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Sensing and Signal Processing in Nondestructive Evaluation
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Sensing and Signal Processing in Nondestructive Evaluation

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

Deadline for manuscript submissions: closed (25 October 2023) | Viewed by 6633

Special Issue Editors

Dr. Kong Fah Tee

E-Mail Website
Guest Editor
Faculty of Engineering and Science, University of Greenwich, Kent ME4 4TB, UK
Interests: structural reliability and risk-cost optimization; structural health monitoring and management; structural system identification and life prediction; forensic engineering; wave propagation and signal processing; experimental stress analysis; fatigue and fracture mechanics; structural control and smart structures; structural behavior and design under extreme loading: fire, wind, and explosion
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Bin Huang

E-Mail Website
Guest Editor
Department of Civil Engineering, Wuhan University of Technology, Wuhan 430070, China
Interests: stochastic model updating; structural health monitoring; stochastic finite element method; SMA components for vibration control

Special Issue Information

Dear Colleagues,

Nondestructive evaluation is defined as testing and analysis techniques to detect flaws and imperfections in materials, components, structures or systems to ensure structural integrity. The acquired data in combination with advanced sensing and signal processing techniques can provide monitoring, inspection, damage assessment and maintenance actions upon demand. In this respect, the Special Issue would focus the attention on all the opportunities for nondestructive evaluation. The Special Issue hence focuses on recent developments in theoretical, computational, experimental and practical aspects in the field. Topics include, but are not limited to, the following:

  • Damage Detection and Assessment
  • Damage Modelling
  • Innovative Sensing Solutions
  • Modal Analysis
  • Model Verification and Validation
  • Modeling and Simulation
  • Non-contact Dynamics Measurement
  • Nondestructive Testing and Evaluation
  • Non-linear Guided Waves
  • Real-world Applications
  • Sensors and Actuators
  • Sensor Network/System Integration
  • Signal Processing
  • Structural Health Monitoring
  • Structural Integrity and Reliability
  • System Identification and Assessment
  • Structural Model Updating
  • Vibration-based damage detection

Dr. Kong Fah Tee
Prof. Dr. Bin Huang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Published Papers (4 papers)

