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Smart Diagnostics: Hierarchical Deep Learning of Acoustic Emission Signals for Early Crack Detection in Zirconia Dental Structures
(2026-05-01)
Tuntiwong, Kuson
;
Wangman, Rangsinee
;
Kanchanatawewat, Kanchana
;
Anucul, Boonjira
;
Sritart, Hiranya
Monolithic zirconia restorations are frequently affected by the unnoticed growth of subcritical cracks, a failure process that is not captured by traditional imaging methods like radiographs and ultrasounds in sophisticated dental architectures. To address this evaluative inadequacy, this research introduces a hierarchical deep learning framework for microcrack detection and spatial localization. We promote a hierarchical deep learning system that integrates Acoustic Emission (AE) detection alongside signal processing. Raw AE signals utilized during dynamic loading are enhanced via Kalman filtering and Continuous Wavelet Transform (CWT) to construct high-fidelity time–frequency scalograms. The diagnostic pipeline operates in two stages: first, a hybrid CNN–BiGRU network with temporal attention fulfills zirconia component-level classification; second, a ResNet-18 backbone integrated with Bidirectional LSTM and Multi-Head Attention precisely localizes defects across five anatomical crown regions. This hierarchical design effectively captures the non-stationary, transient nature of fracture-induced stress waves. The framework achieved an F1-score of 99.00% and an AUC of 0.994, significantly outperforming conventional convolutional networks. By enabling predictive maintenance through early, non-invasive damage localization, this study demonstrates a promising laboratory framework for AE-based crack detection in zirconia dental structures and prosthetics and toward enhanced clinical reliability in digital dentistry.
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Continuous Wavelet Transform based SqueezeNet for Damage Classification of Monolithic Zirconia Dental Crowns Using Acoustic Emission Analysis
(2024-01-01)
Tuntiwong, Kuson
;
Phasukkit, Pattarapong
;
Tungjitkusolmun, Supan
This work presents a novel approach utilizing continuous wavelet transform and deep learning for pattern recognition and classification of generated cracked signals. The research employs a smart sensing data acquisition system in a non-destructive manner, utilizing dual acoustic emission (AE) sensor to determine the specific cracked position at the crown surface. An AE data acquisition module is used to transform the AE signals captured by the AE sensor into.csv data, which is then subjected to denoising utilizing band stop and Bayesian filters to eliminate background noise. Subsequently, the denoised data are processed and classified using an algorithmic model called SqueezeNet, which facilitates the accurate localization of cracks in the Monolithic crown. For the purpose of training and validating the algorithmic model, the study uses AE signals produced by pencil lead breaking (PLB) at the incisal, labial, palatal, left, and right sides of the crown. In order to make it easier to identify the sources of AE signals resulting from PLB-induced AE events, wavelet transform (WT) is used to assess the location of the crown in respect to AE signals. AE signals are divided into two categories and the algorithm's accuracy in classifying them is assessed during the training and testing stages. With a total accuracy of 9 6. 5%, the deep learning-powered AE approach successfully detects flaws on the dental crown surfaces. The integration of a dual AE sensor with a SqueezeNet algorithm based on AE signals sets this study apart, offering an automated and efficient solution for early cracked detection in dental crowns for prototype clinical dental restorative crack identification.

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