A novel two-level approach to defect detection in braided CFRP using air-coupled ultrasonic testing

Zhang, Chenchen; Zhang, Yinghong; Liu, Dianzi; Zhang, Xiaojia; Wu, Xia; Qian, Zhenghua; Li, Xiangyu

doi:10.1016/j.ultras.2022.106884

Cited by 14 publications

(5 citation statements)

References 26 publications

(25 reference statements)

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“…Variational mode decomposition (VMD) developed by [56] is a robust signal decomposition algorithm extensively applied in many area of scientific research. The VMD is an adaptive, non-recursive method of mode variation and signal processing for which the operation of calculating the decomposition components is conducted by iteratively finding the "frequency center" and "bandwidth" of each component based on the optimal solution of the variational model [57,58]. The decomposition using the VMD algorithm can be achieved as follows:…”

Section: Variational Mode Decomposition (Vmd)mentioning

confidence: 99%

A New Insight for Daily Solar Radiation Prediction by Meteorological Data Using an Advanced Artificial Intelligence Algorithm: Deep Extreme Learning Machine Integrated with Variational Mode Decomposition Technique

et al. 2023

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Reliable and precise estimation of solar energy as one of the green, clean, renewable and inexhaustible types of energies can play a vital role in energy management, especially in developing countries. Also, solar energy has less impact on the earth’s atmosphere and environment and can help to lessen the negative effects of climate change by lowering the level of emissions of greenhouse gas. This study developed thirteen different artificial intelligence models, including multivariate adaptive regression splines (MARS), extreme learning machine (ELM), Kernel extreme learning machine (KELM), online sequential extreme learning machine (OSELM), optimally pruned extreme learning machine (OPELM), outlier robust extreme learning machine (ORELM), deep extreme learning machine (DELM), and their versions combined with variational mode decomposition (VMD) as integrated models (VMD-DELM, VMD-ORELM, VMD-OPELM, VMD-OSELM, VMD-KELM, and VMD-ELM), for solar radiation estimation in Kurdistan region, Iraq. The daily meteorological data from 2017 to 2018 were used to implement suggested artificial models at Darbandikhan and Dukan stations, Iraq. The input parameters included daily data for maximum temperature (MAXTEMP), minimum temperature (MINTEMP), maximum relative humidity (MAXRH), minimum relative humidity (MINRH), sunshine duration (SUNDUR), wind speed (WINSPD), evaporation (EVAP), and cloud cover (CLOUDCOV). The results show that the proposed VMD-DELM algorithm considerably enhanced the simulation accuracy of standalone models’ daily solar radiation prediction, with average improvement in terms of RMSE of 13.3%, 20.36%, 25.1%, 27.1%, 34.17%, 38.64%, and 48.25% for Darbandikhan station and 5.22%, 10.01%, 10.26%, 21.01%, 29.7%, 35.8%, and 40.33% for Dukan station, respectively. The outcomes of this study reveal that the VMD-DELM two-stage model performed superiorly to the other approaches in predicting daily solar radiation by considering climatic predictors at both stations.

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Section: Variational Mode Decomposition (Vmd)mentioning

confidence: 99%

A New Insight for Daily Solar Radiation Prediction by Meteorological Data Using an Advanced Artificial Intelligence Algorithm: Deep Extreme Learning Machine Integrated with Variational Mode Decomposition Technique

et al. 2023

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“…However, a detailed explanation of the propagation characteristics and damage evaluation methods of ZGV modes in composite plates was not provided. Ahn et al [24] explored the assessment of microcrack damage in concrete using air-coupled ultrasonic transducers, while Zhang et al [25] proposed a damage assessment method for millimeter-level damage in braided CFRP structures based on air-coupled ultrasonic transducers. Yu et al [26] achieved delamination imaging and quantification of CFRP plates through air-coupled ultrasonic transducers.…”

Section: Introductionmentioning

confidence: 99%

Evaluating impact damage on carbon fiber-reinforced polymer plates utilizing zero-group-velocity Lamb waves

Ren,

Meng,

Deng

2024

Smart Mater. Struct.

