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Nondestructive Evaluation of Material Surfaces: Theory, Techniques and Applications

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A special issue of Coatings (ISSN 2079-6412). This special issue belongs to the section "Surface Characterization, Deposition and Modification".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 24630

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Special Issue Editors

Dr. Vimalathithan Paramsamy Kannan

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Dipartimento di Meccanica Matematica e Management, Politecnico di Bari, Via Orabona 4, 70125 Bari, Italy
Interests: acoustic emission; carbon-fibre-reinforced polymer (CFRP) composites; optical techniques; nondestructive evaluation; thermal degradation kinetics
Special Issues, Collections and Topics in MDPI journals

Dr. Claudia Barile

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Dipartimento di Meccanica, Matematica e Management, Politecnico di Bari, Via Orabona 4, 70125 Bari, Italy
Interests: experimental mechanics; mechanical testing; materials behavior; acoustic emission; CFRP; structural health monitoring; nondestructive evaluation
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Youn ho Cho

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Guest Editor

School of Mechancial Engineering, Pusan National University, Busan 609-735, Korea
Interests: ultrasonic guided wave; nondestructive evaluation; nonlinear ultrasonic NDT
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Young H. Kim

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Guest Editor

School of Mechancial Engineering, Pusan National University, Busan 609-735, Korea
Interests: ultrasonic sensors; measurements; nondestructive materials evaluation

Special Issue Information

Dear Colleagues,

Advances in material surface development have led to a wide range of innovative material surfaces, coatings and thin films. Both the characterization of these material surfaces under static and dynamic loading conditions and the identification of defects represent challenging issues due to the associated dimensional limitations, nonuniform stress distribution, and unexpected system responses. Rapid, accurate and relatively affordable characterization of material surfaces can be achieved through nondestructive evaluation techniques. The available nondestructive techniques can be broadly classified into visual inspection, penetrating radiation, magnetic, electrical, acoustic, chemical, electrochemical, thermal and optical techniques. Choosing the suitable technique for an appropriate application has always been a challenge. This Special Issue aims to collect research and review articles that focus on the different nondestructive evaluation techniques that can be used for evaluating the characteristics of different material surfaces. The topics of interest include but are not limited to the aforementioned nondestructive evaluation techniques. We welcome the submission of original research articles, technical research articles, and review papers related to novel nondestructive evaluation techniques and their application, specifically for material surfaces.

Dr. Vimalathithan Paramsamy Kannan
Dr. Claudia Barile
Prof. Dr. Youn ho Cho
Prof. Dr. Young H. Kim
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. Coatings is an international peer-reviewed open access monthly 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

material surfaces
nondestructive evaluation techniques
ultrasonics
acoustics
shearography
optical techniques
thermography

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Published Papers (10 papers)

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Editorial

Jump to: Research, Review

3 pages, 182 KiB

Open AccessEditorial

Special Issue: Nondestructive Evaluation of Material Surfaces: Theory, Techniques, and Applications

by Vimalathithan Paramsamy Kannan and Claudia Barile

Coatings 2022, 12(7), 960; https://doi.org/10.3390/coatings12070960 - 7 Jul 2022

Cited by 1 | Viewed by 1373

Abstract

Due to the fast-growing industrial world, the demand for characterization tools and techniques has increased equally [...] Full article

(This article belongs to the Special Issue Nondestructive Evaluation of Material Surfaces: Theory, Techniques and Applications)

Research

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18 pages, 6693 KiB

Open AccessArticle

Prediction of Temperature of Liquid Steel in Ladle Using Machine Learning Techniques

by Łukasz Sztangret, Krzysztof Regulski, Monika Pernach and Łukasz Rauch

Coatings 2023, 13(9), 1504; https://doi.org/10.3390/coatings13091504 - 25 Aug 2023

Cited by 4 | Viewed by 2031

Abstract

Maintaining the temperature of liquid steel in the ladle in the required range affects the quality of casted billets, reduces energy consumption, and guarantees smooth control of the melting sequence. Measuring its temperature is a challenging task in industrial settings, often hindered by safety concerns and the expensive nature of equipment. This paper presents models which enable the prediction of the cooling rate of liquid steel for variable production parameters, i.e., steel grade and weight of melt. The models were based on the FEM solution of the Fourier equation, and machine learning approaches such as decision trees, linear regression, and artificial neural networks are utilized. The parameters of the model were identified using data from the monitoring system and inverse analysis. The results of simulations were verified with measurements performed in the production line. Full article

(This article belongs to the Special Issue Nondestructive Evaluation of Material Surfaces: Theory, Techniques and Applications)

