Effect of carbon fiber reinforcement on the compressive and thermal properties of hollow glass microspheres/epoxy syntactic foam

Zhao, Zehua; Lv, Bing Hai; Liao, Bin; Zhang, Wentao; Yan, Kunlun; Zhang, Jingjie; Wang, Xiaoxu

doi:10.1007/s10853-021-06806-0

Cited by 10 publications

(1 citation statement)

References 39 publications

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“…However, solid buoyancy materials require high temperature to promote curing, and the temperature distribution is uneven during the preparation process. The demolded specimens often have significant thermal residual strain, which can easily cause cracking and warping in high-pressure underwater environments, and affect the reliability of equipment [1]. Even the material is directly damaged during the preparation process, making it difficult to use in practical applications.…”

Section: Introductionmentioning

confidence: 99%

Strain and Temperature Sensing Based on Different Temperature Coefficients fs-FBG Arrays for Intelligent Buoyancy Materials

Tian,

Lou,

Zhang

et al. 2024

Sensors

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The temperature and strain fields monitoring during the preparation process of buoyancy materials, as well as the health status after molding, are important for mastering the mechanical properties of buoyancy materials and ensuring the safety of operators and equipment. This paper proposes a short and high-density femtosecond fiber Bragg grating (fs-FBG) array based on different temperature coefficients fibers. By optimizing the parameters of femtosecond laser point-by-point writing technology, high-performance fs-FBG arrays with millimeter level gating length and millimeter level spatial resolution were prepared on two types of fibers. These were successfully embedded in buoyancy materials to achieve in-situ online monitoring of the curing process and after molding. The experimental results show that the fs-FBG array sensor has good anti-chirp performance and achieves online monitoring of millimeter-level spatial resolution. Intelligent buoyancy materials can provide real-time feedback on the health status of equipment in harsh underwater environments. The system can achieve temperature monitoring with an accuracy of 0.56 °C and deformation monitoring with sub-millimeter accuracy; the error is in the order of micrometers, which is of great significance in the field of deep-sea exploration.

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Section: Introductionmentioning

confidence: 99%

Strain and Temperature Sensing Based on Different Temperature Coefficients fs-FBG Arrays for Intelligent Buoyancy Materials

Tian,

Lou,

Zhang

et al. 2024

Sensors

Self Cite

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Research on defect identification of carbon fiber composite materials based on ultrasonic phased array

Jing,

Cai,

et al. 2024

Polymer Composites

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It is more and more difficult to identify defects in carbon fiber composite materials due to the difficulty in making defect samples and the single signal analysis method. In order to better solve the problem of defect identification in carbon fiber composite materials, this study uses ultrasonic phased array equipment to quantitatively locate and detect carbon fiber composite laminates with embedded delamination defects, so as to more intuitively and effectively display the appearance of different delamination defects. The time domain analysis of the collected ultrasonic original signal and the time‐frequency domain analysis using wavelet packet are carried out. A total of 6 eigenvalues were extracted to reflect the ultrasonic signals of different delamination defects. By using genetic algorithm to optimize BP neural network, the recognition accuracy of delamination defects of different sizes is more than 95%, and the recognition accuracy of delamination defects of different depths is 100%, so as to realize the effective intelligent recognition of delamination defects of different sizes and depths of carbon fiber composites. This study is of great significance to improve the accuracy and reliability of defect identification of carbon fiber composite materials.HighlightsThe ultrasonic phased array equipment is used to quantitatively locate the carbon fiber composite laminates with embedded delamination defects, so that the appearance of different defects can be displayed more intuitively and effectively.Using time domain analysis and time‐frequency domain analysis based on wavelet packet, the combination of the two can more comprehensively extract the effective features of the defect signal.The BP neural network is optimized by genetic algorithm, and the results can effectively and automatically identify different layered defects, which lays a good foundation for the rapid and accurate identification of more defects in the future.

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Development of Heat-Resistant Composite Foam Material

Chukhlanov

Selivanov

Ilina

et al. 2023

Lecture Notes in Networks and Systems

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Effect of carbon fiber reinforcement on the compressive and thermal properties of hollow glass microspheres/epoxy syntactic foam

Cited by 10 publications

References 39 publications

Strain and Temperature Sensing Based on Different Temperature Coefficients fs-FBG Arrays for Intelligent Buoyancy Materials

Strain and Temperature Sensing Based on Different Temperature Coefficients fs-FBG Arrays for Intelligent Buoyancy Materials

Research on defect identification of carbon fiber composite materials based on ultrasonic phased array

Development of Heat-Resistant Composite Foam Material

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