Advancing Damage Assessment of CFRP-Composite through BILSTM and Hilbert Upper Envelope Analysis

IF 0.9 4区 材料科学 Q4 MATERIALS SCIENCE, CHARACTERIZATION & TESTING
M. Frik, T. Benkedjouh, A. Bouzar Essaidi, F. Boumediene
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引用次数: 0

Abstract

The aerospace and automotive sectors widely use carbon fiber reinforced plastic because of its exceptional properties, including its high specific modulus, strength, and resistance to fatigue. However, defects such as cracks in the matrix, separation of layers, and separation from bonding can occur during manufacturing and low-velocity impacts, often remaining undetected. As these defects worsen over time, they can significantly weaken the material. To reduce the risk of major failures, regular assessments of carbon fiber reinforced plastic structures are crucial. This study introduces a structural health monitoring technique that minimizes human involvement while effectively tracking the growth of damage in carbon fiber reinforced plastic structures. The approach employs the acoustic emission method and the hilbert transform technique to identify and quantify the progression of damage in carbon fiber reinforced plastic materials. Experimental outcomes from a fatigue test conducted on cross-ply laminates are presented. To precisely predict damage and evaluate the condition of the composite specimen, researchers use the bidirectional long short-term memory model alongside envelope analysis for forecasting. The suggested method achieves a root mean square error of less than 0.03, proving its capability to precisely predict damage and evaluate the condition of the Composite structure. This novel deep learning-driven method adeptly captures the deterioration in performance of carbon fiber reinforced plastic, enhancing predictive accuracy.

Abstract Image

Abstract Image

通过 BILSTM 和希尔伯特上包络分析推进 CFRP 复合材料的损伤评估
摘要由于碳纤维增强塑料具有高比模量、高强度和抗疲劳性等优异性能,因此在航空航天和汽车领域得到广泛应用。然而,在制造和低速撞击过程中,可能会出现基体裂纹、层间分离和粘合分离等缺陷,而且这些缺陷往往不会被发现。随着时间的推移,这些缺陷会逐渐恶化,严重削弱材料的强度。为降低重大故障风险,定期评估碳纤维增强塑料结构至关重要。本研究介绍了一种结构健康监测技术,它在有效跟踪碳纤维增强塑料结构损伤增长的同时,最大程度地减少了人工参与。该方法采用声发射法和希尔伯特变换技术来识别和量化碳纤维增强塑料材料的损伤进展。本文介绍了在交叉层压板上进行的疲劳测试的实验结果。为了精确预测损伤并评估复合材料试样的状况,研究人员使用了双向长短期记忆模型和包络分析进行预测。所建议的方法实现了小于 0.03 的均方根误差,证明了其精确预测损坏和评估复合材料结构状况的能力。这种由深度学习驱动的新方法能够有效捕捉碳纤维增强塑料的性能劣化,提高预测精度。
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来源期刊
Russian Journal of Nondestructive Testing
Russian Journal of Nondestructive Testing 工程技术-材料科学:表征与测试
CiteScore
1.60
自引率
44.40%
发文量
59
审稿时长
6-12 weeks
期刊介绍: Russian Journal of Nondestructive Testing, a translation of Defectoskopiya, is a publication of the Russian Academy of Sciences. This publication offers current Russian research on the theory and technology of nondestructive testing of materials and components. It describes laboratory and industrial investigations of devices and instrumentation and provides reviews of new equipment developed for series manufacture. Articles cover all physical methods of nondestructive testing, including magnetic and electrical; ultrasonic; X-ray and Y-ray; capillary; liquid (color luminescence), and radio (for materials of low conductivity).
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