Search Results (3,389)

Ultra-precision thinning of hard and brittle materials like monocrystalline silicon demands high dynamic stability in thinning spindle. To address the challenge of accurately detecting subtle spindle chatter anomalies in industrial environments characterized by high noise and limited data, this paper proposes a physics-guided dual-stream attention fusion transfer network (PG-AFNet). First, a physics-guided signal preprocessing method was developed. Using variational mode decomposition (VMD) and continuous wavelet transform (CWT) masking, one-dimensional dynamic features and high-frequency regions of interest (ROIs) rich in transient impact features were extracted. Second, the PG-AFNet architecture was designed. By introducing an attention mechanism, it achieves deep integration of one-dimensional purely dynamic sequences with two-dimensional spatiotemporal visual textures to capture surface damage features caused by subtle vibrations. Finally, systematic validations were conducted using a real silicon wafer thinning dataset with 197 real samples. By overcoming small-sample limitations via physical augmentation, PG-AFNet achieved an 82.45% (86.64% after data augmentation) diagnostic accuracy, significantly outperforming traditional baselines. Furthermore, a large-scale cross-load validation on the diverse CWRU dataset yielded an exceptional 99.68% accuracy under mixed-load conditions, conclusively verifying the model’s robust domain generalization. Lastly, a rigorous ablation study explicitly quantified the indispensable contributions of the physics-guided dual-stream architecture and attention fusion. This research provides a feasible theoretical foundation for intelligent surface quality monitoring in semiconductor hard-brittle material processing. Full article

This study investigated the influence of hyperparameter selection within an EEMD–AR–SVM framework for bearing fault diagnosis under constant- and variable-speed operating conditions. Two preprocessing configurations, namely, Method 1, in which EEMD was applied after segmentation, and Method 2, in which EEMD preceded segmentation, were evaluated under three rotational regimes—constant speed, acceleration (Test A), and deceleration (Test B)—while number of Intrinsic Mode Functions (N), autoregressive model order (L), and segment length were systematically varied towards identifying combinations that maximized classification accuracy. The results showed the methods achieved 100% accuracy under constant-speed operation. However, Method 2 consistently outperformed Method 1 under nonstationary regimes, reaching 94.12% accuracy during acceleration and 95.00% during deceleration. The outer race remained the most challenging fault type, although its separability substantially improved when EEMD was performed prior to segmentation. The findings demonstrated, in a clear and interpretable manner, that the empirical choice of N and L directly affects classifier accuracy in stationary and nonstationary scenarios and the order of preprocessing steps plays a decisive role in diagnostic reliability. Such contributions provide a reproducible methodological basis for advancing vibration-based fault diagnosis and support the development of interpretable, high-performance predictive maintenance strategies for industrial environments. Full article

This study aims to determine the current seismic resistance of two masonry minarets that were severely damaged during the 6 February 2023 Kahramanmaraş earthquakes, while also evaluating whether a model-updating approach based on experimental dynamic characteristics can reliably capture the actual seismic behavior and collapse mechanism of such structures under real earthquake conditions. The dynamic characteristics of the minarets were identified using Operational Modal Analysis (OMA) based on previous in-situ vibration measurements. These characteristics were used to calibrate finite element models through a model-updating process employing Multi-Criteria Decision-Making (MCDM) methods. The initial modal analyses revealed discrepancies of up to 13.7% in natural frequencies and 9.7% in mode shapes. After applying MCDM methods to a wide set of model variants, these differences were reduced to 2.0% and 9.2%, respectively, improving the agreement between numerical and experimental results. Once the most representative models were obtained, nonlinear seismic analyses were performed using actual ground motion records from the earthquake. The results included evaluations of peak displacements, base shear forces, and principal stresses. The concentration of principal stresses near the transition zone showed good qualitative agreement with the observed collapse locations, indicating a reasonable consistency between numerical results and observed damage patterns. These findings demonstrate the value of integrating OMA-based model updating with MCDM methods and support a data-driven framework for assessing the seismic performance of historical masonry structures. Full article

