Characterization Of Defects Research Articles

Experimental Investigation Using Robust Deep VMD-ICA and 1D-CNN for Condition Monitoring of Roller Element Bearing

Abstract A rotor-bearing system experiences numerous vibrations during the operation that frequently degrade performance and endanger operational safety. Roller bearing failure has significant consequences, leading to downtime or even a complete outage of rotating machinery. It is crucial to detect and diagnose incipient bearing defects promptly to ensure optimal operation of the machinery and minimize potential disruptions to the process. Deep Independent Component Analysis is a necessity used in modern condition monitoring to detect bearing failure earlier than it occurs. To address this issue the feasibility of utilizing Deep Independent Component Analysis (ICA) method based on Variational Modal Decomposition (VMD) with one-dimensional convolutional neural network (1D-CNN) to diagnose incipient bearing defect. Fast Fourier Techniques are utilized to extract the vibration signatures of artificially damaged bearings on a newly built test bed. VMD addresses to minimize data noise, allowing data to decompose into various sub-datasets for extraction of incipient defect features. With weak defect characteristic signal and noise interference, the Deep VMD-ICA model and 1D-CNN simplicity improved the accuracy of diagnosis corresponding to the experimental results. Moreover, Deep VMD-ICA with 1D-CNN has additionally demonstrated strong performance in comparison to experimental results and is useful for monitoring the condition of industrial machinery. The results reveal that this fault diagnosis approach is reliable, with a diagnostic accuracy of 98.93% for bearing faults.

Rapid Characterization of Point Defects in Solid-State Ion Conductors Using Raman Spectroscopy, Machine-Learning Force Fields, and Atomic Raman Tensors.

The successful design of solid-state photo- and electrochemical devices depends on the careful engineering of point defects in solid-state ion conductors. Characterization of point defects is critical to these efforts, but the best-developed techniques are difficult and time-consuming. Raman spectroscopy─with its exceptional speed, flexibility, and accessibility─is a promising alternative. Raman signatures arise from point defects due to local symmetry breaking and structural distortions. Unfortunately, the assignment of these signatures is often hampered by a shortage of reference compounds and corresponding reference spectra. This issue can be circumvented by calculation of defect-induced Raman signatures from first principles, but this is computationally demanding. Here, we introduce an efficient computational procedure for the prediction of point defect Raman signatures in solid-state ion conductors. Our method leverages machine-learning force fields and "atomic Raman tensors", i.e., polarizability fluctuations due to motions of individual atoms. We find that our procedure reduces computational cost by up to 80% compared to existing first-principles frozen-phonon approaches. These efficiency gains enable synergistic computational-experimental investigations, in our case allowing us to precisely interpret the Raman spectra of Sr(Ti0.94Ni0.06)O3-δ, a model oxygen ion conductor. By predicting Raman signatures of specific point defects, we determine the nature of dominant defects and unravel impacts of temperature and quenching on in situ and ex situ Raman spectra. Specifically, our findings reveal the temperature-dependent distribution and association behavior of oxygen vacancies and nickel substitutional defects. Overall, our approach enables rapid Raman-based characterization of point defects to support defect engineering in novel solid-state ion conductors.

Peridynamic analysis of rolling contact fatigue crack propagation in rail welding joints with pore defects

Pore defects are prevalent in rail welding joints and significantly contribute to the propagation of fatigue cracks. This study develops a peridynamic (PD) model that incorporates the characteristics of pore defects to analyze their impact on rolling contact fatigue behavior. Initially, compact tension (CT) fatigue tests were performed to derive and validate the bond fatigue failure model specific to rail weld materials. Subsequently, pore defects were modeled as holes in the CT specimens, with experimental results being compared to PD simulation outcomes for validation. Finally, a wheel-rail contact PD model was constructed to investigate the mechanisms of fatigue crack propagation in rail welding joints affected by pore defects.

Prevalence, characteristics and risk factors of birth defects in central China livebirths, 2015-2022.

