Yan Zhang, Zhaoming Li, Jin Wang, Tengda Zhang, Yuzhong Zhang
{"title":"Detection of flat-bottom holes in composite materials using multi-dimensional complementary ensemble empirical mode decomposition algorithm","authors":"Yan Zhang, Zhaoming Li, Jin Wang, Tengda Zhang, Yuzhong Zhang","doi":"10.1088/1748-0221/18/11/t11002","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":16184,"journal":{"name":"Journal of Instrumentation","volume":"3 6","pages":"0"},"PeriodicalIF":1.3000,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Instrumentation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1088/1748-0221/18/11/t11002","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"INSTRUMENTS & INSTRUMENTATION","Score":null,"Total":0}
引用次数: 0
Abstract
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.
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.