Byeong-Wook Jang

Bio: Byeong-Wook Jang is an academic researcher from Korea Aerospace Research Institute. The author has contributed to research in topics: Fiber Bragg grating & Fiber optic sensor. The author has an hindex of 9, co-authored 21 publications receiving 276 citations. Previous affiliations of Byeong-Wook Jang include KAIST.

Real-time impact identification algorithm for composite structures using fiber Bragg grating sensors

Abstract: SUMMARY In composite structures, low-velocity impact-induced damage such as delamination are mostly hidden inside laminates or leave a small dent on the impact side. Thus, detecting this type of damage using conventional inspection methods is not easy and requires much time and cost. To enhance efficiency of these methods, information on the estimated impact locations must be provided with a high accuracy. In this way, unnecessary inspections for large intact regions can be reduced. In this study, impact localization algorithms for various composite structures were developed using the impact induced acoustic signals acquired by multiplexed fiber Bragg grating (FBG) sensors. The acoustic waves from a given impact were transmitted to each FBG sensor, and incurred FBG wavelength shifts were captured by a high speed multiplexible FBG interrogation system with a sampling frequency of 100 kHz. After acquisition of the FBG sensor signals at all the training points in the target section, the impact wave arrival time differences between each FBG signal were calculated to produce the input data sets for neural network training. To reliably use the neural network algorithm for impact identification, high reproducible arrival time determination algorithms are essentially required. In this study, such arrival time determination algorithms were developed through various types of structures. Finally, we evaluated the performances of the suggested impact identification algorithms for a composite wing box structure. Copyright © 2012 John Wiley & Sons, Ltd.

Real-time detection of low-velocity impact-induced delamination onset in composite laminates for efficient management of structural health

Abstract: Carbon fiber reinforced polymer (CFRP) laminates have been becoming primary structures in the aerospace industry because of their high specific strength and stiffness. However, CFRP laminates have susceptibility to low-velocity impact events which can easily induce internal or hidden damages such as delamination. Such impacts frequently arise during maintenance, flight operation or in-service events. Thus, composite structures have to be irregularly inspected in addition to the periodic maintenance for ensuring the structural health. However, such irregular inspections can inherently incur the overall maintenance cost because it has to be performed in all suspicious cases of damages. For this reason, the methodology for accurately realizing the onset of delamination induced by low-velocity impact events is required for reducing the operating cost of composite structures. In this paper, the potential of using high speed fiber Bragg grating (FBG) sensing system for detecting the delamination onset was studied for thick CFRP laminates. Because FBG sensors can be simply multiplexed to capture the structural responses, the proposed method in this study can be quite attractive for an efficient impact monitoring system. To obtain the impact response signals and contact force histories, several low-velocity impact experiments were performed in a range of 1–30 J. From the signal processing of these experimental data, the meaningful damage index was introduced using the detail components of wavelet transformed sensor signals. Although this result is in the preliminary step, such damage index can be useful for applying an in-situ impact damage assessment system to the real composite structures in the near future.

Impact source localization for composite structures under external dynamic loading condition

Abstract: This paper suggests a real-time impact localization algorithm for a stiffened composite structure using acoustic emission signals acquired by multiplexed fiber Bragg grating (FBG) sensors. The suggested algorithm requires a database composed of premeasured impact signals at all training points to be used as reference signals. Once the FBG signals were measured from an arbitrary impact event, the root mean-squared (RMS) values were calculated between the obtained signals and all reference signals. Then, the training point that has the minimum RMS values was determined as the resultant impact location. In order to validate the applicability of this algorithm in the external environment, impact localization was performed under a dynamic loading condition. The dynamic loading condition was simulated by exciting the test article using a shaker. The results of validation tests show that the proposed algorithm could successively estimate the impact locations under a dynamic loading condition.

Structural health monitoring of civil infrastructure using optical fiber sensing technology: a comprehensive review.

Abstract: In the last two decades, a significant number of innovative sensing systems based on optical fiber sensors have been exploited in the engineering community due to their inherent distinctive advantages such as small size, light weight, immunity to electromagnetic interference (EMI) and corrosion, and embedding capability. A lot of optical fiber sensor-based monitoring systems have been developed for continuous measurement and real-time assessment of diversified engineering structures such as bridges, buildings, tunnels, pipelines, wind turbines, railway infrastructure, and geotechnical structures. The purpose of this review article is devoted to presenting a summary of the basic principles of various optical fiber sensors, innovation in sensing and computational methodologies, development of novel optical fiber sensors, and the practical application status of the optical fiber sensing technology in structural health monitoring (SHM) of civil infrastructure.

Determination of impact location on composite stiffened panels

Abstract: In this work a methodology for impact identification on composite stiffened panels using piezoceramic sensors has been presented. A large number of impacts covering a wide range of energies (corresponding to small and large mass impacts) at various locations of a composite stiffened panel have been simulated using the finite element (FE) method. To predict the impact location, artificial neural networks have been established using the data generated from FE analyses. A number of sensor signal features have been examined as inputs to the neural network and the effect of noise on the predictions has been investigated. The results of the study show that the trained network is capable of locating impacts with different energies at different locations (e.g. in the bay, over/under the stringer and on the foot of the stringer) in a complicated structure such as a composite stiffened panel.

Drones: Innovative Technology for Use in Precision Pest Management

Abstract: Arthropod pest outbreaks are unpredictable and not uniformly distributed within fields. Early outbreak detection and treatment application are inherent to effective pest management, allowing management decisions to be implemented before pests are well-established and crop losses accrue. Pest monitoring is time-consuming and may be hampered by lack of reliable or cost-effective sampling techniques. Thus, we argue that an important research challenge associated with enhanced sustainability of pest management in modern agriculture is developing and promoting improved crop monitoring procedures. Biotic stress, such as herbivory by arthropod pests, elicits physiological defense responses in plants, leading to changes in leaf reflectance. Advanced imaging technologies can detect such changes, and can, therefore, be used as noninvasive crop monitoring methods. Furthermore, novel methods of treatment precision application are required. Both sensing and actuation technologies can be mounted on equipment moving through fields (e.g., irrigation equipment), on (un)manned driving vehicles, and on small drones. In this review, we focus specifically on use of small unmanned aerial robots, or small drones, in agricultural systems. Acquired and processed canopy reflectance data obtained with sensing drones could potentially be transmitted as a digital map to guide a second type of drone, actuation drones, to deliver solutions to the identified pest hotspots, such as precision releases of natural enemies and/or precision-sprays of pesticides. We emphasize how sustainable pest management in 21st-century agriculture will depend heavily on novel technologies, and how this trend will lead to a growing need for multi-disciplinary research collaborations between agronomists, ecologists, software programmers, and engineers.

A convolutional neural network for impact detection and characterization of complex composite structures

Abstract: This paper reports on a novel metamodel for impact detection, localization and characterization of complex composite structures based on Convolutional Neural Networks (CNN) and passive sensing. Methods to generate appropriate input datasets and network architectures for impact localization and characterization were proposed, investigated and optimized. The ultrasonic waves generated by external impact events and recorded by piezoelectric sensors are transferred to 2D images which are used for impact detection and characterization. The accuracy of the detection was tested on a composite fuselage panel which was shown to be over 94%. In addition, the scalability of this metamodelling technique has been investigated by training the CNN metamodels with the data from part of the stiffened panel and testing the performance on other sections with similar geometry. Impacts were detected with an accuracy of over 95%. Impact energy levels were also successfully categorized while trained at coupon level and applied to sub-components with greater complexity. These results validated the applicability of the proposed CNN-based metamodel to real-life application such as composite aircraft parts.