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19 pages, 4248 KiB

Open AccessArticle

Multi-Scale Analysis of Terahertz Time-Domain Spectroscopy for Inversion of Thermal Growth Oxide Thickness in Thermal Barrier Coatings

by Rui Li, Dongdong Ye, Jianfei Xu and Jiabao Pan

Coatings 2023, 13(7), 1294; https://doi.org/10.3390/coatings13071294 - 24 Jul 2023

Cited by 2 | Viewed by 1609

Abstract

To address the inverse problem of thermal growth oxide (TGO) thickness in thermal barrier coatings (TBCs), a novel multi-scale analysis (MSA) method based on terahertz time-domain spectroscopy (THz-TDS) is introduced. The proposed method involves a MSA technique based on four wavelet basis functions (db4, sym3, haar, coif3). Informative feature parameters characterizing the TGO thickness were extracted by performing continuous wavelet transform (CWT) and max-pooling operations on representative wavelet coefficients. Subsequently, multi-linear regression and machine learning regression models were employed to predict and assess the wavelet feature parameters. Experimental results revealed a discernible trend in the wavelet feature parameters obtained through CWT and max-pooling in the MSA, wherein the visual representation of TGO thickness initially increases and then gradually decreases. Significant variations in these feature parameters with changes in both thickness and scale enabled the effective inversion of TGO thickness. Building upon this, multi-linear regression and machine learning regression prediction were performed using multi-scale data based on four wavelet basis functions. Partial-scale data were selected for multi-linear regression, while full-scale data were selected for machine learning regression. Both methods demonstrated high accuracy prediction performance. In particular, the haar wavelet basis function exhibited excellent predictive performance, as evidenced by regression coefficients of 0.9763 and 0.9840, further confirming the validity of MSA. Hence, this study effectively presents a feasible method for the inversion problem of TGO thickness, and the analysis confirms the promising application potential of terahertz time-domain spectroscopy’s multi-scale analysis in the field of TBCs evaluation. These findings provide valuable insights for further reference. Full article

(This article belongs to the Special Issue Nondestructive Evaluation of Material Surfaces: Theory, Techniques and Applications)

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20 pages, 3004 KiB

Open AccessArticle

Data-Driven Method for Porosity Measurement of Thermal Barrier Coatings Using Terahertz Time-Domain Spectroscopy

by Dongdong Ye, Rui Li, Jianfei Xu and Jiabao Pan

Coatings 2023, 13(6), 1060; https://doi.org/10.3390/coatings13061060 - 7 Jun 2023

Cited by 3 | Viewed by 1982

Abstract

Accurate measurement of porosity is crucial for comprehensive performance evaluation of thermal barrier coatings (TBCs) on aero-engine blades. In this study, a novel data-driven predictive method based on terahertz time-domain spectroscopy (THz-TDS) was proposed. By processing and extracting features from terahertz signals, multivariate parameters were composed to characterize the porosity. Principal component analysis, which enabled effective representation of the complex signal information, was introduced to downscale the dimensionality of the time-domain data. Additionally, the average power spectral density of the frequency spectrum and the extreme points of the first-order derivative of the phase spectrum were extracted. These extracted parameters collectively form a comprehensive set of multivariate parameters that accurately characterize porosity. Subsequently, the multivariate parameters were used as inputs to construct an extreme learning machine (ELM) model optimized by the sparrow search algorithm (SSA) for predicting porosity. Based on the experimental results, it was evident that the predictive accuracy of SSA-ELM was significantly higher than the basic ELM. Furthermore, the robustness of the model was evaluated through K-fold cross-validation and the final model regression coefficient was 0.92, which indicates excellent predictive performance of the data-driven model. By introducing the use of THz-TDS and employing advanced signal processing techniques, the data-driven model provided a novel and effective solution for the rapid and accurate detection of porosity in TBCs. The findings of this study offer valuable references for researchers and practitioners in the field of TBCs inspection, opening up new avenues for improving the overall assessment and performance evaluation of these coatings. Full article

(This article belongs to the Special Issue Nondestructive Evaluation of Material Surfaces: Theory, Techniques and Applications)

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13 pages, 4386 KiB

Open AccessFeature PaperArticle

Nondestructive Inspection of Underwater Coating Layers Using Ultrasonic Lamb Waves

by Jiannan Zhang, Younho Cho, Jeongnam Kim, Azamatjon Kakhramon ugli Malikov, Young H. Kim and Jin-Hak Yi

