Deep Learning-Based Acoustic Emission Scheme for Nondestructive Localization of Cracks in Monolith Zirconia Dental crown under Load

Abstract

This study introduces an innovation non-destructive approach employing dual-sensor acoustic emission (AE) techniques to detect and accurately locate cracks within monolith dental crowns, even when subjected to various load directions. Throughout the process, the AE sensor recorded AE signals that were subsequently converted into .csv data using an AE data acquisition module. Afterward, these digital records underwent denoising to eliminate background and contact-related disturbances. Subsequently, the noise-filtered data underwent processing and classification via a deep learning algorithmic model, enabling accurate localization of cracks within the dental crown. The study utilized AE signals generated by pencil lead breakage (PLB) at the labial, left, palatal, and right surfaces of dental crown for both training and testing of the algorithmic model. Initially, wavelet transform (WT) was employed to analyze and examine the dental crown's location in relation to AE signals. The approach was introduced to precisely identify the sources of AE signals originating from PLB-induced AE events. During the algorithm's training and testing phases, the AE signals were categorized into two groups, and the accuracy of their classification was evaluated. The deep learning-powered AE strategy successfully identified cracks within the dental crown. The total accuracy was 90.625%. This study's innovation resides in its utilization of a dual AE sensor combined with a deep learning algorithm based on AE signals. This combination effectively detects and precisely locates cracks within dental crowns. In contrast to current AE crack-localization methods that depend on human interpretation in radiographic examination, this approach offers an automated and more efficient solution.