Search Results (21)

The Anderson localization of phonons in disordered superlattices has been proposed as a route to suppress thermal conductivity beyond the limits imposed by conventional scattering mechanisms. A commonly used signature of phonon localization is the emergence of the nonmonotonic dependence of thermal conductivity $\kappa$ on system length L, i.e., a $\kappa$-L maximum. However, such behavior has rarely been observed. In this work, we conduct extensive non-equilibrium molecular dynamics (NEMD) simulations, using the LAMMPS package, on both periodic superlattices (SLs) and aperiodic random multilayers (RMLs) constructed from Si/Ge and Lennard-Jones materials. By systematically varying acoustic contrast, interatomic bond strength, and average layer thickness, we examine the interplay between coherent and incoherent phonon transport in these systems. Our two-phonon model decomposition reveals that coherent phonons alone consistently exhibit a strong nonmonotonic $\kappa$-L. This localization signature is often masked by the diffusive, monotonically increasing contribution from incoherent phonons. We further extract the ballistic-limit mean free paths for both phonon types, and demonstrate that incoherent transport often dominates, thereby concealing localization effects. Our findings highlight the importance of decoupling coherent and incoherent phonon contributions in both simulations and experiments. This work provides new insights and design principles for achieving phonon Anderson localization in superlattice structures. Full article

The present study evaluates the viability of fabricating unmanned aerial vehicle (UAV) propellers using fused filament fabrication (FFF), with an emphasis on low-cost, desktop-scale production. The study’s backdrop is the recent adoption of UAVs and advancements in additive manufacturing. While the scope targets accessibility for individual and small-scale users, the results have broader implications for scalable UAV propulsion systems. The research was conducted within an experimental UAV development framework aimed at optimizing propeller performance through strategic material selection, geometrical design optimization, and additive manufacturing processes. Six propeller variants were manufactured using widely available thermoplastic polymers, including polyethylene terephthalate glycol-modified (PETG) and thermoplastic polyurethane (TPU), as well as photopolymer-based propellers fabricated using vat photopolymerization, also known as digital light processing (DLP). Mechanical and aerodynamic characterizations were performed to assess the structural integrity, flexibility, and performance of each material under dynamic conditions. Two blade configurations, a toroidal propeller with anticipated aerodynamic advantages and a conventional tri-blade propeller (Gemfan 51466-3)—were comparatively analyzed. The primary contribution of this work is the systematic evaluation of performance metrics such as thrust generation, acoustic signature, mechanical strength, and thermal stress imposed on the electrical motor, thereby establishing a benchmark for polymer-based propeller fabrication via additive manufacturing. The findings underscore the potential of polymeric materials and layer-based manufacturing techniques in advancing the design and production of UAV propulsion components. Full article

Acoustic emission (AE) is a powerful tool for investigating the intermittency of plastic flow by capturing elastic waves generated by dislocation rearrangements under load. This study explores the correlation between AE and plastic instabilities, such as Lüders bands, the Portevin–Le Chatelier (PLC) effect, and necking, each showing distinct AE signatures. Lüders and PLC bands generate significant AE during discontinuous yielding, with a sharp rise in AE levels and a shift in the spectrum to lower frequencies—characteristic of localized deformation. In contrast, necking exhibits limited AE activity, due to reduced strain hardening and dislocation mobility during late-stage deformation. A phenomenological model, based on dislocation dynamics and initially devised for uniform deformation, is discussed to explain the observed AE spectral features during localized plastic flow. This study underscores AE’s potential for non-destructive evaluation and failure prediction in structural metals, emphasizing its sensitivity to microstructural changes and instabilities. Understanding AE behavior across deformation stages offers valuable insights into improving material reliability and predicting failure. Full article

