Search Results (9)

The use of composites in industry is increasing due to their ability to replace traditional materials. Carbon fiber-reinforced polymers offer a favorable strength-to-weight ratio, making them advantageous in numerous applications. Delamination is a common failure mode for composite materials, making it a crucial factor in ensuring material safety during service life. While fiber orientation in composites is designed for specific directional reinforcement, out-of-plane loads are often neglected, posing a critical challenge. Implementing through-thickness reinforcement, such as tufting, can enhance out-of-plane resistance, enabling more accurate structural designs. Non-destructive testing methods, particularly acoustic emission, play a significant role in ensuring component safety by detecting early damage and flaws. This study focused on monitoring mode II interlaminar fracture toughness and end-notched flexure (ENF), using acoustic emissions to compare the performance of samples with different through-thickness reinforcements against that of nonreinforced samples. The research analyzed acoustic emission patterns during testing, revealing a strong correlation with failure stages and the resistance induced by reinforcements. This approach provided valuable insights into damage characterization, supported by fractography analysis, especially concerning the final stages of failure due to damage, and the effects of different thread reinforcements. Acoustic emission proved crucial for real-time monitoring, enabling informed decisions to be made regarding component repair and lifespan extension in composite materials. Full article

Airfield pavements are important assets that have to secure the safe operation of an airport. On this basis, assessing and reporting the bearing capacity of an airfield runway pavement is a critical task. Recently, the Aircraft Classification Rating-Pavement Classification Rating (ACR-PCR) system has been introduced, which uses the PCR index for expressing the bearing capacity of an airfield pavement. In order to accurately determine PCR, the mechanical characteristics and the thicknesses of the individual layers of a pavement are required. For this purpose, it is not seldom that in the absence of resources dedicated to detailed pavement evaluation procedures, assumptions for the material characteristics of the pavement considering typical materials may be made, while pavement thicknesses may be derived by pavement design records. The present paper highlights the importance of using Non-Destructive Testing (NDT) for accurately assessing the in-situ condition of a flexible runway pavement and determining the PCR index. In order to achieve the goal of the investigation, measurements were performed along the flexible pavement of an airport runway. In addition, the paper focuses on the impact of the variation of the thickness and of the mechanical characteristics of the asphalt concrete layers on the PCR index and on the interpretation of the results considering the acceptance of aircraft operations by airport authorities. Full article

Material-resolving computed tomography is a powerful and well-proven tool for various clinical applications. For industrial scan setups and materials, several problems, such as K-edge absence and beam hardening, prevent the direct transfer of these methods. This work applies dual-energy computed tomography methods for material decomposition to simulated phantoms composed of industry-relevant materials such as magnesium, aluminium and iron, as well as some commonly used alloys like Al–Si and Ti64. Challenges and limitations for multi-material decomposition are discussed in the context of X-ray absorption physics, which provides spectral information that can be ambiguous. A deep learning model, derived from a clinical use case and based on the popular U-Net, was utilised in this study. For various reasons outlined below, the training dataset was simulated, whereby phantom shapes and material properties were sampled arbitrarily. The detector signal is computed by a forward projector followed by Beer–Lambert law integration. Our trained model could predict two-material systems with different elements, achieving a relative error of approximately 1% through simulated data. For the discrimination of the element titanium and its alloy Ti64, which were also simulated, the relative error increased to 5% due to their similar X-ray absorption coefficients. To access authentic CT data, the model underwent testing using a 10c euro coin composed of an alloy known as Nordic gold. The model detected copper as the main constituent correctly, but the relative fraction, which should be 89%, was predicted to be ≈70%. Full article

This paper describes the design and implementation of an ultrasonic non-contact air-coupled technique (UNCACT) using antisymmetric Lamb waves (ALW) for NDT assessments in novel composite sandwich plates of a car body shell. This technique is complemented with a C-Scan image implementation using guided waves. The finite element method (FEM) was developed using Comsol 6.1 for the interpretation of the several wave modes presented in the experiments, including the ALW mode. This FEM model is indispensable for the correct interpretation of the received signals and contributes to a better implementation of this technology. This is a novel contribution building upon previously reported work. Additionally, the phase velocity method (PVM) was applied for the verification of the ALW mode in the portion of the RF signal necessary for the C-Scan image. Full article