This study analyzed the prevalence, epidemiological characteristics and risk factors of birth defects among livebirths in central China, aiming to provide evidences for the prevention of birth defects and government Decision-makings. Birth data from China's Hubei Province between 2015 and 2022 were collected, including basic information of the livebirths, the mothers and the fathers, as well as information about delivery and each prenatal examination. The livebirths prevalence of birth defects was calculated and the trends were mapped. The basic characteristics of birth defects were evaluated by the difference analysis between case and health groups. Univariate and multivariate Poisson regression was performed to examine the independent risk factors for birth defects. Among 43,568 livebirths, 166 livebirths were born with birth defects, resulted in a total prevalence rate of 3.81 per 1,000 livebirths, showing a remarkable uptrend from 0.41per 1,000 livebirths in 2015 to 9.23 per 1,000 livebirths in 2022. The peak of the prevalence was in January and February. Congenital malformation of the musculoskeletal system was the main type of birth defect in central China livebirths, followed by cleft lip and cleft palate. Overall, newborns with birth defect had significantly earlier delivery gestational age, poorer health and higher proportion of infants with low birth weight than healthy births. The gender of livebirths, excess weight at delivery (≥80 kg) of mothers, more than 2 times of gravidity or parity of mothers, and advanced paternal age (≥40 years) were independent risk factors for birth defects (or specific birth defects). The livebirths prevalence of birth defects shows increasing trend in central China, which deserves the attention of the government and would-be parents. Elevated paternal age, excess maternal weight, gravidity and parity should be considered when planning their families.

Grinding defect characteristics and removal mechanism of unidirectional Cf/SiC composites

Grinding defect characteristics and removal mechanism of unidirectional Cf/SiC composites

Effect of prerolling before artificial aging on the mechanical properties of high-Zn-content Al–Zn–Mg–Cu–Sc–Zr–Mn alloy

The effect of prerolling before artificial aging on the mechanical properties of the Al–Zn–Mg–Cu–Sc–Zr–Mn alloy with 12.4 wt% Zn (Zn/Mg ratio >1.3 at.%) is studied. The alloy sheets are prepared via casting, homogenization, hot rolling, and solution treatment. The sheets are further cold rolled to reduce their thicknesses by 0%–50% before being artificially aged. Yield strength, ultimate tensile strength, and elongation are increased from 648 to 697 MPa, from 662 to 712 MPa, and from 2.2% to 4.4% by prerolling up to 30%, and prerolling again by 50% reduces both the strength and elongation. Changes in the microstructural features, such as grain morphology, dislocation density, and defect characteristics, are attributed to the variations in the strength and elongation of the alloy. Work hardening during prerolling is responsible for the increase in strength, while pore closure and shear band formation induced by prerolling either increase or decrease the elongation. Furthermore, precipitation kinetics are accelerated by applying prerolling, which facilitates a faster aging response, lesser processing time to reach the peak hardness, and better mechanical properties.

A Novel Approach to Identifying Hibernating Myocardium Using Radiomics-Based Machine Learning.

Background To assess the feasibility of a machine learning (ML) approach using radiomics features of perfusion defects on rest myocardial perfusion imaging (MPI) to detect the presence of hibernating myocardium. Methodology Data of patients who underwent 99mTc-sestamibi MPI and 18F-FDG PET/CT for myocardial viability assessment were retrieved. Rest MPI data were processed on ECToolbox, and polar maps were saved using theNFile PMap tool. The reference standard for defining hibernating myocardium was the presence of mismatched perfusion-metabolism defect with impaired myocardial contractility at rest. Perfusion defects on the polar maps were delineated with regions of interest (ROIs) after spatial resampling and intensity discretization. Replicable random sampling allocated 80% (257) of the perfusion defects of the patients from January 2017 to September 2022 to the training set and the remaining 20% (64) to the validation set. An independent dataset of perfusion defects from 29 consecutive patients from October 2022 to January 2023 was used as the testing set for model evaluation. One hundred ten first and second-order texture features were extracted for each ROI. After feature normalization and imputation, 14 best-ranked features were selected using a multistep feature selection process including the Logistic Regression and Fast Correlation-Based Filter. Thirteen supervised ML algorithms were trained with stratified five-fold cross-validation on the training set and validated on the validation set. The ML algorithms with a Log Loss of <0.688 and <0.672 in the cross-validation and validation steps were evaluated on the testing set. Performance matrices of the algorithms assessed included area under the curve (AUC), classification accuracy (CA), F1 score, precision, recall, and specificity. To provide transparency and interpretability, SHapley Additive exPlanations (SHAP) values were assessed and depicted as beeswarm plots. Results Two hundred thirty-nine patients (214 males; mean age 56 ± 11 years) were enrolled in the study. There were 371 perfusion defects (321 in the training and validation sets; 50 in the testing set). Based on the reference standard, 168 perfusion defects had hibernating myocardium (139 in the training and validation sets; 29 in the testing set). On cross-validation, six ML algorithms with Log Loss <0.688 had AUC >0.800. On validation, 10 ML algorithms had a Log Loss value <0.672, among which six had AUC >0.800. On model evaluation of the selected models on the unseen testing set, nine ML models had AUC >0.800 with Gradient Boosting Random Forest (xgboost) [GB RF (xgboost)] achieving the highest AUC of 0.860 and could detect the presence of hibernating myocardium in 21/29 (72.4%) perfusion defects with a precision of 87.5% (21/24), specificity 85.7% (18/21), CA 78.0% (39/50) and F1 Score 0.792. Four models depicted a clear pattern of model interpretability based on the beeswarm SHAP plots. These were GB RF (xgboost), GB (scikit-learn), GB (xgboost), and Random Forest. Conclusion Our study demonstrates the potential of ML in detecting hibernating myocardium using radiomics features extracted from perfusion defects on rest MPI images. This proof-of-concept underscores the notion that radiomics features capture nuanced information beyond what is perceptible to the human eye, offering promising avenues for improved myocardial viability assessment.

