Search Results (36)

In this work, a novel approach of guided wave-based damage identification in composite laminates is proposed. The novelty of this research lies in the implementation of ConvLSTM-based autoencoders for the generation of full wavefield data of propagating guided waves in composite structures. The developed surrogate deep learning model takes as input full wavefield frames of propagating waves in a healthy plate, along with a binary image representing delamination, and predicts the frames of propagating waves in a plate, which contains single delamination. The evaluation of the surrogate model is ultrafast (less than 1 s). Therefore, unlike traditional forward solvers, the surrogate model can be employed efficiently in the inverse framework of damage identification. In this work, particle swarm optimisation is applied as a suitable tool to this end. The proposed method was tested on a synthetic dataset, thus showing that it is capable of estimating the delamination location and size with good accuracy. The test involved full wavefield data in the objective function of the inverse method, but it should be underlined as well that partial data with measurements can be implemented. This is extremely important for practical applications in structural health monitoring where only signals at a finite number of locations are available. Full article

Surgical ASTM F139 stainless steel is used for temporary fixtures in the biomedical field. Tribocorrosion is a major concern in this application. The aim of the present work was to study the interplay between tribocorrosion behavior and the surface chemistry of the ASTM F139 stainless steel in phosphate-buffered saline solution (PBS). Sliding wear tests were conducted against alumina balls at different electrochemical potentials: open circuit potential (OCP), cathodic potential (−100 mV versus the OCP), and anodic potentials (+200 mV_Ag/AgCl and +700 mV_Ag/AgCl). The normal load was 20 N. The wear volume was estimated based on micrographs obtained from the wear tracks using confocal laser scanning microscopy. Moreover, the wear tracks were also examined by scanning electron microscopy (SEM). The surface chemistry of the ASTM F139 specimens was analyzed by X-ray photoelectron spectroscopy (XPS). The wear volume was dependent on the electrochemical potential, being maximized at +700 mVAg/AgCl. Delamination areas and grooves were observed in the wear tracks. Detailed assessment of the surface chemistry inside the wear tracks allowed identification of the main chemical species and their relative quantities, thus enabling correlation of the passive film composition with the observed tribocorrosion behavior. Full article

The ability of cryogenic treatment to improve tool steel performance is well established; however, the selection of optimal heat treatment is pivotal for cost reduction and extended tool life. This investigation delves into the influence of distinct cryogenic and tempering treatments on the hardness, fracture toughness, and tribological properties of Vanadis 6 tool steel. Emphasis was given to comprehending wear mechanisms, wear mode identification, volume loss estimation, and detailed characterization of worn surfaces through scanning electron microscopy coupled with energy dispersive spectroscopy and confocal microscopy. The findings reveal an 8–9% increase and a 3% decrease in hardness with cryogenic treatment compared to conventional treatment when tempered at 170 °C and 530 °C, respectively. Cryotreated specimens exhibit an average of 15% improved fracture toughness after tempering at 530 °C compared to conventional treatment. Notably, cryogenic treatment at −140 °C emerges as the optimum temperature for enhanced wear performance in both low- and high-temperature tempering scenarios. The identified wear mechanisms range from tribo-oxidative at lower contacting conditions to severe delaminative wear at intense contacting conditions. These results align with microstructural features, emphasizing the optimal combination of reduced retained austenite and the highest carbide population density observed in −140 °C cryogenically treated steel. Full article

In the present study, the influence of the addition of copper (Cu) on the wear behavior of a Al-12.6Si eutectic alloy developed using the spray forming (SF) method was discussed, and the results were compared with those of as-cast (AC) alloys. The microstructural features of the alloys were examined using both optical and the scanning electron microscopy, and the chemical composition and phase identification were achieved by X-ray diffraction (XRD) analysis. The results revealed that the microstructure of binary the SF alloy consisted of fine primary and eutectic Si phases, evenly distributed in the equiaxed α-Al matrix, whereas the Cu-based SF ternary alloy consisted of uniformly distributed fine eutectic Si particulates and spherical-shaped θ-Al₂Cu precipitates, uniformly distributed in α-Al matrix. In contrast, the AC ternary (Al-12.6Si-2Cu) alloy consisted of unevenly dispersed eutectic Si needles and the coarse intermetallic compound θ-Al₂Cu in the α-Al matrix. The addition of Cu enhanced the micro hardness of the SF ternary alloy by 8, 34, and 41% compared to that of the SF binary, AC ternary, and binary alloys, respectively. The wear test was conducted using a pin-on-disc wear testing machine at different loads (10–40 N) and sliding velocities (1–3 ms⁻¹). The wear tests revealed that SF alloys exhibited an improved wear behavior in the entire applied load and sliding velocity range in comparison to that of the AC alloys. At a load of 40 N and a sliding velocity of 1 ms⁻¹, the wear rate of the SF2 alloy is 62, 47, and 23% lower than that of the AC1, AC2, and SF1 alloys, respectively. Similarly, at a sliding velocity of 3 ms⁻¹, the wear rate of the SF2 alloy is 52%, 42%, and 21% lower than that of the AC1, AC2, and SF1 alloys, respectively. The low wear rate in the SF2 alloy was due to the microstructural modification during spray forming, the precipitation of fine Al₂Cu intermetallic compounds, and increased solid solubility. The SF alloys show an increased transition from oxidative to abrasive wear, while the AC alloys demonstrate wear mechanisms that change from oxidative to abrasive, including delamination, with an increase in sliding velocity and load. Full article

