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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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Airborne Dental Material Particulates and Occupational Exposure: Computational and Field Insights into Airflow Dynamics and Control Strategies
(2025-11-01)
Chanbandit, Chanapat
;
Kanchanatawewat, Kanchana
;
Oo, Ghaim Man
;
Thongsri, Jatuporn
;
Tuntiwong, Kuson
Occupational exposure to airborne polymethacrylate (PMMA) particles during dental laboratory procedures poses an underexplored health risk. This study presents the first integrated Computational Fluid Dynamics (CFD) and real-time particle monitoring investigation of 0.5 µm PMMA particle dispersion during mechanical polishing in an actual clinic. We quantitatively assessed particle behavior in 30 s exposure scenarios by examining the effects of dental professional work orientations and comparing two mitigation strategies, rear-inlet portable air cleaners (PACs) and a Box Dust Collector (BC), with an emphasis on the safety of both personnel and patients. The findings establish that operatory airflow is a primary safety determinant: aligning the workflow with the main airflow (0°). Furthermore, the combined use of PACs and BC demonstrated synergistic superiority, achieving the optimal reduction in peak concentrations and airborne residence time. PACs alone reduced working zone concentrations by up to 80%, while BC provided a crucial 40–60 s delay in initial plume dispersion. We conclude that effective exposure control requires a proactive, two-stage engineering defense: source confinement augmented by continuous ambient filtration. This research provides a robust, evidence-based foundation for defining airflow-aware ergonomic and combined engineering standards in the evolving digital era of dentistry.
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Crack Localization Detection in Monolithic Zirconia Dental Crowns via 1D-Convolutional Neural Networks Algorithm-Based Acoustic Emission Analysis
(2024-01-01)
Tuntiwong, Kuson
;
Wangman, Rangsinee
;
Sritart, Hiranya
;
Kanchanatawewat, Kanchana
;
Tungjitkusolmun, Supan
The feasibility of utilizing the acoustic emission (AE) technique for the detection and classification of cracks within monolithic dental crown is assessed in this study, owing to its non-destructive nature which enables passive monitoring of structures. The AE signals captured are subjected to analysis to extract pertinent information regarding the source and location of the cracks. A novel approach is proposed, employing deep learning 1D convolutional neural networks (1D-CNNs) for the recognition and classification of the recorded cracked signals. The AE signals, obtained through a handmade AE data acquisition unit, are converted into .csv format and subjected to denoising using Bayesian methods to eliminate background noise. The signals are collected through the breakage of pencil lead (Vallen systeme) Hsu-Nielsen-Source 0.5 (ASTM E976) applied to each surface of the dental crown. Subsequently, the data signals are divided into training and testing groups following an 85 / 15 split. The performance of the deep learning 1D-CNNs is evaluated based on Precision, Recall and total accuracy metrics. The applicated of automated deep learning in this study demonstrated significantly high overall accuracy (98.67%). The integration of handmade data acquisition with 1D-CNN crack detection proves to be an effective method for early screening. The novel method harnesses acoustic emission signals in 1D-CNNs, thereby enhancing the accuracy of clinical dental restorative crack identification and determining the onset time.

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