Critical physics-informed fatigue life prediction of laser 3D printed AlSi10Mg alloys with mass internal defects

The significant scatter in high cycle fatigue life of additively manufactured metallic components presents an increasing challenge to structural integrity. This fatigue life variation is radically attributed to the differences in physical features of critical defects that lead to crack initiation. To address this issue, this paper proposes an integrated framework for identifying critical defects and predicting fatigue life using physics-informed machine learning, with a focus on the impact of 3D defect features. By employing X-ray tomography, high cycle fatigue tests, and fractography analyses on post-mortem specimens, a dataset associated with mass internal defects is first built up to correlate the spatial geometric features of critical defects with fatigue life. A kernel support vector machine is then used to formulate a critical defect identification model, aimed at identifying critical defects among numerous defects by evaluating their geometric attributes. Finally, a fatigue life prediction model is developed using a physics-informed neural network, which incorporates the influence of defect geometry on fatigue life as physical constraints in the loss function. The integrated framework demonstrates that fatigue life predictions from identified critical defects in each specimen exhibit small deviations, with the average prediction falling within twice the error bands. This study is expected to provide a valuable reference for fatigue assessment of additively manufactured components through sequential critical defect identification and fatigue life prediction.

Research on Welding Defect Classification Methods Based on Convolutional Neural Networks

To address the issues of low detection and identification rates and inaccurate classification of welding defects, this paper proposes a welding defect classification method based on convolutional neural networks. By modifying the ResNet101 backbone network and incorporating a multi-head attention mechanism (MHA), the method enhances the focus on regions of interest in welding defect images, thereby improving the ability to extract defect features. This approach reduces the chances of missed detections and false positives. By analyzing the effects of different optimizers and dynamic factors on recall (M) and precision (N), the optimal optimizer and dynamic factor are identified. The algorithm is validated on the JPEGWD dataset. Experimental results show that the recall (M) reaches 0.9634, representing a 4.88% improvement, and the precision (N) reaches 0.9875, representing a 3.81% improvement. The mean average precision (mAP) is 0.9817, reflecting a 5.98% improvement. These results effectively enhance the recognition rate of welding defects and reduce the probability of missed detections and false positives.

Nondestructive fatigue life prediction for additively manufactured metal parts through a multimodal transfer learning framework

Understanding the fatigue behavior and accurately predicting the fatigue life of laser powder bed fusion (L-PBF) parts remain a pressing challenge due to complex failure mechanisms, time-consuming tests, and limited fatigue data. This study proposes a physics-informed data-driven framework, a multimodal transfer learning (MMTL) framework, to understand process-defect-fatigue relationships in L-PBF by integrating various modalities of fatigue performance, including process parameters, XCT-inspected defects, and fatigue test conditions. It aims to leverage a pre-trained model with abundant process and defect data in the source task to predict fatigue life nondestructively with limited fatigue test data in the target task. MMTL employs a hierarchical graph convolutional network (HGCN) to classify defects in the source task by representing process parameters and defect features in graphs, thereby enhancing its interpretability. The feature embedding learned from HGCN is then transferred to fatigue life modeling in neural network layers, enabling fatigue life prediction for L-PBF parts with limited data. MMTL validation through a numerical simulation and real-case study demonstrates its effectiveness, achieving an F1-score of 0.9593 in defect classification and a mean absolute percentage log error of 0.0425 in fatigue life prediction. MMTL can be extended to other applications with multiple modalities and limited data.

