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Acoustic and Ultrasonic Sensing Technology in Non-Destructive Testing—2nd Edition

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: 25 November 2026 | Viewed by 3519

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Special Issue Editors

Dr. Sergio Castiñeira-Ibáñez

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Centro de Tecnologías Físicas, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: acoustics materials; acoustic lenses; finite elements method; topology, geometric parametrical and translation optimisation; sound propagation in complex media; composite materials; US material characterisation; medical US applications; modelling ultrasonic devices; ultrasonic NDT
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Dr. Daniel Tarrazó-Serrano

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Prof. Dr. Constanza Rubio Michavila

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Special Issue Information

Dear Colleagues,

We present the launch of a Special Issue of the MDPI journal Sensors devoted to “Acoustic and Ultrasonic Sensing Technology in Non-Destructive Testing—2nd Edition”.

The applications of ultrasonic sensors are extremely varied, ranging from use in the verification of trees in poor condition, owing to their non-invasive qualities, to the production of microfluidic movement by means of slightly focused acoustic waves. They have found broad appeal across diverse disciplines and applications, ranging from use as sensors for guiding and checking for industrial and non-industrial non-destructive testing to biological, medical, and food industry applications.

This Special Issue aims to highlight recent advances in the modelling and development of ultrasound devices and materials. Topics may include, but are not limited to, the following keywords.

Dr. Sergio Castiñeira-Ibáñez
Dr. Daniel Tarrazó-Serrano
Prof. Dr. Constanza Rubio Michavila
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

ultrasonic devices
HIFU transducer(s)
non-destructive testing material characterization
photonic nanojets
ultrasonic imaging and visualization
magnetic resonance imaging (MRI) compatible materials
medical and biomedical ultrasonic sensors
biological ultrasonic sensors
food characterization ultrasonic sensors

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Acoustic and Ultrasonic Sensing Technology in Non-Destructive Testing in Sensors (18 articles)

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Research

23 pages, 4696 KB

Open AccessArticle

The Role of Infill Density in Impact Localization for Additively Manufactured Structures

by Hussain Altammar

Sensors 2026, 26(9), 2720; https://doi.org/10.3390/s26092720 - 28 Apr 2026

Viewed by 660

Abstract

The optimization of impact localization in 3D-printed structures is critical for their application in smart monitoring and damage detection systems. This study investigates the influence of infill density on the accuracy of low-velocity impact localization in 3D-printed plates. Specimens with cubic infill patterns and varying densities (30%, 50%, and 100%) were fabricated and subjected to impacts with varying locations and magnitudes using two different sensor network configurations. A genetic algorithm integrated with continuous wavelet transform was employed to simultaneously determine impact coordinates and group velocity. Key findings reveal that lower infill structures act as mechanical low-pass filters, producing clean and low-frequency signals, while higher densities support complex wave propagation with higher energy and broader frequency content. The dominant frequency of first arrival shifts toward lower values with increasing impact energy across all densities. Group velocity increases with both impact energy and infill density. For 30% infill, it averages around 450 m/s, while for 100% infill it exceeds 800 m/s. The genetic algorithm demonstrated robust performance across all experimental conditions, simultaneously estimating impact coordinates and group velocity with average errors below 6% for all infill densities. Spatial probability mass functions revealed tightly clustered predictions around true impact locations, with maximum probabilities reaching 68% and uncertainties below 5%. Computational efficiency varied modestly with infill density. These findings provide quantitative relationships between infill density, wave propagation characteristics, and localization performance for designing a reliable structural health monitoring of additively manufactured structures. Full article

(This article belongs to the Special Issue Acoustic and Ultrasonic Sensing Technology in Non-Destructive Testing—2nd Edition)

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36 pages, 9428 KB

Open AccessArticle

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

by Kuson Tuntiwong, Rangsinee Wangman, Kanchana Kanchanatawewat, Boonjira Anucul, Hiranya Sritart, Pattarapong Phasukkit and Supan Tungjitkusolmun

Sensors 2026, 26(9), 2682; https://doi.org/10.3390/s26092682 - 26 Apr 2026

Viewed by 1366

Abstract

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. Full article

(This article belongs to the Special Issue Acoustic and Ultrasonic Sensing Technology in Non-Destructive Testing—2nd Edition)

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16 pages, 5851 KB

Open AccessArticle

Bolt Anchorage Defect Identification Based on Ultrasonic Guided Wave and Deep Learning

by Hui Xing, Weiguo Di, Xiaoyun Sun, Mingming Wang and Chaobo Li

Sensors 2025, 25(20), 6431; https://doi.org/10.3390/s25206431 - 17 Oct 2025

Cited by 1 | Viewed by 955

Abstract

As a critical supporting component in geotechnical engineering structures such as bridges, tunnels, and highways, the anchorage quality of bolts directly impacts their structural safety. The ultrasonic guided wave method is a popular method for the non-destructive testing of anchorage quality. However, noise from complex field environments, modal mixing caused by anchoring interface reflections, and dispersion effects make it challenging to directly extract defect features from guided wave signals in the time or frequency domains. To address these challenges, this study proposes a solution based on the combination of the guided wave time–frequency spectrum and the gated attention residual network (GA-ResNet). The GA-ResNet introduces a gating mechanism to balance spatial attention and channel attention, and it is used for anchoring model type recognition. Experiments were conducted on four types of anchorage models, and the time–frequency spectrum was selected to be the input feature. The results demonstrate that the GA-ResNet can effectively predict the anchorage bolt defect type and prevent potential safety accidents. Full article

(This article belongs to the Special Issue Acoustic and Ultrasonic Sensing Technology in Non-Destructive Testing—2nd Edition)

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