Enhancing the acoustic emission technique using fuzzy artificial bee colony-based deep learning for characterizing selective laser melted AlSi10Mg specimens

Abstract

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.