In order to solve the problems of low efficiency, time consumption and high costs in the detection of defects on wind turbine surfaces in industrial scenarios, an improved YOLOv5 algorithm for wind turbine surface defect detection is proposed, named YOLOv5s-L. Firstly, the C3 module of YOLOv5s is replaced with the C2f module, which is more abundant in gradient flow, to enhance the ability of feature extraction and feature fusion. Secondly, the Squeeze and Excitation (SE) module is embedded in the YOLOv5 Backbone network to filter out redundant feature information and retain important feature information. Thirdly, the weighted Bidirectional Feature Pyramid Network (BiFPN) is introduced to replace the FPN + PAN, which can achieve a higher level of feature fusion while keeping the weight light. Finally, the Focal Loss function is used to replace the CIOU Loss function of the YOLOv5 algorithm to optimize the training model and improve the accuracy of the algorithm. The experimental results show that, compared with the traditional YOLOv5 algorithm, the average precision mAP is improved by 1.9%, and the frame rate FPS can reach 145 F/s without increasing the model parameters; it can satisfy the requirements for real-time, accurate detection on mobile devices. This method provides effective support for surface defect detection of wind turbines and provides reference for intelligent wind farm operation and maintenance. Full article

Coercivity is the strength of the reverse magnetic field required to demagnetize a material after saturation, and it is an indication of the hardness of magnetic materials. Air gaps cause errors in coercivity measurement referred to as the lift-off effect. This paper proposes a new method to address this issue by incorporating additional inductance measurements and formulating a calibration method. The calibration principle is based on the fact that both the coercivity and the inductance measurements change with the variation of air gaps. This paper starts by finding how coercivity changes with air gaps between the sensor and the sample, then derives the coefficients for the coercivity–inductance relationship for different samples. A correction method is then proposed to predict the base coercivity (i.e., the coercivity when the air gap = 0) using the inductance and coercivity measurement results at an unknown lift-off. The measurement system was implemented, and experimental results suggest the error caused by air gaps can be reduced from 40% to less than 10%. Full article

In the past several decades, many destructive and non-destructive testing techniques have been developed to evaluate the characteristics of metal matrix composites (MMCs). This research aims to calculate the mechanical properties of the Al@Al${}_2$O${}_3$ composites by varying alumina nanoparticles (Al${}_2$O${}_3$ NPs) content using a non-invasive, position sensing detector (PSD) unit-based optical method. The composite was prepared by a powder metallurgy technique, and its characterization was conducted using SEM and XRD to understand its surface morphology and microstructure. The natural frequency and Young’s modulus of the composite were estimated experimentally. Young’s modulus was calculated using this natural frequency. The proposed study shows that Young’s modulus of the composite increases with an increase in Al${}_2$O${}_3$ NPs content in the composition, irrespective of the testing method. Along with this, natural frequency also increases with the increase in the Al${}_2$O${}_3$ NPs content. Evaluated properties were compared with the numerical modeling using COMSOL Multiphysics. The experimental and numerical results are equivalent and within the margin of error. This study illustrates the development of an experimental approach for evaluating the mechanical properties of a composite material. This experimental approach can be used whenever sample dimension and space are constrained to evaluate the mechanical behavior of nanomaterials and nanocomposites. Full article

This paper reviews the state-of-the-art approaches in defect localization and specifies the remaining questions and challenges. Furthermore, this study presents a novel defect localization methodology using the nonlinear interaction of primary Lamb wave modes and vibroacoustic modulation (VAM), combined with damage imaging, to address the current shortcomings of defect localization. The study investigates this methodology experimentally with respect to defect interpretation, resolution, and applicability. Two Lamb waves with high and low frequencies, one being continuous and the other a tone burst, were excited using two different piezoelectric sensors. The amplitude of the measured signal at the first sideband frequency was evaluated with a short-time Fourier transform (STFT) and used for damage imaging via the delay and sum method. This study also includes a discussion on identifying the source of nonlinearity reflected in the first sideband. The experimental measurements prove that the localization of defect nonlinearity is possible with high accuracy, without the need for a baseline measurement, and with a minimum number of sensors. Sensitivity measurements with respect to the required length of the high-frequency tone burst and the sensor arrangement were also conducted. Full article