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Smart Composite and Sensors

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

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 21686

Special Issue Editors

Prof. Dr. Heung Soo Kim

E-Mail Website1 Website2
Guest Editor
Department of Mechanical, Robotics and Energy Engineering, Dongguk University, 30 Pil-dong 1 Gil, Jung-gu, Seoul 04620, Korea
Interests: Prognostics and Health Management (PHM), Artificial Intelligence, Biomimetic Actuator, Adaptive Structures, Structural Analysis, Structural Optimization, Numerical Analysis, Composite Structures
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jun-Sik Kim

E-Mail Website
Guest Editor
Department of Mechanical System Engineering, Kumoh National Institue of Technology, 61 Daehak-ro, Gumi, Gyeongbuk 39177, Korea
Interests: smart materials and structures; smart actuators and sensors; mechanics of composite structures; computational mechanics; aeroelasticity; elasticity; rotorcraft dynamics
Prof. Dr. Jaehun Lee

E-Mail Website
Guest Editor
Department of Mechanical, Robotics and Energy Engineering, Dongguk University, 30 Pil-dong 1 Gil, Jung-gu, Seoul 04620, Korea
Interests: smart composites and structures; computational mechanics; reduced-order modeling; mechanics of composite structures; structural design optimization for large-scale dynamical systems; domain decomposition and substructuring schemes
Prof.Dr. Asif Khan

E-Mail Website
Guest Editor
Department of Mechanical, Robotics and Energy Engineering, Dongguk University, 30 Pil-dong 1 Gil, Jung-gu, Seoul 04620, Korea
Interests: mechanics of smart composite laminates; applications of smart materials; machine learning for structural health monitoring; modeling and simulation of dynamic behaviour of engineering systems; finite element methods; energy harvesting from ambient resources
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

For the last two decades, sensors and actuators have been combined with composite materials to enhance their functionalities and make them intelligent, adaptive, or smart structures. Commonly enhanced functionalities of smart composites compared with conventional composites are but not limited to vibration control, shape control, self-sensing, self-diagnostic, reaction to changing environmental conditions, and energy harvesting. In smart composites, sensors could be integrated with the host composite in the form of discrete patches or sensing networks. Additionally, many materials could be employed as sensors in smart composites such as piezoelectric, fiber-optics, shape-memory alloys, etc. This Special Issue aims to disseminate theoretical and experimental work from esteemed professionals like yourself on Smart Composites and Sensors.

You are cordially invited to submit your original research articles and review papers that contain theoretical and experimental investigations of all aspects of smart composite materials and sensors in the current Special Issue. The list of potential research topics includes but is not limited to:

  • Modeling and analysis of smart composites;
  • Characterizing smart elements as sensors in composite materials;
  • Structural health monitoring of smart composites;
  • Nondestructive evaluation of smart composites;
  • Static analysis of smart composites;
  • Dynamic analysis of smart composites;
  • Modal analysis of smart composites;
  • Vibration control of smart composites;
  • Shape control of smart composites;
  • Energy harvesting from smart composites;
  • Manufacturing of smart composites;
  • Interdisciplinary approaches and applications for smart composites;
  • Nanosensors for composites;
  • Embedded/surface bonded network of sensors in smart composites;
  • Data-driven manufacturing for SHM of smart composites;
  • Advanced control of smart composites.

Prof. Dr. Heung Soo Kim
Prof. Dr. Jun-Sik Kim
Prof. Dr. Jae Hun Lee
Dr. Asif Khan
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.

Keywords

  • Smart composites
  • Sensors
  • Piezoelectrics
  • Optical fiber sensor
  • Polymer-based sensing
  • Shape memory alloys
  • Smart/intelligent structures
  • Artificial intelligence

Published Papers (7 papers)