This paper presents the use of noncontact ultrasound for the nondestructive detection of defects in two plastic plates made of polyamide (PA6) and polyethylene (PE). The aim of the study was to: (1) assess the presence of defects as well as their size, type, and orientation based on the amplitudes of Lamb ultrasonic waves measured in plates made of polyamide (PA6) and polyethylene (PE) due to their homogeneous internal structure, which mainly determined the selection of such model materials for testing; and (2) verify the possibilities of building automatic quality control and defect detection systems based on ML based on the results of the above-mentioned studies within the Industry 4.0/5.0 paradigm. Tests were conducted on plates with generated synthetic defects resembling defects found in real materials such as delamination and cracking at the edge of the plate and a crack (discontinuity) in the center of the plate. Defect sizes ranged from 1 mm to 15 mm. Probes at 30 kHz were used to excite Lamb waves in the slab material. This method is sensitive to the slightest changes in material integrity. A significant decrease in signal amplitude was observed, even for defects of a few millimeters in length. In addition to traditional methods, machine learning (ML) was used for the analysis, allowing an initial assessment of the method’s potential for building cyber-physical systems and digital twins. By training ML models on ultrasonic data, algorithms can distinguish subtle differences between signals reflected from normal and defective areas of the material. Defect types such as voids, cracks, or weak bonds often produce distinct acoustic signatures, which ML models can learn to recognize with high accuracy. Using techniques like feature extraction, ML can process these high-dimensional ultrasonic datasets, identifying patterns that human inspectors might overlook. Furthermore, ML models are adaptable, allowing the same trained algorithms to work on various material batches or panel types with minimal retraining. This combination of automation and precision significantly enhances the reliability and efficiency of quality control in industrial manufacturing settings. The achieved accuracy results, 0.9431 in classification and 0.9721 in prediction, are comparable to or better than the AI-based quality control results in other noninvasive methods of flat surface defect detection, and in the presented ultrasonic method, they are the first described in this way. This approach demonstrates the novelty and contribution of artificial intelligence (AI) methods and tools, significantly extending and automating existing applications of traditional methods. The susceptibility to augmentation by AI/ML may represent an important new property of traditional methods crucial to assessing their suitability for future Industry 4.0/5.0 applications. Full article

Stored products, such as grains and processed foods, are susceptible to infestation by various insects. The early detection of insects in the supply chain is crucial, as introducing invasive pests to new environments may cause disproportionate harm. The STAR Center at Stevens Institute of Technology developed the Acoustic Stored Product Insect Detection System (A-SPIDS) to detect pests in stored products. The system, which comprises a sound-insulated container for product samples with a built-in internal array of piezoelectric sensors and additional electret microphones to record outside noise, was used to conduct numerous measurements of the vibroacoustic signatures of various insects, including the Callosobruchus maculatus, Tribolium confusum, and Tenebrio molitor, in different materials. A normalization method was implemented using the ambient noise of the sensors as a reference, to accommodate for the proprietary, non-calibrated sensors and allowing to set relative detection thresholds for unknown sensitivities. The normalized envelope of the filtered signals was used to characterize and compare the insect signals by estimating the Normalized Signal Pulse Amplitude (NSPA) and the Normalized Spectral Energy Level (NSEL). These parameters characterize the insect detection Signal Noise Ratio (SNR) for pulse-based detection (NSPA) and averaged energy-based detection (NSEL). These metrics provided an initial step towards the design of a reliable detection algorithm. In the conducted tests NSPA was significantly larger than NSEL. The NSPA reached 70 $\mathrm{d}$$\mathrm{B}$ for T. molitor in corn flakes. The insect signals were lower in flour where the averaged NSPA and NSEL values were around 40 dB and 11 $\mathrm{d}$$\mathrm{B}$ to 16 $\mathrm{d}$$\mathrm{B}$, respectively. Full article