Coherent Raman scattering (CRS) techniques such as coherent anti-Stokes Raman scattering (CARS) provide chemically specific vibrational contrast with signal levels far exceeding spontaneous Raman scattering (SpRS). Extending these to broadband excitation enables multiplex detection across wide spectral regions, including the fingerprint region, CH-stretch bands and high-frequency vibrational modes. This review provides a structured overview of excitation architecture for broadband CRS, ranging from low-energy oscillator schemes to energy-scalable platforms. The discussion is organized along key design parameters, including spectral bandwidth, excitation intensity, and probe delay, which jointly determine the accessible operating regimes. Rather than representing competing methods, the reviewed architectures are presented as a complementary toolbox for application-driven spectroscopy in chemically reactive environments and complex biological systems. In addition, a representative OPCPA-based implementation is presented as a platform demonstration to illustrate accessible operating regimes, single-shot stability, and multiplex detection capability under realistic experimental conditions. Full article

This study investigates the mechanisms of nonlinear modal interactions in a circularly curved cantilever beam, utilizing the geometrically exact Timoshenko beam formulation. The governing equations take into account shear deformation, rotary inertia, and the geometric nonlinearities associated with significant deflections. A Chebyshev pseudospectral scheme is employed to achieve highly accurate linear eigenvalues, which are subsequently used in a nonlinear modal projection to develop a reduced-order model. Explicit expressions for the quadratic and cubic modal coupling coefficients are derived. The Harmonic Balance Method is then applied to explore internal resonance phenomena, frequency modulation behavior, and the transfer of energy between non-commensurate lateral and normal vibration modes. Full article

Metastructures, waveguides composed of multiple unit cells (meta-atoms), have gained significant attention for controlling wave propagation in engineering applications, especially in the context of elastic and acoustic waves. However, existing metastructures often lack sufficient tunable functionality to dynamically control both elastic vibration and acoustic wave transmission using a single external parameter. This study introduces a phase-change material (PCM)-embedded meta-atom, where a core mass is connected to an outer shell by Archimedean spiral bridges. The solid–liquid phase transition of PCM induces a notable change in the effective shear modulus, enabling dynamic wave control. The mechanism for bandgap formation transitions from Bragg scattering in the solid PCM state to local resonance in the liquid state. Core rotation, driven by the phase transition, is key to generating flat bands and low-frequency locally resonant bandgaps at high temperatures. Temperature-dependent, mode-selective transmission behavior is observed, with transverse vibrations and acoustic waves exhibiting opposite blocking and transmission characteristics at the same frequency. This design provides a promising approach for decoupling sound and vibration management, using temperature control driven by the PCM phase transition. The work contributes to multifunctional metastructures with applications in adaptive noise control, structural health monitoring, and tunable vibration isolation systems. Full article

Existing studies on transverse phonon-polaritons in one-dimensional piezoelectric superlattices, based on the assumption of infinite lateral dimensions (perpendicular to the periodic direction of ferroelectric domains), have shown that only transverse superlattice vibrations with a strain component along the periodic direction can couple with electromagnetic waves to generate transverse phonon-polaritons. Real samples, however, inevitably have finite lateral dimensions, indicating that the infinite-lateral-size model requires modification. In this study, we find that in laterally finite systems, pure transverse superlattice vibrations (those without any strain component along the periodic direction) can also couple with electromagnetic waves, giving rise to a new class of pure transverse phonon-polaritons. Theoretical analysis reveals that the energy of this mode is primarily confined to the crystal surface and propagates as surface waves. Experimental verification confirms the existence of this polariton, and this result provides a new degree of freedom for the design of microwave devices based on piezoelectric superlattices. Full article

In this paper, a drive–vibration integrated piezoelectric actuator (DVIPA) is proposed for vibration-assisted implantation of flexible electrodes. Conventional implantation systems typically rely on separate actuation and vibration modules, which increase system complexity and limit integration. To address this limitation, the proposed DVIPA integrates driving and vibration functions within a single compact structure by employing two piezoelectric bimorphs for clamping and a piezoelectric stack for combined actuation. A composite excitation waveform, consisting of high-frequency sinusoidal signals superimposed on the rising stage of a low-frequency trapezoidal wave, is applied to simultaneously generate forward motion and vibration. This configuration enables a coupled motion mode that facilitates insertion while reducing the risk of buckling. A prototype of the DVIPA was developed and experimentally evaluated. The results show that vibration-assisted implantation can be achieved under various operating conditions, with independently adjustable driving and vibration parameters. A maximum speed of 328 μm/s is obtained, meeting the requirements for flexible electrode implantation. Agarose gel experiments further demonstrate that vibration frequencies above 40 Hz and voltages between 20 and 40 V can effectively assist implantation of polydimethylsiloxane (PDMS) without buckling failure. Overall, the proposed actuator provides a compact and integrated solution for vibration-assisted implantation, offering potential advantages in applications with limited space. Full article

