journal article·2021-01

Advances, limitations and prospects of nondestructive testing and evaluation of thick composites and sandwich structures: A state-of-the-art review

Walter Nsengiyumva, Shuncong Zhong, Jiewen Lin, Qiukun Zhang, Jianfeng Zhong, Yuexin Huang

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Abstract

Abstract In recent years, composite materials have gained popularity in numerous high-tech and engineering applications, owing to their outstanding physico-mechanical properties. They were initially used as fairings/reinforcements for different structures, but their application has recently shifted from general-purpose structures to primary and secondary load-bearing structures, where structural failures would result in catastrophic safety repercussions. This increased scope of application has prompted the introduction of composite structures featuring significant thickness and complexity. As such, the application of nondestructive testing and evaluation (NDT&E) to localize and characterize flaws in these materials at their incipient initiations could save resources, eliminate unplanned breakdown, and provide a timely window for repair-maintenance activities. Therefore, this paper critically reviews the recent advances in NDT&E as applied to the inspection of thick composite parts and sandwich structures (composites with a thickness ≥15 mm) and determines possible research prospects to address the limitations of the current technologies. A brief overview of defects/damage occurring in composite structures is provided followed by the main NDT&E techniques used to detect these flaws. Since there are many NDT&E techniques available, this work limits its scope on techniques that focus on the detection, localization, and characterization of flaws in thick composites and sandwich structures.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Ultrasonics and Acoustic Wave Propagation · Non-Destructive Testing Techniques · Thermography and Photoacoustic Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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