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Research

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23 pages, 45404 KiB  
Article
Model-Based Damage Localization Using the Particle Swarm Optimization Algorithm and Dynamic Time Wrapping for Pattern Recreation
by Ilias Zacharakis and Dimitrios Giagopoulos
Sensors 2023, 23(2), 591; https://doi.org/10.3390/s23020591 - 4 Jan 2023
Cited by 3 | Viewed by 1243
Abstract
Vibration-based damage detection methods are a subcategory of Structural Health Monitoring (SHM) methods that rely on the fact that structural damage will affect the dynamic characteristic of a structure. The presented methodology uses Finite Element Models coupled with a metaheuristic optimization algorithm in [...] Read more.
Vibration-based damage detection methods are a subcategory of Structural Health Monitoring (SHM) methods that rely on the fact that structural damage will affect the dynamic characteristic of a structure. The presented methodology uses Finite Element Models coupled with a metaheuristic optimization algorithm in order to locate the damage in a structure. The search domains of the optimization algorithm are the variables that control a parametric area, which is inserted into the FE model. During the optimization procedure, this area changes location, stiffness, and mass to simulate the effect of the physical damage. The final output is a damaged FE model which can approximate the dynamic response of the damaged structure and indicate the damaged area. For the current implementation of this Damage Detection Framework, the Particle Swarm Optimization algorithm is used. As an effective metric of the comparison between the FE model and the experimental structure, Transmittance Functions (TF) are used that require output only acceleration signals. As with most model-based methods, a common concern is the modeling error and how this can be surpassed. For this reason, the Dynamic Time Wrapping (DTW) algorithm is applied. When damage occurs in a structure it creates some differences between the Transmittance Functions (TF) of the healthy and the damaged state. With the use of DTW, the damaged pattern is recreated around the TF of the FE model, while creating the same differences and, thus, minimizing the modeling error. The effectiveness of the proposed methodology is tested on a small truss structure that consists of Carbon-Fiber Reinforced Polymer (CFRP) filament wound beams and aluminum connectors, where four cases are examined with the damage to be located on the composite material. Full article
(This article belongs to the Special Issue Sensing and Signal Processing in Nondestructive Evaluation)
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21 pages, 6264 KiB  
Article
Three-Dimensional Printing Quality Inspection Based on Transfer Learning with Convolutional Neural Networks
by Cheng-Jung Yang, Wei-Kai Huang and Keng-Pei Lin
Sensors 2023, 23(1), 491; https://doi.org/10.3390/s23010491 - 2 Jan 2023
Cited by 4 | Viewed by 2200
Abstract
Fused deposition modeling (FDM) is a form of additive manufacturing where three-dimensional (3D) models are created by depositing melted thermoplastic polymer filaments in layers. Although FDM is a mature process, defects can occur during printing. Therefore, an image-based quality inspection method for 3D-printed [...] Read more.
Fused deposition modeling (FDM) is a form of additive manufacturing where three-dimensional (3D) models are created by depositing melted thermoplastic polymer filaments in layers. Although FDM is a mature process, defects can occur during printing. Therefore, an image-based quality inspection method for 3D-printed objects of varying geometries was developed in this study. Transfer learning with pretrained models, which were used as feature extractors, was combined with ensemble learning, and the resulting model combinations were used to inspect the quality of FDM-printed objects. Model combinations with VGG16 and VGG19 had the highest accuracy in most situations. Furthermore, the classification accuracies of these model combinations were not significantly affected by differences in color. In summary, the combination of transfer learning with ensemble learning is an effective method for inspecting the quality of 3D-printed objects. It reduces time and material wastage and improves 3D printing quality. Full article
(This article belongs to the Special Issue Sensing and Signal Processing in Nondestructive Evaluation)
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21 pages, 33588 KiB  
Article
A Study on the Application of Machine and Deep Learning Using the Impact Response Test to Detect Defects on the Piston Rod and Steering Rack of Automobiles
by Young-Geun Yoon, Ji-Hoon Woo and Tae-Keun Oh
Sensors 2022, 22(24), 9623; https://doi.org/10.3390/s22249623 - 8 Dec 2022
Viewed by 1289
Abstract
The main parts of automobiles are the piston rod of the shock absorber and the steering rack of the steering gear, and their quality control is critical in the product process. In the process line, these products are normally inspected through visual inspection, [...] Read more.
The main parts of automobiles are the piston rod of the shock absorber and the steering rack of the steering gear, and their quality control is critical in the product process. In the process line, these products are normally inspected through visual inspection, sampling, and simple tensile tests; however, if there is a problem or abnormality, it is difficult to identify the type and location of the defect. Usually, these defects are likely to cause surface cracks during processing, which in turn accelerate the deterioration of the shock absorber and steering, causing serious problems in automobiles. As a result, the purpose of this study was to present, among non-destructive methods, a shock response test method and an analysis method that can efficiently and accurately determine the defects of the piston rod and steering rack. A test method and excitation frequency range that can measure major changes according to the location and degree of defects were proposed. A defect discrimination model was constructed using machine and deep learning through feature derivation in the time and frequency domains for the collected data. The analysis revealed that it was possible to effectively distinguish the characteristics according to the location as well as the presence or absence of defects in the frequency domain rather than the time domain. The results indicate that it will be possible to quickly and accurately check the presence or absence of defects in the shock absorber and steering in the automobile manufacturing process line in the future. It is expected that this will play an important role as a key factor in building a smart factory. Full article
(This article belongs to the Special Issue Sensing and Signal Processing in Nondestructive Evaluation)
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Review

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17 pages, 4687 KiB  
Review
Laser Thermal Wave Diagnostics of the Thermal Resistance of Soldered and Bonded Joints in Semiconductor Structures
by Alexey Glazov and Kyrill Muratikov
Sensors 2023, 23(7), 3590; https://doi.org/10.3390/s23073590 - 30 Mar 2023
Cited by 2 | Viewed by 1320
Abstract
This paper is a review of recent applications of a laser photothermal mirage technique for sensing and measuring the thermal resistance of joint layers in modern electronic devices. A straightforward theoretical model of the interfacial thermal resistance based on the formation of a [...] Read more.
This paper is a review of recent applications of a laser photothermal mirage technique for sensing and measuring the thermal resistance of joint layers in modern electronic devices. A straightforward theoretical model of the interfacial thermal resistance based on the formation of a thin intermediate layer between jointed solids is described. It was experimentally shown that thermal properties of solder layers cannot be evaluated simply on the base of averaging the thermal properties of solder components. The review presents the laser thermal wave methodology for measuring thermal parameters of soldered and adhesively bonded joints. The developed theoretical model makes it possible to carry out a quantitative estimation of local thermal conductivities of joints and their thermal resistances by fitting theoretical results with experimental data obtained by the laser beam deflection method. The joints made with lead-containing and lead-free solders were studied. The anomalous distribution of thermal properties in the solder layer is explained by the diffusion of various atoms detected by energy dispersive X-ray spectroscopy. The laser beam deflection method made it possible to reveal a strong influence of the surface pretreatment quality on the interfacial thermal resistance. Full article
(This article belongs to the Special Issue Sensing and Signal Processing in Nondestructive Evaluation)
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