Metallic Shear Plate Dampers (MSPDs) are essential components in passive vibration control systems and require rapid post-earthquake inspection to assess damage and determine replacement needs. Traditional visual inspection methods suffer from low efficiency and limited ability to detect concealed damage. This study proposes a novel MSPD damage detection method based on active sensing and the k-nearest neighbor (KNN) algorithm, featuring high accuracy, efficiency, and low cost. Quasi-static tests were conducted to simulate various damage states. Sweep-frequency excitation was applied using a charge amplifier, and piezoelectric sensors were employed to generate and receive stress wave signals corresponding to different damage conditions. The acquired signals were processed using wavelet packet transform (WPT) and energy spectrum analysis to extract discriminative time–frequency features, which were used to train and validate the KNN model. Results show that the model achieved a validation accuracy of 98.9% using all valid data and 98.1% using a single excitation-sensing channel. When tested on an MSPD with a similar overall structure but lacking stiffeners, the model achieved an accuracy of 92.6% in distinguishing between healthy and damaged states. This indicates that the proposed method has good robustness and practical potential for MSPDs with similar damage evolution and failure modes despite certain structural variations. Full article

The concepts of food matrix and holistic analysis have been used in a wide range of scientific disciplines to describe the sum of the parts of a whole that provide a specific property or functionality to the sample. Traditional chemical and physical analysis needs to destroy the sample (e.g., dilution, extraction, drying) before analysis. The utilization of vibrational spectroscopy techniques, like near (NIR), mid infrared (MIR) and Raman spectroscopy, allows for the non-destructive analysis of food ingredients and products. The resulting output of this analysis is based on the information provided by the vibrational modes of atoms present in the different molecules, allowing the measurement of different chemical and physical characteristics of the food. The objective of this paper is to discuss the ability of vibrational spectroscopy methods to provide robust tools to analyze the food matrix holistically, moving away from the traditional analysis of individual compounds or chemical parameters. Studies discussed and presented in this review demonstrated the ability of vibrational spectroscopy (e.g., NIR, MIR and Raman spectroscopy, hyperspectral imaging) to assess the whole food matrix beyond the traditional notion of developing a calibration model. Full article

This study investigates the effectiveness of various feature extraction methods applied to vibration signals for the automatic detection of production defects in CNC (Computerised Numerical Control) milling machines. A dataset consisting of real-world data collected from CNC machines equipped with accelerometers was used. The objective of the study is to compare three main groups of techniques: time-domain analysis (TDA), frequency-domain analysis (FDA), and time–frequency-domain analysis (TFA). The findings indicate that basic TDA features lack the necessary sensitivity to accurately distinguish between Good Processing (GP) and Bad Processing (BP) states. Frequency-domain methods, such as the Fast Fourier Transform (FFT), median frequency calculation, and the Welch periodogram, provide better insights but still have limitations. The most effective results are obtained with TFA methods, particularly Empirical Mode Decomposition (EMD) and the Hilbert–Huang Transform (HHT), which reveal deeper signal characteristics. Following the feature optimisation studies, it was determined that a combination of four features—FMED, IMF2, IMF5 and WPT26—yielded the optimal performance, with an accuracy of 91.48%. The incorporation of a fifth feature resulted in information saturation within the model and did not improve performance. This study makes a novel contribution to literature by conducting an in-depth investigation into the most effective feature extraction and selection techniques for achieving robust discrimination between GP and BP productions using vibration signals in CNC milling processes. Conclusively, TFA features, supported by advanced signal processing, offer a strong basis for reliable, automated defect detection in CNC milling operations. Full article