The subjects of this research are the burial clothes of Polish King Sigismund III Vasa and his wife Constance, which were woven and embroidered with silk and metal threads. Fragments of the textiles underwent spectroscopic, spectrometric, and thermogravimetric analyses. The hydrofluoric acid extraction method was improved to isolate various classes of dyes from the textile samples that had direct contact with human remains. High-performance liquid chromatography, coupled with diode array and tandem mass spectrometry detectors with electrospray ionization (HPLC-DAD-ESI-MS/MS) facilitated the detection and identification of colorants present in the textiles. Cochineal, indigo-, madder-, orchil-, and tannin-producing plants were identified as the sources of dyes used. Scanning electron microscopy with an energy-dispersive X-ray detector (SEM-EDS) was employed to identify and characterize the silk fibers and mordants and the metal threads. The presence of iron, aluminum, sodium, and calcium in the silk threads suggests their potential use as mordants. The analysis of the metal threads revealed that most of them were made from flattened gilded silver wire, with only a few being cut from a sheet of metal. Typical degradation mechanisms of metal threads were shown, resulting from both burial environment and earlier manufacturing process, and the use of the textiles in clothing, i.e., a significant loss of the gold layer was observed in most of silver gilt threads, caused by abrasion and delamination. The results of the thermal analysis confirmed the presence of silk and silver threads in the examined textiles. Full article

Sewage and water networks are crucial infrastructures of modern urban society. The uninterrupted functionality of these networks is paramount, necessitating regular maintenance and rehabilitation. In densely populated urban areas, trenchless methods, particularly those employing cured-in-place pipe technology, have emerged as the most cost-efficient approach for network rehabilitation. Common diagnostic methods for assessing pipe conditions, whether original or retrofitted with-cured-in-place pipes, typically include camera examination or laser scans, and are limited in material characterization. This study introduces three innovative methods for characterizing critical aspects of pipe conditions. The impact-echo method, ground-penetrating radar, and impedance spectroscopy address the challenges posed by polymer liners and offer enhanced accuracy in defect detection. These methods enable the characterization of delamination, identification of caverns behind cured-in-place pipes, and evaluation of overall pipe health. A machine learning algorithm using deep learning on images acquired from impact-echo signals using continuous wavelet transformation is presented to characterize defects. The aim is to compare traditional machine learning and deep learning methods to characterize selected pipe defects. The measurement conducted with ground-penetrating radar is depicted, employing a heuristic algorithm to estimate caverns behind the tested polymer composites. This study also presents results obtained through impedance spectroscopy, employed to characterize the delamination of polymer liners caused by uneven curing. A comparative analysis of these methods is conducted, assessing the accuracy by comparing the known positions of defects with their predicted characteristics based on laboratory measurements. Full article

Quasi-static indentation (QSI) experiments are conducted to investigate the localization, identification and evolution of induced damage in laminate composite up to delamination initiation using acoustic emission (AE) techniques. In this study, we propose a continuous wavelet transform (CWT)-based damage localization method for composites, which can simultaneously identify two damage modes, namely matrix cracking and delamination. The experimental findings demonstrate that the proposed algorithm, which utilizes the arrival time difference within a specific frequency band of the AE signal, effectively reduces the average location error from 3.81% to 2.31% compared to the existing method. Furthermore, the average signal location time has significantly decreased from several minutes to a mere 2 s. Matrix cracking and delamination are identified based on the maximum frequency band of CWT. Both types of damage exhibit prominent peaks within the 40 kHz–50 kHz frequency range, indicating their shared nature as manifestations of matrix damage, albeit with distinct modes of presentation. The first damage pattern that occurs is matrix cracking, succeeded by delamination damage. The nonlinear phase of the mechanical response curve is associated with the rapid aggregation of matrix cracking. Before the onset of macroscopic delamination damage, microscopic delamination damage begins to accumulate. A concentration of high-energy delamination damage signals predicts the initiation of macroscopic delamination. Full article

