45°). Initially, a study quantified damage modes based on the stress-strain curve and cumulative AE energy. AE signals for each specimen (X, Y, 45°, Z), across deformation stages (elastic and plastic), and damage modes were analyzed using continuous wavelet transform to extract time-frequency features. A novel convolutional neural network, based on artificial bee colonies and fuzzy C-means, was developed for scalogram classification. Data augmentation with Gaussian white noise enhanced the approach. Cross-validation ensured robustness against overfitting and suboptimal local maxima. Evaluation metrics, including the confusion matrix, precision-recall curve, and F1 score, demonstrated the algorithm's high accuracy of 92.6%, precision-recall curve of 92.5%, and F1 score of 92.5% for AE signals based on printing direction (X, Y, 45°, Z). The study highlighted the potential for improving AE signal classification related to elastic and plastic deformation stages with 100% accuracy. For damage modes, the algorithm achieved a confusion matrix accuracy of 90.6%, a precision-recall curve of 90.4%, and an F1 score of 90.5%. This approach demonstrates high accuracy in classifying AE signals across different printing orientations, deformation stages, and damage modes of AlSi10Mg specimens manufactured through SLM.

中文翻译：

---

使用基于模糊人工蜂群的深度学习增强声发射技术，用于表征选择性激光熔化的 AlSi10Mg 样品

本文介绍了通过选择性激光熔化 (SLM) 生产的 AlSi10Mg 样品的声发射 (AE) 信号的分类。拉伸测试表征了不同方向（X、Y、Z、45°）打印样本的机械性能。最初，一项研究根据应力-应变曲线和累积声发射能量量化了损伤模式。使用连续小波变换来分析每个样本（X、Y、45°、Z）、跨变形阶段（弹性和塑性）和损伤模式的声发射信号，以提取时频特征。开发了一种基于人工蜂群和模糊 C 均值的新型卷积神经网络，用于尺度图分类。高斯白噪声的数据增强增强了该方法。交叉验证确保了针对过度拟合和次优局部最大值的稳健性。包括混淆矩阵、精确回忆曲线和 F1 分数在内的评估指标表明，该算法对于基于打印方向（X、X、 Y、45°、Z）。该研究强调了以 100% 准确度改进与弹性和塑性变形阶段相关的 AE 信号分类的潜力。对于损伤模式，该算法实现了 90.6% 的混淆矩阵准确率、90.4% 的精确召回率曲线和 90.5% 的 F1 分数。该方法展示了对通过 SLM 制造的 AlSi10Mg 样品的不同打印方向、变形阶段和损伤模式的 AE 信号进行分类的高精度。