Molar Incisor Hypomineralization: Prevalence and Treatment Needs in 7- to 9-year-old Children of Lucknow City.

Molar incisor hypomineralization (MIH) is one of the most common developmental disturbances that dental practitioners encounter, which may influence the child's quality of life and can impact their future dental health. To determine prevalence and treatment needs of MIH in children of Lucknow. A total of 800 children aged 7 to 9 years were clinically screened for the presence of MIH. All demographic details were filled in by the examiner in communication with the parents, and examination was performed using the Würzburg MIH concept. The overall prevalence of MIH in the children examined was 5.12%. A higher prevalence was found in males (7%) than in females (3.25%). The distribution of MIH was higher in the mandibular arch than in the maxillary arch. Mandibular molars were the most affected teeth, followed by maxillary incisors, and the least affected teeth were maxillary molars. On the basis of severity, 55.14% of teeth with MIH had no breakdown or hypersensitivity followed by 30.14% of teeth with hypersensitivity but no breakdown, and 7.35% of teeth had both hypersensitivity and breakdown. Based on severity, in 39.70% of teeth, the intervention suggested was fluoridated toothpaste, casein phosphopeptide-amorphous calcium phosphate (CPP-ACP) remineralizing agent, and varnish application. This was followed by the application of sealants and low-viscosity glass ionomer cement (GIC) in 38.97% of teeth, and in 7.35% of teeth, short- and long-term restorations, including stainless steel crown (SSC), were recommended. Prevalence of 5.12% was observed in children of Lucknow city. The characteristics of MIH defects were predictive of the treatment of the affected first permanent molars and incisors and can guide their management. Chowdhury AR, Singh N, Rathore M. Molar Incisor Hypomineralization: Prevalence and Treatment Needs in 7- to 9-year-old Children of Lucknow City. Int J Clin Pediatr Dent 2024;17(7):790-795.

Heterogeneous Nucleation Regulation Amends Unfavorable Crystallization Orientation and Defect Features of Antimony Selenosulfide Film for High-Efficient Planar Solar Cells.

Antimony selenosulfide (Sb2(S,Se)3) has obtained widespread concern for photovoltaic applications as a light absorber due to superior photoelectric features. Accordingly, various deposition technologies have been developed in recent years, especially hydrothermal deposition method, which has achieved a great success. However, device performances are limited with severe carrier recombination, relating to the quality of absorber and interfaces. Herein, bulk and interface defects are simultaneously suppressed by regulating heterogeneous nucleation kinetics with barium dibromide (BaBr2) introduction. In details, the Br adsorbs and dopes on the polar planes of cadmium sulfide (CdS) buffer layer, promoting the exposure of nonpolar planes of CdS, which facilitates the favorable growth of [hk1]-Sb2(S,Se)3 films possessing superior crystallinity and small interface defects. Additionally, the Se/S ratio is increased due to the replacement of S/Se by Br, causing a downshift of the Fermi levels with a benign band alignment and a shallow-level defect. Moreover, Ba2+ is located at grain boundaries by coordination with S and Se ions, passivating grain boundary defects. Consequently, the efficiency is increased from 7.70% to 10.12%. This work opens an avenue towards regulating the heterogeneous nucleation kinetics of Sb2(S,Se)3 film deposited via hydrothermal deposition approach to optimize its crystalline orientation and defect features.

Full Thickness Nasal Reconstruction With Paired Pericranial and Paramedian Forehead Flaps.

The reconstruction of full-thickness nasal defects poses a significant challenge following oncologic resection. This study aims to share a technique using paired pericranial forehead flap (PCF) with contralateral paramedian flap (PMF) for such defects. Patient outcomes were reviewed, and the advantages and disadvantages of the reconstructive technique are discussed. A retrospective review of a single surgeon practice was done between 2019 and 2024. Cases of nasal reconstruction with a paired PCF and PMF following oncologic resection were reviewed. Defect characteristics, reconstructive technique, and postoperative complications were evaluated. A literature review summarizing the evolution of this technique from inception to April 2024 was conducted using PubMed. The literature review identified 7 reports describing the use of a paired PCF and PMF for nasal reconstruction. The modifications and enhancements described in each study are summarized. The case series included 13 patients requiring oncologic resection for squamous cell carcinoma (8 patients) or basal cell carcinoma (5 patients). Every case required reconstruction of at least 2 nasal subunits, primarily involving the nasal tip, alae, and columella. Reconstruction was performed with the ipsilateral PCF, contralateral PMF, and structural grafts. Auricular cartilage grafts were universally used for structural support, with additional costal cartilage grafts and a split calvaria bone graft in select cases. The technique showed good functional and esthetic outcomes without any notable graft failures or donor site complications. The combination of an ipsilateral PCF and contralateral PMF is an effective strategy for reconstructing full-thickness nasal defects involving multiple nasal subunits.

