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Damage Analysis of Carbon Fiber-Reinforced Thermoplastic Laminates under Bending Based on Acoustic Emission and Digital Image Correlation

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Abstract

Carbon fiber-reinforced thermoplastic composites have become very popular as a kind of high-performance composite materials, which is widely used to process important structural parts. Monitoring and identifying the deformation and damage process of carbon fiber-reinforced thermoplastic laminates is of great engineering significance. In this paper, the three-point bending process of thermoplastic laminates (PPS/5HS) was studied using acoustic emission (AE) and digital image correlation (DIC). Simultaneously, the AE signals generated during bending were analyzed in time and frequency domain, and the results were compared with those of the thermosetting laminates. Finally, factor analysis (FA) and fuzzy cluster method (FCM) were performed on acoustic emission signals, the damage types and damage accumulation mode of laminates under bending was obtained. The results showed that the AE technique and DIC method can effectively monitor the entire mechanical failure process of PPS/5HS laminates, and FA together with FCM can effectively be used to study the damage types of PPS/5HS laminates and the accumulation process of different damage types.

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Acknowledgment

The authors are grateful for the financial support provided by the Six Talent Peaks Project in Jiangsu Province (Grant No. 2019-KTHY-059).

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Authors and Affiliations

  1. Faculty of Civil Engineering and Mechanics, Jiangsu University, Zhenjiang, 212013, Jiangsu, China

    Weiguo Wu, Changheng Shi & Yingqi Huang

  2. College of Civil Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China

    Can Tang

  3. AECC Beijing Institute of Aeronautical Materials, Beijing, 100095, China

    Yang Yang

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  1. Weiguo Wu
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  2. Changheng Shi
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  4. Can Tang
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Wu, W., Shi, C., Huang, Y. et al. Damage Analysis of Carbon Fiber-Reinforced Thermoplastic Laminates under Bending Based on Acoustic Emission and Digital Image Correlation. J. of Materi Eng and Perform 33, 1411–1423 (2024). https://doi.org/10.1007/s11665-023-08042-w

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  • DOI: https://doi.org/10.1007/s11665-023-08042-w

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