Coatings 2023, 13(4), 728; https://doi.org/10.3390/coatings13040728 - 3 Apr 2023

Cited by 23 | Viewed by 2155

Abstract

Coatings play a crucial role in protecting ships and marine structures from corrosion and extending their service life. The reliability of these coatings depends on their proper maintenance, which in turn, relies on the application of reliable diagnostic techniques. Non-destructive testing (NDT) techniques are useful in material diagnostics, such as detecting debonded zone in water. However, the challenging access environment in the ocean, and the high attenuation characteristics of the material itself add too many technical challenges. In this paper, we propose a guided wave-based technique for characterizing the bonded zone state of coatings, which uses FFT analysis in different bonded zone states. The proposed technique has been demonstrated to be effective in characterizing the bonded zone state of water coatings through numerical and experimental results. Full article

(This article belongs to the Special Issue Nondestructive Evaluation of Material Surfaces: Theory, Techniques and Applications)

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16 pages, 2664 KiB

Open AccessArticle

Nondestructive Evaluation of Thermal Barrier Coatings Thickness Using Terahertz Time-Domain Spectroscopy Combined with Hybrid Machine Learning Approaches

by Rui Li, Dongdong Ye, Zhou Xu, Changdong Yin, Huachao Xu, Haiting Zhou, Jianwu Yi, Yajuan Chen and Jiabao Pan

Coatings 2022, 12(12), 1875; https://doi.org/10.3390/coatings12121875 - 2 Dec 2022

Cited by 11 | Viewed by 2147

Abstract

To ensure the thermal stability of aero-engine blades under high temperature and harsh service environments, it is necessary to quickly and accurately evaluate the thickness of thermal barrier coatings (TBCs). In this work, it was proposed to use the terahertz nondestructive testing (NDT) technique combined with the hybrid machine learning algorithm to measure the thickness of TBCs. The finite difference time-domain (FDTD) method was used to model the optical propagation characteristics of TBC samples with different thicknesses (101–300 μm) in the frequency band. To make the terahertz time-domain signal obtained simulation more realistic, uniform white noise was added to the simulation data and wavelet denoising was conducted to mimic the real testing environment. Principal components analysis (PCA) algorithm and whale optimization algorithm (WOA) combined with an optimized Elman neural network algorithm was employed to set up the hybrid machine learning model. Finally, the hybrid thickness regression prediction model shows low error, high accuracy, and an exceptional coefficient of determination R² of 0.999. It was demonstrated that the proposed hybrid algorithm could meet the thickness evaluation requirements. Meanwhile, a novel, efficient, safe, and accurate terahertz nondestructive testing method has shown great potential in the evaluation of structural integrity of thermal barrier coatings in the near future. Full article

(This article belongs to the Special Issue Nondestructive Evaluation of Material Surfaces: Theory, Techniques and Applications)

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18 pages, 4645 KiB

Open AccessArticle

Data-Driven Model Selection for Compacted Graphite Iron Microstructure Prediction

by Grzegorz Gumienny, Barbara Kacprzyk, Barbara Mrzygłód and Krzysztof Regulski

Coatings 2022, 12(11), 1676; https://doi.org/10.3390/coatings12111676 - 4 Nov 2022

Cited by 4 | Viewed by 1452

Abstract

Compacted graphite iron (CGI), having a specific graphite form with a large matrix contact surface, is a unique casting material. This type of cast iron tends to favor direct ferritization and is characterized by a complex of very interesting properties. Intelligent computing tools such as artificial neural networks (ANNs) are used as predictive modeling tools, allowing their users to forecast the microstructure of the tested cast iron at the level of computer simulation. This paper presents the process of the development of a metamodel for the selection of a neural network appropriate for a specific chemical composition. Predefined models for the specific composition have better precision, and the initial selection provides the user with automation of reasoning and prediction. Automation of the prediction is based on the rules obtained from the decision tree, which classifies the type of microstructure. In turn, the type of microstructure was obtained by clustering objects of different chemical composition. The authors propose modeling the prediction of the volume fraction of phases in the CGI microstructure in a three-step procedure. In the first phase, k-means, unsupervised segmentation techniques were used to determine the metamodel (DT), which in the second phase enables the selection of the appropriate ANN submodel (third phase). Full article

(This article belongs to the Special Issue Nondestructive Evaluation of Material Surfaces: Theory, Techniques and Applications)

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20 pages, 3588 KiB

Open AccessArticle

Assessment of Defects under Insulation Using K-Medoids Clustering Algorithm-Based Microwave Nondestructive Testing

by Shin Yee Tan, Muhammad Firdaus Akbar, Nawaf H. M. M. Shrifan, Ghassan Nihad Jawad and Mohd Nadhir Ab Wahab

Coatings 2022, 12(10), 1440; https://doi.org/10.3390/coatings12101440 - 30 Sep 2022