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Research

23 pages, 6145 KiB  
Article
Analysis of Air-Coupled Transducer-Based Elastic Waves Generation in CFRP Plates
by Tomasz Wandowski, Damian Mindykowski, Pawel Kudela and Maciej Radzienski
Sensors 2021, 21(21), 7134; https://doi.org/10.3390/s21217134 - 27 Oct 2021
Cited by 11 | Viewed by 1677
Abstract
In this paper, the analysis of non-contact elastic waves generation in carbon fiber reinforced-polymer (CFRP) plate was conducted. Full non-contact elastic waves generation and sensing methods were also analyzed. Elastic waves generation was based on an air-coupled transducer (ACT) while waves sensing was [...] Read more.
In this paper, the analysis of non-contact elastic waves generation in carbon fiber reinforced-polymer (CFRP) plate was conducted. Full non-contact elastic waves generation and sensing methods were also analyzed. Elastic waves generation was based on an air-coupled transducer (ACT) while waves sensing was based on a laser Doppler vibrometer. The excitation frequency was equal to 40 kHz. An optimal ACT slope angle for the generation of elastic waves mode was determined with the aid of dispersion curves calculated by using a semi-analytical model. Due to the stack sequence in the composite plate (unidirectional composite), ACT slope angles were different for waves generation in the direction along and across reinforcing fibers direction. Moreover, experimental verification of the optimal ACT slope angles was conducted. It was possible to generate A0 wave mode in the direction along and across the reinforcing fibers. Optimal angles determined using ACT were equal to 16° (along fibers) and 34° (across fibers). In the case of optimal angles, elastic waves amplitudes are almost two times higher than for the case of ACT oriented perpendicularly to the plate surface. Moreover, experimental results based on ACT showed that it was possible to generate the SH0 mode in the direction across the fiber for optimal angles equal to 10°. Finally, based on the A0 wave mode propagation, the process for localization of discontinuities was performed. Discontinuities in the form of additional mass simulating damage were investigated. A simple signal processing algorithm based on elastic wave energy was used for creating damage maps. Authors compared discontinuity localization for ACT oriented perpendicularly to the plate and at the optimal slope angle. The utilization of non-contact waves excitation at optimal ACT slope angles helped to focus the wave energy in the desired direction. Moreover, in this case, elastic waves with the highest amplitudes were generated. Full article
(This article belongs to the Special Issue Smart Composite and Sensors)
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18 pages, 9092 KiB  
Article
Design and Integrated Analysis of a Flexible Support Microstructure with a Honeycomb Sandwich for the Optical Window of a Hypersonic Remote Sensor
by Mingqiang Zhang, Yaobin Li, Yalin Ding, Jianjun Sun and Jing Li
Sensors 2021, 21(17), 5919; https://doi.org/10.3390/s21175919 - 02 Sep 2021
Cited by 1 | Viewed by 1831
Abstract
In order to reduce the influence of the optical window on the image quality of a hypersonic visible light optical remote sensor, we propose a method of adding a double-layer semicircular honeycomb core microstructure with flexible support of a high temperature elastic alloy [...] Read more.
In order to reduce the influence of the optical window on the image quality of a hypersonic visible light optical remote sensor, we propose a method of adding a double-layer semicircular honeycomb core microstructure with flexible support of a high temperature elastic alloy between a window glass and a frame to reduce the influence of complex thermal stress on the surface accuracy of the optical window. An equivalent model of a semicircular honeycomb structure was established, the elastic parameters of the semicircular honeycomb sandwich microstructure were derived by an analytical method, and a numerical verification and finite element simulation were carried out. The results show that the equivalent model is in good agreement with the detailed model. The optical-mechanical-thermal integrated simulation analysis of the optical window assembly with flexible supporting microstructure proves that the semicircular honeycomb sandwich flexible supporting structure has a positive effect on stress attenuation of the window glass and ensures the wave aberration accuracy of the transmitted optical path difference of the optical window (PV < 0.665 λ, RMS < 0.156 λ, λ = 632.8 nm). Combined with the actual optical system, the optical performance of the window assembly under the flexible support structure is verified. The simulation results show that the spatial frequency of the modulation transfer function (MTF) of the optical system after focusing is not less than 0.58 in the range of 0–63 cycle/mm and the relative decline of MTF is not more than 0.01, which meet the imaging requirements of a remote sensor. The study results show that the proposed metal-based double-layer semicircular honeycomb sandwich flexible support microstructure ensures the imaging quality of the optical window under ultra-high temperature conditions. Full article
(This article belongs to the Special Issue Smart Composite and Sensors)