Stability and deformation of a vacancy defect in skyrmion crystal under external magnetic and temperature fields

Skyrmion, a local bubble-like topological magnetization structure, can collectively emergent in magnets in a lattice form skyrmion crystal (SkX). SkX has great application potential in functional devices because it can manipulate material properties via coupling with atomic lattices. The lattice defects such as vacancy widely exist in the SkX as well, and they have rich dynamic behaviors and have great implications for the host material. However, although the nature of ideal SkX is well studied, the characteristics of SkX defects are relatively underdeveloped. Here, we deeply studied the structural properties of a vacancy defect in the SkX by a thermodynamic phase-field simulation. We found that the higher external magnetic and temperature fields favor rigid skyrmions (crystal), in which the SkX vacancy is less deformed, while the lower fields favor softer skyrmions where the SkX vacancy structure is considerably deformed. Such unique deformation and stability of the SkX vacancy are mainly the results of the competition between free energies in the view of thermodynamics. Our study demonstrated that the external field-controlled static properties of SkX vacancy highly depend on the quasiparticle nature of skyrmions. This indicates the properties of SkX defects can be controlled by the SkX features under different fields, which should open an avenue for the study and design of smart materials and advanced devices by engineering skyrmion crystals.

Resolving the Trap Distribution of Chalcogenide-Based Heterojunctions via Optical Injection Deep Level Transient Spectroscopy.

Chalcogenide semiconductors have emerged as promising candidates for next-generation optoelectronics, mainly benefiting from their cost-effective processability, chemical versatility, and excellent stability. However, the device performance is limited by the complex defect characteristics, necessitating a detailed understanding of the defect density, types, and distribution. This study employs optical injection deep level transient spectroscopy to characterize the trap features of antimony bismuth sulfide-based heterojunctions. Photoexcitation offers the possibility to determine the spatial distribution of traps. It reveals distinct distributions of hole and electron traps. Short-wavelength light (570 nm) detects hole traps near the hole transport layer, while long-wavelength light (830 nm) identifies electron traps near the electron transport layer. Additional intermediate-wavelength tests (670 and 755 nm) and light field distribution simulation further elucidate the trap distribution. Transient absorption and transient photocurrent also support the non-uniform trap distribution within the chalcogenide-based photodiodes.

Quantitative detection of the width and depth of subsurface defects in curved structures using ultrasonic Rayleigh waves

ABSTRACT Rayleigh waves are highly sensitive to surface and subsurface defects. However, the varying surface profiles of complex structures induce constant changes in the Rayleigh waveform. Furthermore, Rayleigh waves encompass multi-dimensional characteristics of subsurface defects, significantly increasing the difficulty of quantitatively detecting the sizes of subsurface defects in different dimensions. This paper presents a machine learning (ML)-based method for quantitatively detecting subsurface defect depth and width in curved structures using laser-generated Rayleigh waves. An experimentally validated finite element model is first established to construct a dataset of Rayleigh wave signals labeled with multidimensional subsurface defect sizes. The dataset is augmented from 188 to 368 samples using the Mix-up algorithm, with additional experimental signals incorporated. Feature parameters of Rayleigh waves are extracted in both time and frequency domains through waveform analysis and sliding window wavelet transform (SWT). These features are used to train ML models: Gaussian Process (GP), Extreme Gradient Boosting (XGBoost), and Random Forest (RF). Comparative results demonstrate GP achieves the highest prediction accuracy for subsurface defect depth and width on the test set, reaching 98.64% and 97.73%, respectively. The study highlights the integration of experimental and simulated data in training, which reduces costs while enhancing model robustness in practical applications.