Delamination is a critical failure mode in power electronics packages that can significantly impact their reliability and performance, due to the large amounts of electrical power managed by the most recent devices which induce remarkable thermomechanical loads. The finite element (FE) simulation of this phenomenon is very challenging for the identification of the appropriate modeling tools and their subsequent calibration. In this study, we present an advanced FE modeling approach for delamination, together with fundamental guidelines to calibrate it. Considering a reference power electronics package subjected to thermomechanical loads, FE simulations with a global–local approach are proposed, also including the implementation of a bi-linear cohesive zone model (CZM) to simulate the complex interfacial behavior between the different layers of the package. A parametric study and sensitivity analysis is presented, exploring the effects of individual CZM variables on the delamination behavior, identifying the most crucial ones and accurately describing their underlying functioning. Then, this work gives valuable insights and guidelines related to advanced and aware FE simulations of delamination in power electronics packages, useful for the design and optimization of these devices to mitigate their vulnerability to thermomechanical loads. Full article

The timely diagnosis of defects at their incipient stage of formation is crucial to extending the life-cycle of technical appliances. This is the case of mechanical-related stress, either due to long aging degradation processes (e.g., corrosion) or in-operation forces (e.g., impact events), which might provoke detrimental damage, such as cracks, disbonding or delaminations, most commonly followed by the release of acoustic energy. The localization of these sources can be successfully fulfilled via adoption of acoustic emission (AE)-based inspection techniques through the computation of the time of arrival (ToA), namely the time at which the induced mechanical wave released at the occurrence of the acoustic event arrives to the acquisition unit. However, the accurate estimation of the ToA may be hampered by poor signal-to-noise ratios (SNRs). In these conditions, standard statistical methods typically fail. In this work, two alternative deep learning methods are proposed for ToA retrieval in processing AE signals, namely a dilated convolutional neural network (DilCNN) and a capsule neural network for ToA (CapsToA). These methods have the additional benefit of being portable on resource-constrained microprocessors. Their performance has been extensively studied on both synthetic and experimental data, focusing on the problem of ToA identification for the case of a metallic plate. Results show that the two methods can achieve localization errors which are up to 70% more precise than those yielded by conventional strategies, even when the SNR is severely compromised (i.e., down to 2 dB). Moreover, DilCNN and CapsNet have been implemented in a tiny machine learning environment and then deployed on microcontroller units, showing a negligible loss of performance with respect to offline realizations. Full article

It has been demonstrated that a possible solution to reducing delamination in a unidirectional composite laminate lies in the replacement of conventional carbon-fibre-reinforced polymer layers with optimized thin-ply layers, thus creating hybrid laminates. This leads to an increase in the transverse tensile strength of the hybrid composite laminate. This study investigates the performance of a hybrid composite laminate reinforced by thin plies used as adherends in bonded single lap joints. Two different composites with the commercial references Texipreg HS 160 T700 and NTPT-TP415 were used as the conventional composite and thin-ply material, respectively. Three configurations were considered in this study: two reference single lap joints with a conventional composite or thin ply used as the adherends and a hybrid single lap. The joints were quasi-statically loaded and recorded with a high-speed camera, allowing for the determination of damage initiation sites. Numerical models of the joints were also created, allowing for a better understanding of the underlying failure mechanisms and the identification of the damage initiation sites. The results show a significant increase in tensile strength for the hybrid joints compared to the conventional ones as a result of changes in the damage initiation sites and the level of delamination present in the joint. Full article

This report is on convolution neural network (CNN) fusion lock-in thermography, which can implement the intelligent identification of defects for aviation honeycomb sandwich composites (HSCs). First, HSCs specimens with subsurface delamination defects were fabricated and stimulated by halogen lamps according to sinusoidal modulation, and the defects were reliably inspected using lock-in thermography. The amplitude and phase images (commonly referred to as feature images) were obtained by using a digital lock-in correlation algorithm. Furthermore, these feature images were changed into gray or color-level image formalism datasets, which is pre-processed in ways including contrast enhancement, threshold segmentation as well as mosaic data augmentation. Finally, the four-layer feature pyramid structure and ransformer are combined and introduced to the popular YOLOv5 CNN model, and a YOLOLT CNN model is formed to realize the defect identification. The average precision (AP) in the defect identification of HSCs in complex environments (contains noise and other objects) reached 93.2% and achieved an average recognition speed of 0.6 s/image. Full article