The research described in this paper focuses on the development of an innovative unmanned aerial vehicle (UAV) tailored for a specific mission: detecting the acoustic signature emitted by chainsaws, identifying deforestation, and reporting its location for legality assessment. Various calculations were conducted to determine the optimal solution, resulting in the choice of a fixed-wing UAV. A comparative analysis between tri-rotor and quadcopter systems was performed, leading to the selection of the tri-rotor configuration. The primary objective of this study is to design an innovative hybrid UAV concept with key features including a fixed-wing design and integrated VTOL (vertical takeoff and landing) capability in the experimental model. The aircraft has been constructed using advanced materials such as fiber-reinforced polymer composites, manufactured using both conventional and advanced techniques like continuous fiber additive manufacturing and the use of a polymer matrix. Additionally, the aerodynamic configuration is optimized to achieve a cruise speed of approximately 50 km/h and a flight autonomy exceeding 3 h. The UAV has been equipped with payloads for mounting sensors to collect meteorological data, and crucially, the VTOL system has been optimized to vectorize thrust for improved performance during the transition from hover to cruise flight. This paper details the entire manufacturing and assembly process of the drone, covering both the structural framework and associated electrical installations. A dedicated sound detection system is incorporated into the drone to identify chainsaw noise, with the aim of preventing deforestation. Full article

Structural health monitoring is a popular inspection method that utilizes acoustic emission (AE) signals for fault detection in engineering infrastructures. Diagnosis based on the propagation of AE signals along any surface material offers an attractive solution for fault identification. However, the classification of AE signals originating from failure events, especially coating failure (coating disbondment), is a challenging task given the AE signature of each material. Thus, different experimental settings and analyses of AE signals are required to classify the various types of coating failures, and they are time-consuming and expensive. Hence, to address these issues, we utilized machine learning (ML) classification models in this work to evaluate epoxy-based-protective-coating disbondment based on the AE principle. A coating disbondment experiment consisting of coated carbon steel test panels for the collection of AE signals was implemented. The obtained AE signals were then processed to construct the final dataset to train various state-of-the-art ML classification models to divide the failure severity of coating disbondment into three classes. Consequently, methods for the extraction of useful features, the handling of data imbalance, and a reduction in the bias of ML models were also effectively utilized in this study. Evaluations of state-of-the-art ML classification models on the AE signal dataset in terms of standard metrics revealed that the decision forest classification model outperformed the other state-of-the-art models, with accuracy, precision, recall, and F1 score values of 99.48%, 98.76%, 97.58%, and 98.17%, respectively. These results demonstrate the effectiveness of utilizing ML classification models for the failure severity prediction of protective-coating defects via AE signals. Full article

The direct tactile assessment of surface textures during palpation is an essential component of open surgery that is impeded in minimally invasive and robot-assisted surgery. When indirectly palpating with a surgical instrument, the structural vibrations from this interaction contain tactile information that can be extracted and analysed. This study investigates the influence of the parameters contact angle $\alpha$ and velocity $\overrightarrow{v}$ on the vibro-acoustic signals from this indirect palpation. A 7-DOF robotic arm, a standard surgical instrument, and a vibration measurement system were used to palpate three different materials with varying $\alpha$ and $\overrightarrow{v}$. The signals were processed based on continuous wavelet transformation. They showed material-specific signatures in the time–frequency domain that retained their general characteristic for varying $\alpha$ and $\overrightarrow{v}$. Energy-related and statistical features were extracted, and supervised classification was performed, where the testing data comprised only signals acquired with different palpation parameters than for training data. The classifiers support vector machine and k-nearest neighbours provided $99.67\%$ and $96.00\%$ accuracy for the differentiation of the materials. The results indicate the robustness of the features against variations in the palpation parameters. This is a prerequisite for an application in minimally invasive surgery but needs to be confirmed in realistic experiments with biological tissues. Full article

The modification of interatomic distances due to high pressure leads to exotic phenomena, including metallicity, superconductivity and magnetism, observed in materials not showing such properties in normal conditions. In two-dimensional crystals, such as graphene, atomic bond lengths can be modified by more than 10 percent by applying in-plane strain, i.e., without generating high pressure in the bulk. In this work, we study the strain-induced Mott transition on a honeycomb lattice by using computationally inexpensive techniques, including the Gutzwiller Wave Function (GWF) and different variants of Gutzwiller Approximation (GA), obtaining the lower and upper bounds for the critical Hubbard repulsion (U) of electrons. For uniaxial strain in the armchair direction, the band gap is absent, and electron correlations play a dominant role. A significant reduction in the critical Hubbard U is predicted. Model considerations are mapped onto the tight-binding Hamiltonian for monolayer graphene by the auxiliary Su–Schrieffer–Heeger model for acoustic phonons, assuming zero stress in the direction perpendicular to the strain applied. Our results suggest that graphene, although staying in the semimetallic phase even for extremely high uniaxial strains, may show measurable signatures of electron correlations, such as the band narrowing and the reduction in double occupancies. Full article