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19 pages, 11196 KiB  
Article
Numerical Investigation of Ferrofluid Preparation during In-Vitro Culture of Cancer Therapy for Magnetic Nanoparticle Hyperthermia
by Izaz Raouf, Piotr Gas and Heung Soo Kim
Sensors 2021, 21(16), 5545; https://doi.org/10.3390/s21165545 - 18 Aug 2021
Cited by 15 | Viewed by 2731
Abstract
Recently, in-vitro studies of magnetic nanoparticle (MNP) hyperthermia have attracted significant attention because of the severity of this cancer therapy for in-vivo culture. Accurate temperature evaluation is one of the key challenges of MNP hyperthermia. Hence, numerical studies play a crucial role in [...] Read more.
Recently, in-vitro studies of magnetic nanoparticle (MNP) hyperthermia have attracted significant attention because of the severity of this cancer therapy for in-vivo culture. Accurate temperature evaluation is one of the key challenges of MNP hyperthermia. Hence, numerical studies play a crucial role in evaluating the thermal behavior of ferrofluids. As a result, the optimum therapeutic conditions can be achieved. The presented research work aims to develop a comprehensive numerical model that directly correlates the MNP hyperthermia parameters to the thermal response of the in-vitro model using optimization through linear response theory (LRT). For that purpose, the ferrofluid solution is evaluated based on various parameters, and the temperature distribution of the system is estimated in space and time. Consequently, the optimum conditions for the ferrofluid preparation are estimated based on experimental and mathematical findings. The reliability of the presented model is evaluated via the correlation analysis between magnetic and calorimetric methods for the specific loss power (SLP) and intrinsic loss power (ILP) calculations. Besides, the presented numerical model is verified with our experimental setup. In summary, the proposed model offers a novel approach to investigate the thermal diffusion of a non-adiabatic ferrofluid sample intended for MNP hyperthermia in cancer treatment. Full article
(This article belongs to the Special Issue Smart Composite and Sensors)
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17 pages, 3492 KiB  
Article
Understanding Covalent Grafting of Nanotubes onto Polymer Nanocomposites: Molecular Dynamics Simulation Study
by Seunghwa Yang
Sensors 2021, 21(8), 2621; https://doi.org/10.3390/s21082621 - 08 Apr 2021
Cited by 7 | Viewed by 2752
Abstract
Here, we systematically interrogate the effects of grafting single-walled (SWNT) and multi-walled carbon nanotubes (MWNT) to polymer matrices by using molecular dynamics (MD) simulations. We specifically investigate key material properties that include interfacial load transfer, alteration of nanotube properties, and dispersion of nanotubes [...] Read more.
Here, we systematically interrogate the effects of grafting single-walled (SWNT) and multi-walled carbon nanotubes (MWNT) to polymer matrices by using molecular dynamics (MD) simulations. We specifically investigate key material properties that include interfacial load transfer, alteration of nanotube properties, and dispersion of nanotubes in the polymer matrix. Simulations are conducted on a periodic unit cell model of the nanocomposite with a straight carbon nanotube and an amorphous polyethylene terephthalate (PET) matrix. For each type of nanotube, either 0%, 1.55%, or 3.1% of the carbon atoms in the outermost nanotubes are covalently grafted onto the carbon atoms of the PET matrix. Stress-strain curves and the elastic moduli of nanotubes and nanocomposites are determined based on the density of covalent grafting. Covalent grafting promotes two rivalling effects with respect to altering nanotube properties, and improvements in interfacial load transfer in the nanocomposites are clearly observed. The enhanced interface enables external loads applied to the nanocomposites to be efficiently transferred to the grafted nanotubes. Covalent functionalization of the nanotube surface with PET molecules can alter the solubility of nanotubes and improve dispersibility. Finally, we discuss the current limitations and challenges in using molecular modelling strategies to accurately predict properties on the nanotube and polymers systems studied here. Full article
(This article belongs to the Special Issue Smart Composite and Sensors)
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12 pages, 3019 KiB  
Communication
Rapid Fabrication of Renewable Carbon Fibres by Plasma Arc Discharge and Their Humidity Sensing Properties
by Yi Chen, Fang Fang, Robert Abbel, Meeta Patel and Kate Parker
Sensors 2021, 21(5), 1911; https://doi.org/10.3390/s21051911 - 09 Mar 2021
Cited by 3 | Viewed by 2475
Abstract
Submicron-sized carbon fibres have been attracting research interest due to their outstanding mechanical and electrical properties. However, the non-renewable resources and their complex fabrication processes limit the scalability and pose difficulties for the utilisation of these materials. Here, we investigate the use of [...] Read more.