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This paper presents an effective method for evaluating the impact damage of composite plates using zero-group-velocity (ZGV) Lamb waves. A finite element (FE) model of the carbon fiber-reinforced polymer (CFRP) plate is established to analyze in detail the propagation characteristics of the S1-ZGV Lamb wave mode with a specified propagation direction. The study investigates the changes in the S1-ZGV mode with varying damage levels, characterized by a decrease in elastic moduli. Results indicate that as the damage level increases, the corresponding S1-ZGV frequency and amplitude decrease proportionally. The spectral amplitude at the initial S1-ZGV frequency exhibits a consistent and significant decrease with increasing damage levels, offering a reliable method for accurately assessing damage in CFRP plates. Additionally, the S1-ZGV mode of the CFRP plate is experimentally excited using the pitch-catch technique with air-coupled ultrasonic transducers to explore the variations in the S1-ZGV mode with different impact damages. Experimental findings show that the spectral amplitude of the S1-ZGV mode at the initial S1-ZGV frequency decreases monotonically and sensitively with an increasing number of impacts. These experimental results correlate with the FE analysis, validating the effectiveness of accurately evaluating impact damage in CFRP plates based on the spectral amplitude of S1-ZGV modes.

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“…Nondestructive Testing is a process that examines the physical characteristics, chemical parameters, defect location, and other information of the tested object without compromising its structure or performance. Currently, the most common non-destructive testing techniques used in material diagnosis are UltrasonicTesting, Magnetic Powder testing, Eddy Current Testing, and other conventional testing methods [4][5][6] , among which industrial CT testing technology has many advantages over traditional non-destructive testing techniques. With the development of micro-CT technology in recent years, image resolution has successfully reached the micro level, enabling the microstructure analysis of composite materials.…”

Section: Introductionmentioning

confidence: 99%

Investigation on pore characteristics of carbon fiber composite using micro-CT

Chi¹,

Han²,

Miao³

et al. 2023

International Conference on Optoelectronic Materials and Devices (ICOMD 2022)

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Carbon fiber reinforced resin matrix composite (CFRP) has been widely used in aerospace, military and other fields because of its low density and high strength. In composite components, the reliability of materials is significantly impacted by flaws like bubbles. The microscopic CT (computed tomography) technique is suitable for identifying material flaws. This paper takes CFRP as an example to study the pore structure characteristics. The porosity of the sample is 18.66%. Three distinct areas of the carbon fiber were chosen simultaneously, It makes up 98.58% of the pore size in the ROI (regions of interest)-1 area when the pore diameter is between 200μm and 400μm. When the pore size is 41.98-131μm, 98.58% of the total pore size is represented. The inner pore diameter of ROI-3 is 41.98-52.90μm, and there are 31,023 pores in total, which is 42.35% of all pores. The fractal dimensions of cuboid pore models in three distinct regions are centered in the ranges 1. 62-1.68, 1.52-1.55, and 1.35-1.38, respectively. This suggests that a significant portion of the pores at the microscale or even smaller scale are determined by the number of pores and that the filling capacity of pores varies, which is extremely important for the subsequent cross-scale seepage behavior and seepage law of composite materials.

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A novel two-level approach to defect detection in braided CFRP using air-coupled ultrasonic testing

Cited by 14 publications

References 26 publications

A New Insight for Daily Solar Radiation Prediction by Meteorological Data Using an Advanced Artificial Intelligence Algorithm: Deep Extreme Learning Machine Integrated with Variational Mode Decomposition Technique

A New Insight for Daily Solar Radiation Prediction by Meteorological Data Using an Advanced Artificial Intelligence Algorithm: Deep Extreme Learning Machine Integrated with Variational Mode Decomposition Technique

Evaluating impact damage on carbon fiber-reinforced polymer plates utilizing zero-group-velocity Lamb waves

Investigation on pore characteristics of carbon fiber composite using micro-CT

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