Focused Electromagnetic Acoustic Transducers for Lamb Wave Inspection

Abstract Ultrasonic inspection of conductive samples can be performed using electromagnetic acoustic transducers (EMATs). These allow generation of guided waves, such as Lamb waves, in thin plates. Lamb waves are used for defect screening as they can travel long distances with relatively little attenuation. However, large wavelengths are used, which results in poor sensitivity to subwavelength sized defects. This work presents methods for using Lamb waves to detect smaller defects. Curved, geometrically focused EMATs were used to generate and detect Lamb waves in a 1 mm thick aluminum plate. The focal spot of these EMATs is investigated and the dependence of the focal spot dimensions and locations on the wavelength of the generation signal are presented. An array of geometrically focused detection EMATs was used to measure a 10×10×0.5mm square machined defect, simulating wall thinning. Perturbations in the pulse transmission in the region of the defect were detected, along with reflections, with each detector in the array sensitive to different defect features. B-scans and C-scans are presented utilizing these perturbations and reflections to locate, size, and image the machined defect. Analysis of data from the B-scan is shown to accurately size the defect width to within ±0.73mm. Very good agreement is shown between the C-scan images and the known location and orientation of the defect. Data fusion techniques are presented to combine data sets from different receiver EMATs and increase the defect detection capability, accuracy, and precision, with the potential for full defect sizing demonstrated.

Insulator Defect Detection Based on YOLOv5s-KE

To tackle the issue of low detection accuracy in insulator images caused by intricate backgrounds and small defect sizes, as well as the requirement for real-time detection on embedded and mobile devices, this research introduces the YOLOv5s-KE model. Integrating multiple strategies, YOLOv5s-KE aims to boost detection accuracy significantly. Initially, an enhanced anchor generation method utilizing the K-means++ algorithm is proposed to generate more appropriate anchor boxes for insulator defects. Moreover, an attention mechanism is integrated into both the backbone and neck networks to enhance the model’s capacity to focus on defect features and resist interference. To improve the detection of small defects, the EIoU loss function is implemented in place of the original CIoU loss function. In order to meet the real-time detection needs on embedded and mobile devices, the model is further refined through the integration of Ghost convolution for lightweight feature extraction and a linear transformation to reduce the computational burden of standard convolution. A channel pruning strategy is deployed to optimize the sparsely trained network, diminishing redundancy, and improving model generalization. Additionally, the CARAFE operator replaces the original upsampling operator to minimize model parameters and elevate detection speed. Experimental outcomes demonstrate that YOLOv5s-KE achieves a detection accuracy of 92.3% on the Chinese transmission line insulator dataset, marking a 5.2% enhancement over the original YOLOv5s. The streamlined version of YOLOv5s-KE achieves a detection speed of 94.3 frames per second, indicating an improvement of 30.1 frames per second compared to the original model. Model parameters are condensed to 9.6 M, resulting in a detection accuracy of 91.1%. This study underscores the precision and efficiency of the proposed approach, suggesting that the advanced strategies explored introduce novel possibilities for insulator defect detection.

The numerical reconstruction methods for characterizing defects by full matrix data using an ultrasonic array

Abstract This study introduces ultrasonic imaging and inversion methods for the quantitative characterization of crack defects. The Total Focusing Method (TFM) is a high-precision ultrasonic imaging method, but it may produce length errors when used for large-angle defect detection. An SV transverse wave TFM method is introduced, significantly reducing length errors in a 60° crack to 8.31%. However, the ultrasonic image method is influenced by factors like array position, limiting its practicality. To overcome this limitation, the study proposes an inversion method using the subarray scattering coefficient matrix, which is more stable and less sensitive to array positioning. Comparative analysis with finite element simulations and experiments indicates optimal array positions associated with different methods. At these positions, both methods yield angular characterization errors for the 60° crack of less than 1%, validating the proposed techniques for non-destructive testing applications.

Study on ultrasonic lamb wave testing technology of honeycomb sandwich structures

Abstract Honeycomb sandwich composite panels have complex structures, large sizes, many types of defects, and significant sound attenuation, which lead to low efficiency and poor sensitivity of ultrasonic non-destructive testing. In the study, a non-linear ultrasonic lamb wave testing technique for honeycomb sandwich composite panels is proposed. Firstly, the anisotropic propagation characteristics and modal structure of lamb waves in honeycomb sandwich composite panels are analyzed, and the lamb wave modes with non-linear ultrasonic accumulation are excited. Secondly, the data extraction method and imaging algorithm of non-linear ultrasonic lamb wave imaging testing are proposed. Finally, an enhanced imaging algorithm is proposed to improve image quality based on the sliding window algorithm. Based on the comparative analysis of air-coupled ultrasonic testing images and metallographic tests, it can be seen that non-linear ultrasonic lamb wave testing signals can be used to characterize the damage distribution characteristics of honeycomb sandwich composite panels, display debonding and weak debonding defects, and have the advantages of being able to perform on the same side testing and high efficiency, which can be used for non-destructive testing of honeycomb sandwich composite panels.