Smart Diagnostics: Hierarchical Deep Learning of Acoustic Emission Signals for Early Crack Detection in Zirconia Dental Structures

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dc.contributor.authorTuntiwong, Kuson
dc.contributor.authorWangman, Rangsinee
dc.contributor.authorKanchanatawewat, Kanchana
dc.contributor.authorAnucul, Boonjira
dc.contributor.authorSritart, Hiranya
dc.contributor.authorPhasukkit, Pattarapong
dc.contributor.authorTungjitkusolmun, Supan
dc.date.accessioned2026-08-06T10:55:23Z
dc.date.available2026-08-06T10:55:23Z
dc.date.issued2026-05-01
dc.description.abstractMonolithic 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.
dc.identifier.citationSensors, 26(9), 2026
dc.identifier.doi10.3390/s26092682
dc.identifier.issn14248220
dc.identifier.other2-s2.0-105038790179
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/18063
dc.sourceSensors
dc.subjectacoustic emission
dc.subjectcrack detection
dc.subjectdeep learning
dc.subjectdental zirconia
dc.subjectgated recurrent unit s (GRU)
dc.subjectlong short-term memory (LSTM)
dc.subjectnondestructive testing
dc.subjectpencil lead  break (PLB) test
dc.subjectscalogram
dc.titleSmart Diagnostics: Hierarchical Deep Learning of Acoustic Emission Signals for Early Crack Detection in Zirconia Dental Structures
dc.typeArticle

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