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Jinpeng Bai

Bio: Jinpeng Bai is an academic researcher. The author has contributed to research in topics: Terahertz time-domain spectroscopy & Nondestructive testing. The author has an hindex of 1, co-authored 1 publications receiving 15 citations.

Papers
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Journal ArticleDOI
TL;DR: In this article, the authors investigated the efficacy of terahertz (THz) time-domain spectroscopy (TDS) imaging technology in detecting hidden defects in aircraft glass fiber (GF) sandwich composites.

32 citations


Cited by
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TL;DR: In this article, the authors present recent advances in non-destructive testing and evaluation (NDT&E) and in-situ structural health monitoring (SHM) techniques for damage detection in fiber-reinforced polymer (FRP) composites.
Abstract: The application of fiber-reinforced polymer (FRP) composites is continuously increasing due to their superior mechanical properties and the associated weight advantage. However, they are susceptible to more complex types of damage, and advanced damage characterization systems are required to prevent catastrophic failures. Various non-destructive testing and evaluation (NDT&E) and in-situ structural health monitoring (SHM) techniques have been applied for damage detection in FRP composites. These techniques have been continuously developed to achieve reliable inspections, especially for safety-critical applications such as the aerospace industry. This review presents recent advances in NDT&E techniques and SHM techniques, particularly for damage diagnosis in FRP composites. For selecting the most suitable NDT technique based on specific criteria, the analytical hierarchy process is applied as a decision-making tool to evaluate and rank the NDT techniques. The size of the specimen is found to be the most important criterion that significantly affects technique selection. Finally, the importance of developing in-situ SHM systems is outlined, and different in-situ SHM systems are then reviewed and discussed. This review provides progress of the recent damage characterization techniques and enables researchers to devise selection criteria to select the most appropriate technique for their own work.

38 citations

Journal ArticleDOI
TL;DR: In this paper, a series of static and dynamic tensile tests are carried out to understand the influence of different loading conditions on failure and fracture of honeycomb sandwich T-joints within strain rates up to 5000 1−1.
Abstract: Applications of sandwich-structured composites gained increasing interest in aviation and aerospace industries as well as in modern lightweight design. In order to improve the reliability of computational models for the dimensioning of such structures, experimental data are indispensable prerequisites. In the current manuscript, the essential outcomes of experimental and numerical investigations of strain rate effects on the stiffness of honeycomb sandwich T-joints are presented. A series of static and dynamic tensile tests is carried out to understand the influence of different loading conditions on failure and fracture of honeycomb sandwich T-joints within strain rates up to 5000 s−1. A digital image correlation technique was employed to characterize the fracture behavior as well as to verify the strain gauges measurements. Failure modes of adhesively bonded sandwich T-joints are determined by a detailed fractographic analysis. In addition, numerical simulation was performed via three-dimensional finite element models using ABAQUS software, which exhibit excellent agreement with experimental results.

32 citations

Journal ArticleDOI
TL;DR: In this paper, an improved imaging method for detecting bonding defects is proposed based on the statistical characteristics of variance and kurtosis to detect defects in the upper/lower adhesive layers in a multilayered CMC.

24 citations

Journal ArticleDOI
TL;DR: A novel approach to predict the defect depths in GFRP based on terahertz time-domain spectroscopy signal analysis with neural networks is reported, which shows that in general the one-dimension convolutional neural network model outperforms the long-short term memory recurrent neural network and the bidirectional LSTM-RNN models.

11 citations

Journal ArticleDOI
TL;DR: In this article, two main types commonly used modified natural rubber, carbon black filled and silica filled, were studied using terahertz (THz) dielectric spectroscopic technique during their thermal aging.

10 citations