Infrared thermography (IRT), is one of the most interesting techniques to identify different kinds of defects, such as delamination and damage existing for quality management of material. Objective detection and segmentation algorithms in deep learning have been widely applied in image processing, although very rarely in the IRT field. In this paper, spatial deep-learning image processing methods for defect detection and identification were discussed and investigated. The aim in this work is to integrate such deep-learning (DL) models to enable interpretations of thermal images automatically for quality management (QM). That requires achieving a high enough accuracy for each deep-learning method so that they can be used to assist human inspectors based on the training. There are several alternatives of deep Convolutional Neural Networks for detecting the images that were employed in this work. These included: 1. The instance segmentation methods Mask–RCNN (Mask Region-based Convolutional Neural Networks) and Center–Mask; 2. The independent semantic segmentation methods: U-net and Resnet–U-net; 3. The objective localization methods: You Only Look Once (YOLO-v3) and Faster Region-based Convolutional Neural Networks (Fast-er-RCNN). In addition, a regular infrared image segmentation processing combination method (Absolute thermal contrast (ATC) and global threshold) was introduced for comparison. A series of academic samples composed of different materials and containing artificial defects of different shapes and nature (flat-bottom holes, Teflon inserts) were evaluated, and all results were studied to evaluate the efficacy and performance of the proposed algorithms. Full article

(1) Background: A delaminated tear is described as a horizontal split in the tendon substance. This review summarizes the clinical and radiologic characteristics of delaminated tears of the rotator cuff. (2) Methods: Initial radiological characteristics of a delaminated tear include the horizontal component of a partial-thickness tear determined using magnetic resonance (MR) arthrography. As demonstrated using indirect MR arthrography, the tear gradually progresses to be defined as either horizontal intrasubstantial splitting of the bursal and articular layers or differential retraction of the bursal and articular layers. (3) Results: The existence of delaminated tears is a poor prognostic factor in functional and morphologic outcomes after the repair of rotator cuff tendons and many surgical techniques have been introduced to solve this problem. Although the presence of a delaminated tear does not affect the arthroscopic repair outcome, the presence of medium-to-large, retracted delaminated tears may be an adverse negative prognostic factor after single-row repair. (4) Conclusion: Advances in imaging and surgical techniques have improved the detection of delaminated rotator cuff tears. Preoperative identification of delaminated tears on magnetic resonance imaging is clinically important because tailored surgical repair techniques must be chosen for successful outcomes. Full article

The identification and severity of structural damages in carbon fiber reinforced polymer (CFRP), especially in the early stage, is critical in structural health monitoring (SHM) of composite materials. Among several approaches used to accomplish this goal, ultrasound inspection using Lamb waves has been taking place within non-destructive testing (NDT) methods. Likewise, the use of digital signal processing techniques for structural damage diagnosis has become popular due to the fact that it provides relevant information through feature extraction. In this context, this paper presents an alternative strategy based on the use of root mean square deviation (RMSD) and correlation coefficient deviation metric (CCDM) representative indices to extract the most sensitive information related to damage in CFRP plates through ultrasonic NDT signals in specific frequency ranges. In the experimental analysis, CFRP coupons were subjected to two types of damages: cracking and delamination. The signals, generated by piezoelectric transducers attached to the host structure using the pitch-catch method of Lamb waves, were subject to signal processing parameters based on the proposed approach. The results reveal that the proposed method was able to characterize the different types of damage in CFRP, as well as their severity in specific frequency bands. The results indicate the feasibility of the proposed method to detect and characterize damage in composite materials in a simple way, which is attractive for industrial applications. Full article

The Delamination Threshold Load (DTL) is a key parameter representing damage resistance of a laminate and is normally identified by locating a sudden drop in the impact force-time history for the laminate made of unidirectional layers. For the woven composite, however, their failure mechanisms appear different and the current literature is not providing any clear procedure regarding the identification of the delamination initiation, as well as the evolution of the failure mechanisms associated with it. In this paper, experimental data have been collected using woven glass and carbon fiber composites. The results are analyzed in terms of force-time and force-displacement curves. While delamination and other damages were clearly observed using ultrasonic scans, the analysis of the results does not reveal any trend changes of the curves that can be associated with the incipient nucleation of delamination. A preliminary discussion regarding the nature of the mechanisms through which the delamination propagates in woven composite and a justification for the absence of a sudden change of the stiffness have been presented. It raises a question on the existence of DTL for woven composites under low velocity impact. Full article