Submicron-sized carbon fibres have been attracting research interest due to their outstanding mechanical and electrical properties. However, the non-renewable resources and their complex fabrication processes limit the scalability and pose difficulties for the utilisation of these materials. Here, we investigate the use of plasma arc technology to convert renewable electrospun lignin fibres into a new kind of carbon fibre with a globular and porous microstructure. The influence of arc currents (up to 60 A) on the structural and morphological properties of as-prepared carbon fibres is discussed. Owing to the catalyst-free synthesis, high purity micro-structured carbon fibres with nanocrystalline graphitic domains are produced. Furthermore, the humidity sensing characteristics of the treated fibres at room temperature (23 °C) are demonstrated. Sensors produced from these carbon fibres exhibit good humidity response and repeatability in the range of 30% to 80% relative humidity (RH) and an excellent sensitivity (0.81/%RH) in the high RH regime (60–80%). These results demonstrate that the plasma arc technology has great potential for the development of sustainable, lignin-based carbon fibres for a broad range of application in electronics, sensors and energy storage. Full article
(This article belongs to the Special Issue Smart Composite and Sensors)
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30 pages, 21611 KiB  
Article
Rotate Vector (RV) Reducer Fault Detection and Diagnosis System: Towards Component Level Prognostics and Health Management (PHM)
by Ali Rohan, Izaz Raouf and Heung Soo Kim
Sensors 2020, 20(23), 6845; https://doi.org/10.3390/s20236845 - 30 Nov 2020
Cited by 28 | Viewed by 4652
Abstract
In prognostics and health management (PHM), the majority of fault detection and diagnosis is performed by adopting segregated methodology, where electrical faults are detected using motor current signature analysis (MCSA), while mechanical faults are detected using vibration, acoustic emission, or ferrography analysis. This [...] Read more.
In prognostics and health management (PHM), the majority of fault detection and diagnosis is performed by adopting segregated methodology, where electrical faults are detected using motor current signature analysis (MCSA), while mechanical faults are detected using vibration, acoustic emission, or ferrography analysis. This leads to more complicated methods for overall fault detection and diagnosis. Additionally, the involvement of several types of data makes system management difficult, thus increasing computational cost in real-time. Aiming to resolve that, this work proposes the use of the embedded electrical current signals of the control unit (MCSA) as an approach to detect and diagnose mechanical faults. The proposed fault detection and diagnosis method use the discrete wavelet transform (DWT) to analyze the electric motor current signals in the time-frequency domain. The technique decomposes current signals into wavelets, and extracts distinguishing features to perform machine learning (ML) based classification. To achieve an acceptable level of classification accuracy for ML-based classifiers, this work extends to presenting a methodology to extract, select, and infuse several types of features from the decomposed wavelets of the original current signals, based on wavelet characteristics and statistical analysis. The mechanical faults under study are related to the rotate vector (RV) reducer mechanically coupled to electric motors of the industrial robot Hyundai Robot YS080 developed by Hyundai Robotics Co. The proposed approach was implemented in real-time and showed satisfying results in fault detection and diagnosis for the RV reducer, with a classification accuracy of 96.7%. Full article
(This article belongs to the Special Issue Smart Composite and Sensors)
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17 pages, 7834 KiB  
Article
Intelligent Steam Power Plant Boiler Waterwall Tube Leakage Detection via Machine Learning-Based Optimal Sensor Selection
by Salman Khalid, Woocheol Lim, Heung Soo Kim, Yeong Tak Oh, Byeng D. Youn, Hee-Soo Kim and Yong-Chae Bae
Sensors 2020, 20(21), 6356; https://doi.org/10.3390/s20216356 - 07 Nov 2020
Cited by 16 | Viewed by 4043
Abstract
Boiler waterwall tube leakage is the most probable cause of failure in steam power plants (SPPs). The development of an intelligent tube leak detection system can increase the efficiency and reliability of modern power plants. The idea of e-maintenance based on multivariate algorithms [...] Read more.
Boiler waterwall tube leakage is the most probable cause of failure in steam power plants (SPPs). The development of an intelligent tube leak detection system can increase the efficiency and reliability of modern power plants. The idea of e-maintenance based on multivariate algorithms was recently introduced for intelligent fault detection and diagnosis in SPPs. However, these multivariate algorithms are highly dependent on the number of input process variables (sensors). Therefore, this work proposes a machine learning-based model integrated with an optimal sensor selection scheme to analyze boiler waterwall tube leakage. Finally, a real SPP test case is employed to validate the proposed model’s effectiveness. The results indicate that the proposed model can successfully detect waterwall tube leakage with improved accuracy vs. other comparable models. Full article
(This article belongs to the Special Issue Smart Composite and Sensors)
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