A recently proposed, game-changing transformative energetics concept based on predictive synthesis and preprocessing at the nanoscale is considered as a pathway towards the development of the next generation of high-end nanoenergetic materials for future multimode solid propulsion systems and deep-space-capable small satellites. As a new door for the further performance enhancement of transformative energetic materials, we propose the predictive ion-assisted pulse-plasma-driven assembling of the various carbon-based allotropes, used as catalytic nanoadditives, by the 2D-ordered linear-chained carbon-based multicavity nanomatrices serving as functionalizing nanocarriers of multiple heteroatom clusters. The vacant functional nanocavities of the nanomatrices available for heteroatom doping, including various catalytic nanoagents, promote heat transfer enhancement within the reaction zones. We propose the innovative concept of fine-tuning the vibrational signatures, functionalities and nanoarchitectures of the mentioned nanocarriers by using the surface acoustic waves-assisted micro/nanomanipulation by the pulse-plasma growth zone combined with the data-driven carbon nanomaterials genome approach, which is a deep materials informatics-based toolkit belonging to the fourth scientific paradigm. For the predictive manipulation by the micro- and mesoscale, and the spatial distribution of the induction and energy release domains in the reaction zones, we propose the activation of the functionalizing nanocarriers, assembled by the heteroatom clusters, through the earlier proposed plasma-acoustic coupling-based technique, as well as by the Teslaphoresis force field, thus inducing the directed self-assembly of the mentioned nanocarbon-based additives and nanocarriers. Full article

Gamma titanium aluminide (γ-TiAl) is considered a high-performance, low-density replacement for nickel-based superalloys in the aerospace industry due to its high specific strength, which is retained at temperatures above 800 °C. However, low damage tolerance, i.e., brittle material behavior with a propensity to rapid crack propagation, has limited the application of γ-TiAl. Any cracks introduced during manufacturing would dramatically lower the useful (fatigue) life of γ-TiAl components, making the workpiece surface’s quality from finish machining a critical component to product quality and performance. To address this issue and enable more widespread use of γ-TiAl, this research aims to develop a real-time non-destructive evaluation (NDE) quality monitoring technique based on acoustic emission (AE) signals, wavelet transform, and deep neural networks (DNN). Previous efforts have opted for traditional approaches to AE signal analysis, using statistical feature extraction and classification, which face challenges such as the extraction of good/relevant features and low classification accuracy. Hence, this work proposes a novel AI-enabled method that uses a convolutional neural network (CNN) to extract rich and relevant features from a two-dimensional image representation of 1D time-domain AE signals (known as scalograms), subsequently classifying the AE signature based on pedigreed experimental data and finally predicting the process-induced surface quality. The results of the present work show good classification accuracy of 80.83% using scalogram images, in-situ experimental data, and a VGG-19 pre-trained neural network, establishing the significant potential for real-time quality monitoring in manufacturing processes. Full article