This work investigates the influence of sodium promotion on Ag/m-ZrO₂ catalysts for methanol steam reforming (MSR), focusing on activity, selectivity, surface chemistry, and mechanistic pathways. Temperature programmed reduction (TPR), XANES/EXAFS, CO₂ TPD, DRIFTS, and temperature programmed surface reaction methods were combined with fixed bed MSR testing to develop an integrated structure–function understanding of Na-modified Ag-ZrO₂ interfaces. Na addition systematically increases surface basicity, stabilizes strongly basic O²⁻ sites, and weakens the ν(CH) vibrational mode of surface formate, thereby facilitating C–H bond scission and accelerating decarboxylation to CO₂. At moderate promoter levels (0.5–1.0 wt.% Na), the catalysts show significantly enhanced CO₂ selectivity and increased conversion relative to unpromoted Ag/m-ZrO₂, while CH₄ formation remains negligible. Excessive Na (≥1.8 wt.%) leads to slower formate decomposition, greater carbonate stabilization, and suppressed conversion, revealing a narrow optimum around 1 wt.% Na. Short-term stability testing demonstrates steady conversion and product selectivity for both unpromoted and Na-promoted catalysts, with the latter maintaining markedly higher CO₂ selectivity. Although Pt/YSZ retains far superior intrinsic activity at ~10× higher space velocity, Ag offers a cost-advantaged alternative where lower cost metals are desirable. Collectively, these findings show that Na promotion enables tunable MSR selectivity on Ag/m-ZrO₂ by directing formate decomposition toward the CO₂-forming pathway. Full article

Due to the non-stationary and nonlinear characteristics of diesel engine vibration signals, fault features cannot be fully extracted, which limits fault diagnosis performance. To address this issue, an improved fault diagnosis method combining optimized Variational Mode Decomposition with a Transformer and Support Vector Machine is proposed. An improved dung beetle optimization algorithm is employed to obtain optimal parameters for Variational Mode Decomposition. The envelope entropy minimization principle is applied to select the optimal intrinsic mode functions after Variational Mode Decomposition, achieving signal denoising. Analysis of variance is integrated for feature significance testing to screen critical features. The selected features are fed into a Transformer network for training. At the final classification stage, the traditional SoftMax classifier is replaced with a Support Vector Machine classifier. Full article

In this work, the electronic, thermochemical, and vibrational characterization of the drug delivery system formed by clusters of selenium (Se₆ allotrope) and 5-fluorouracil (5-FU) are studied, based on density functional theory (DFT) calculations. Computational calculations were performed using the B3LYP functional and the 6-31G(d,p) base set, considering an aqueous medium through the CPCM solvation model. We propose evaluating two different interaction modes based on experimental observations: Se–H(N) (through the amino groups of 5-FU) and Se–O(C) (through the carbonyl oxygen of 5-FU). All complexes proved to be energetically stable, exhibiting chemisorption as their adsorption process. Analysis of adsorption energy and thermodynamic parameters indicates that both interaction pathways are equally viable, which agrees with previous experimental findings. The theoretical FT-IR spectra of these complexes also coincide with the experimental results. Furthermore, global molecular descriptors show that the stability of the selenium carrier is not affected by post-functionalization, which is desirable for more controlled drug delivery systems. Full article

Structural health monitoring (SHM) is essential for the safety and long-term performance of civil and mechanical infrastructure, yet traditional vibration-based approaches often struggle with nonlinear behavior and environmental variability. Koopman operator theory provides a promising alternative by enabling linear analysis of nonlinear structural dynamics through observable functions. This review examines 67 peer-reviewed studies published between 2010 and 2025 and selected using Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. We outline the development of Koopman-based methods from Dynamic Mode Decomposition (DMD) and Extended-DMD (EDMD) to recent applications in civil, mechanical, and aerospace systems. This review clarifies the mathematical foundations of Koopman analysis and its relationship to structural dynamics. It also identifies major research gaps, including limited damage-sensitive observable design, insufficient use of structural mechanics constraints, the absence of quantitative links between Koopman spectra and physical damage, inadequate benchmarking, and the need for real-time deployment strategies. We conclude by outlining a hybrid Koopman framework that integrates physics-based information with data-driven learning to support interpretable and scalable SHM. Full article