Cited by 8 | Viewed by 2084

Abstract

Composite insulations, such as ceramics, are commonly utilized in the turbine system as a thermal coating barrier to protect the metal substrate against high temperatures and pressure. The presence of delamination in the composite insulations may cause turbine failure, leading to a catastrophic accident. Thus, regular non-destructive testing is required to detect and evaluate insulation defects. Among the non-destructive testing techniques, the microwave technique has emerged as a promising method for assessing defects in ceramic coatings. Although the method is promising, microwave non-destructive testing suffers from poor spatial imaging, making the defect assessment challenging. In this paper, a novel technique based on microwave non-destructive testing with a k-medoids clustering algorithm for delamination detection is proposed. The representative ceramic coating sample is scanned using a Q-band open-ended rectangular waveguide with 101 frequency points that operated between 33 to 50 GHz. The measured data is transformed from the frequency domain to the time domain using an inverse fast Fourier transform. The principal component analysis is then used to reduce the dimensionality of 101 time steps into only 3 dominant attributes. The attributes of each inspected location are classified as defect or defect-free using the k-medoids clustering algorithm for accurately detecting and sizing the defects in the ceramic insulation. The results reported in this paper highlight the superiority of the k-medoids clustering algorithm in delamination detection, with an accuracy rate of 95.4%. This is a significant step forward compared to earlier approaches for identifying ceramic defects. Full article

(This article belongs to the Special Issue Nondestructive Evaluation of Material Surfaces: Theory, Techniques and Applications)

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Review

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18 pages, 1919 KiB

Open AccessReview

Nondestructive Evaluation of Fiber-Reinforced Polymer Using Microwave Techniques: A Review

by Danladi Agadi Tonga, Muhammad Firdaus Akbar, Nawaf H. M. M. Shrifan, Ghassan Nihad Jawad, Nor Azlin Ghazali, Mohamed Fauzi Packeer Mohamed, Ahmed Jamal Abdullah Al-Gburi and Mohd Nadhir Ab Wahab

Coatings 2023, 13(3), 590; https://doi.org/10.3390/coatings13030590 - 9 Mar 2023

Cited by 6 | Viewed by 2666

Abstract

Carbon-fiber-reinforced polymer (CFRP) is widely acknowledged as a leading advanced material structure, offering superior properties compared to traditional materials, and has found diverse applications in several industrial sectors, such as that of automobiles, aircrafts, and power plants. However, the production of CFRP composites is prone to fabrication problems, leading to structural defects arising from cycling and aging processes. Identifying these defects at an early stage is crucial to prevent service issues that could result in catastrophic failures. Hence, routine inspection and maintenance are crucial to prevent system collapse. To achieve this objective, conventional nondestructive testing (NDT) methods are utilized to inspect CFRP components. However, the restricted field penetration within the CFRP makes conventional NDT approaches ineffective. Recently, microwave techniques have been developed to address the challenges associated with CFRP inspection by providing better material penetration and more precise results. This paper offers a review of the primary NDT methods employed to inspect CFRP composites, emphasizing microwave-based NDT techniques and their key features. Full article

(This article belongs to the Special Issue Nondestructive Evaluation of Material Surfaces: Theory, Techniques and Applications)

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22 pages, 2292 KiB

Open AccessReview

Nondestructive Testing Technologies for Rail Inspection: A Review

by Wendong Gong, Muhammad Firdaus Akbar, Ghassan Nihad Jawad, Mohamed Fauzi Packeer Mohamed and Mohd Nadhir Ab Wahab

Coatings 2022, 12(11), 1790; https://doi.org/10.3390/coatings12111790 - 21 Nov 2022

Cited by 18 | Viewed by 6237

Abstract

Alongside the development of high-speed rail, rail flaw detection is of great importance to ensure railway safety, especially for improving the speed and load of the train. Several conventional inspection methods such as visual, acoustic, and electromagnetic inspection have been introduced in the past. However, these methods have several challenges in terms of detection speed and accuracy. Combined inspection methods have emerged as a promising approach to overcome these limitations. Nondestructive testing (NDT) techniques in conjunction with artificial intelligence approaches have tremendous potential and viability because it is highly possible to improve the detection accuracy which has been proven in various conventional nondestructive testing techniques. With the development of information technology, communication technology, and sensor technology, rail health monitoring systems have been evolving, and have become equally significant and challenging because they can achieve real-time detection and give a risk warning forecast. This paper provides an in-depth review of traditional nondestructive techniques for rail inspection as well as the development of using machine learning approaches, combined nondestructive techniques, and rail health monitoring systems. Full article

(This article belongs to the Special Issue Nondestructive Evaluation of Material Surfaces: Theory, Techniques and Applications)

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