Belt conveyors are commonly used for the transportation of bulk materials. The most characteristic design feature is the fact that thousands of idlers are supporting the moving belt. One of the critical elements of the idler is the rolling element bearing, which requires monitoring and diagnostics to prevent potential failure. Due to the number of idlers to be monitored, the size of the conveyor, and the risk of accident when dealing with rotating elements and moving belts, monitoring of all idlers (i.e., using vibration sensors) is impractical regarding scale and connectivity. Hence, an inspection robot is proposed to capture acoustic signals instead of vibrations commonly used in condition monitoring. Then, signal processing techniques are used for signal pre-processing and analysis to check the condition of the idler. It has been found that even if the damage signature is identifiable in the captured signal, it is hard to automatically detect the fault in some cases due to sound disturbances caused by contact of the belt joint and idler coating. Classical techniques based on impulsiveness may fail in such a case, moreover, they indicate damage even if idlers are in good condition. The application of the inspection robot can “replace” the classical measurement done by maintenance staff, which can improve the safety during the inspection. In this paper, the authors show that damage detection in bearings installed in belt conveyor idlers using acoustic signals is possible, even in the presence of a significant amount of background noise. Influence of the sound disturbance due to the belt joint can be minimized by appropriate signal processing methods. Full article

On mixed sand–gravel beaches, impacts from gravel- and cobble-sized grains—mobilized by the energetic shorebreak—limit the utility of in situ instrumentation for measuring the small-scale response of the beach face on wave period time scales. We present field observations of swash zone morpho-sedimentary dynamics at a steep, megatidal mixed sand–gravel beach using aeroacoustic and optical remote sensing. Coincident observations of bed level and mean surficial sediment grain size in the swash zone were obtained using an array of optical cameras paired with acoustic range sensors. Lagrangian tracking of swash-transported cobbles was carried out using an additional downward-oriented camera. The principal objective of the study was to investigate linkages between sediment grain size dynamics and swash zone morphological change. In general, data from the range sensor and camera array show that increases in bed level corresponded to increases in mean grain size. Finer-scale structures in the bed level and mean grain size signals were observable over timescales of minutes, including signatures of bands of coarse-grained material that migrated shoreward with the leading edge of the swash prior to high tide berm formation. The direction and magnitude of cobble transport in the swash varied with cross-shore position, and with the composition of the underlying bed. These results demonstrate that close-range remote sensing techniques can provide valuable insights into the roles of cobble-sized versus sand-sized particle dynamics in the swash zone on mixed sand–gravel beaches. Full article

The purpose of this work is to analyze the damage process resulting from buckling load applied on composites reinforced by flax fibre. Continous buckling test was performed on specimens until cracks appeared on their outer face. This test was monitored with an acoustic emission system. The high sensitivity of this method allows the detection of any process or mechanism generating sound waves. Moreover, this technic has the advantage of not causing contact in the deformed zone and thus to overcome the parasitic damage that may result from the stress concentrations in these areas. A multiparametric analysis is used to identify the acoustic signatures corresponding to each damage mechanism involved in the materials, and then follow their evolution in order to identify the most critical mechanisms leading to the final breakage of the material. The presence of these damage mechanisms was confirmed post-test by microscopic observations. Three orientations of laminate specimens (0°, 90° and 45°), relative to flax fabric architecture, were tested in order to characterize and highlight on their own damage process. Similarities as differences were observed between these mechanisms. We have deduced that the high porosity rate found in our composites are resulting from manufacturing parameters. Architecture and properties of the flax fabric influenced negatively the mechanical properties later by accentuating the gap between theoretical and practical values (17% to 22.4%) and by accelerating the development of certain damages such as matrix cracking which acoustic hit density is superior to 70% and fiber/matrix decohesion which occurs very early. Full article

This paper deals with the development of an original apparatus called TRIBOLUMEN designed specifically for friction experiments on transparent materials. The friction measurement is synchronized with an acoustic emission (AE) sensor and the device is also equipped with a high-speed camera offering a direct view at the interface to gain a deeper understanding of tribological mechanisms. The TRIBOLUMEN device is in ball-on-flat contact configuration with a range of strokes from 5 to 500 µm and an oscillation frequency from 5 to 600 Hz. The experiments showed that this device has an adequate rigidity and can detect subtle friction modifications of the oscillating contacts. The observation of a steel-on-glass contact in real-time highlighted the initiation of Hertzian cracks followed by the formation of debris in the contact. Using the synchronous measurement, these mechanisms were clearly associated with different stages in the friction measurement and in the AE signals, which permitted to identify the AE signature of Hertzian cracks. Full article