基于多维互补系综经验模态分解算法的复合材料平底孔检测

IF 1.3 4区 工程技术 Q3 INSTRUMENTS & INSTRUMENTATION
Yan Zhang, Zhaoming Li, Jin Wang, Tengda Zhang, Yuzhong Zhang
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引用次数: 0

摘要

摘要复合材料由于具有耐高温、高强度和优异的抗疲劳性能,被广泛应用于汽车制造、航空航天、基础设施等领域。因此,对复合材料缺陷检测的需求也越来越大。主动红外热像仪作为一种无损检测技术,可以实现全场缺陷检测,适用于复合材料的缺陷检测。然而,这种方法容易受到环境和热源引起的噪声的影响。为了解决缺陷信号被噪声淹没的问题,本文提出了一种多维互补系综经验模态分解(MCEEMD)算法。该方法将信号分解为低频背景噪声、高频加热噪声和有用缺陷信号,这些噪声易于去除,提高缺陷图像的比噪比(CNR)。基于该方法对碳纤维增强塑料(CFRP)进行了缺陷检测实验,实验结果表明,该方法能够有效地去除环境噪声和加热噪声,在CFRP样品上检测出12个缺陷中的11个,平均CNR为9.107。与传统的差分绝对对比度法相比,该方法可额外检测出1个长宽比为1.67的小缺陷和1个深度为2mm的深缺陷。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detection of flat-bottom holes in composite materials using multi-dimensional complementary ensemble empirical mode decomposition algorithm
Abstract Due to high-temperature resistance, high strength, and excellent fatigue resistance, composite materials are widely used in automotive manufacturing, aerospace, infrastructure and other fields. Consequently, the demand for defect detection of composite materials is also increasing. As a non-destructive testing technique, the active infrared thermography, which can achieve full-field defect detection, is suitable for defect detection of composite materials. However, this method is susceptible to noises caused by the environment and heating sources. In order to solve the problem of the defect signal being submerged by these noises, a multi-dimensional complementary ensemble empirical mode decomposition (MCEEMD) algorithm is introduced in this paper. This method can decompose the signal into the low-frequency background noise, the high-frequency heating noise, and useful defect signals, and these noises can be easily removed to improve the contrast to noise ratio (CNR) of defect images. Based on this proposed method, a defect detection experiment on the carbon fiber reinforced plastic (CFRP) is performed in this paper, and experimental results show that the method can effectively remove environmental noise and heating noise, and it can detect 11 out of 12 defects on the CFRP sample with an average CNR of 9.107. Compared with the traditional differential absolute contrast method, this method can detect one additional small defect with the aspect ratio of 1.67 and one deep defect with a depth of 2 mm.
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来源期刊
Journal of Instrumentation
Journal of Instrumentation 工程技术-仪器仪表
CiteScore
2.40
自引率
15.40%
发文量
827
审稿时长
7.5 months
期刊介绍: Journal of Instrumentation (JINST) covers major areas related to concepts and instrumentation in detector physics, accelerator science and associated experimental methods and techniques, theory, modelling and simulations. The main subject areas include. -Accelerators: concepts, modelling, simulations and sources- Instrumentation and hardware for accelerators: particles, synchrotron radiation, neutrons- Detector physics: concepts, processes, methods, modelling and simulations- Detectors, apparatus and methods for particle, astroparticle, nuclear, atomic, and molecular physics- Instrumentation and methods for plasma research- Methods and apparatus for astronomy and astrophysics- Detectors, methods and apparatus for biomedical applications, life sciences and material research- Instrumentation and techniques for medical imaging, diagnostics and therapy- Instrumentation and techniques for dosimetry, monitoring and radiation damage- Detectors, instrumentation and methods for non-destructive tests (NDT)- Detector readout concepts, electronics and data acquisition methods- Algorithms, software and data reduction methods- Materials and associated technologies, etc.- Engineering and technical issues. JINST also includes a section dedicated to technical reports and instrumentation theses.
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