Search Results (1,267)

In autonomous manufacturing systems, the performance of time-series-based anomaly detection and fault diagnosis is highly sensitive to window size selection. Conventional approaches rely on empirical rules or fixed window settings, which often fail to capture the diverse temporal characteristics of anomalies and lead to performance degradation. This study systematically addresses the window size selection problem by categorizing anomaly patterns into three representative types: variability, cycle, and local spike. Each pattern is associated with a distinct temporal scale and underlying physical mechanism. Based on this insight, an Anomaly Deviation-Based Window Size Selection (ADW) method is proposed, which quantitatively evaluates anomaly deviation as a function of window size. Unlike traditional preprocessing-oriented approaches, the proposed method redefines window size as a core design variable that directly governs anomaly representation and diagnostic sensitivity. The effectiveness of the ADW method is validated using tension data from a roll-to-roll continuous manufacturing process and vibration data from a rotating bearing fault dataset. Experimental results demonstrate that the proposed approach consistently identifies optimized window sizes tailored to different anomaly types, leading to improved fault classification accuracy and diagnostic robustness. The proposed framework provides a physically interpretable and data-driven guideline for adaptive window size selection in long-term autonomous manufacturing systems. Full article

This study investigated the dynamic behavior of AA7075 plates joined by Friction Stir Welding (FSW), focusing on the influence of key process parameters, rotation, and traverse speeds, on the resulting dynamic characteristics. Experimental Modal Analysis (EMA) was performed under free boundary conditions to determine resonance frequencies, mode shapes, and damping ratios, revealing that an increase in traverse speed consistently led to a decrease in natural frequencies across most modes, thereby indicating reduced joint stiffness attributed to insufficient heat input. Furthermore, localized weld defects caused significant damping variations, particularly in low-order modes. To complement the experimental findings and enable simultaneous, multi-output prediction of these coupled dynamic parameters, a Co-Active Neuro-Fuzzy Inference System (CANFIS) model was developed. The CANFIS architecture utilized spindle speed and feed rate as inputs to predict natural frequency and damping ratio for multiple vibration modes as tightly coupled outputs. The trained model demonstrated strong agreement and high predictive accuracy against the EMA experimental data, with convergence analysis confirming its stable learning and excellent generalization capability. The successful integration of EMA and CANFIS establishes a robust hybrid framework for both physical interpretation and intelligent, coupled prediction of the dynamic behavior of FSW-welded AA7075 plates. Full article

To compensate for hysteresis, low damping vibration, and their coupling effects, this paper proposes a gated recurrent unit and convolutional neural network (GRU-CNN) model as a feedforward control model that maps desired displacement trajectories to driving voltages. The GRU-CNN integrates a gated recurrent unit (GRU) layer to capture long-term temporal dependencies, a multi-layer convolutional neural network (CNN) to extract local data features, and residual connections to mitigate information distortion. The GRU-CNN is then combined with transfer learning (TL) for feedforward control of cross-batch and cross-type piezoelectric actuators (PEAs), so as to reduce reliance on training datasets. The analysis focuses on the impacts of target PEA data volume and source-target similarity on transfer learning strategies. The GRU-CNN trained on PEA #1 achieves high control accuracy, with a mean absolute error (MAE) of 0.077, a root mean square error (RMSE) of 0.129, and a coefficient of determination (R²) of 0.997. When transferred to cross-batch PEA #2 and cross-type PEA #3, the GRU-CNN feedforward controller still delivers favorable performance; R² values all exceed 0.98, representing at least a 27% improvement compared to training from scratch. These results indicate that the proposed transfer learning-based feedforward control method can effectively reduce retraining effort, suggesting its potential applicability to batch production scenarios. Full article

In contemporary semiconductor manufacturing, wafer-handling robots are essential for achieving high-speed and high-precision wafer transportation. However, the demand for rapid motion and lightweight design introduces flexible transmission components that are prone to residual vibrations, which degrade positioning accuracy and system stability. To address this challenge, this paper proposes a vibration-suppression trajectory planning method based on the Gray Goose Optimization (GGO) algorithm. The proposed algorithm integrates grouped global search with local optimization capabilities, making it well suited for solving multi-objective optimization problems. Comparative tests conducted on eight randomly selected multimodal benchmark functions from the CEC2013 test suite verify the effectiveness and robustness of the GGO algorithm. Establishing a multi-objective function that considers both motion time and vibration energy enables the GGO algorithm to determine the switching time points of an S-shaped velocity profile, thereby generating smooth trajectories with continuous velocity and acceleration. By varying different initial conditions, the trade-off between motion time and vibration energy is systematically analyzed with respect to angular displacement, initial acceleration, and time-weighting factors. Simulation results indicate that the planned trajectories exhibit negligible displacement variation under zero-mean disturbances. The velocity error remains within 0.1 deg·s⁻¹, and the acceleration error is confined within 0.2 deg·s⁻². Consequently, Pareto-optimal solutions are successfully obtained with respect to both motion time and residual vibration energy. Full article

Functional ankle instability (FAI) is a common consequence of lateral ankle sprains, characterized by impaired sensorimotor control. While orthoses and localized vibration have shown individual benefits for FAI, their combined application in a wearable device has not been previously investigated. This pilot randomized trial compared the effects of a vibrotactile foot orthosis (VFO) and a vibrotactile ankle orthosis (VAO) on joint position sense (JPS) and postural control in individuals with FAI. Sixteen participants were randomized to receive either a VFO or a VAO, both delivering 30–50 Hz pulsed vibration in 20 min sessions, three times a week, for two weeks. Outcome measures included joint position sense (JPS) error (°), center of pressure (COP) velocity (mm/s), the Star Excursion Balance Test (SEBT), and the Six-Meter Hop Test (SMHT), which were assessed pre-intervention, immediately post-intervention, and after two weeks of use. The analysis showed a statistically significant interaction between time and intervention group for JPS error (p = 0.02, η² = 0.42). Specifically, the VFO group improved JPS significantly more than VAO at two weeks follow-up (MD = −1.75°, p = 0.005, d = −1.68). Both groups significantly reduced in anteroposterior COP velocity after two weeks (VFO: MD = 1, p = 0.003, d = 1.47; VAO: MD = 1.39, p ˂ 0.001, d = 2.05) with no between-group differences. No changes were observed in the SEBT or SMHT. Plantar-based vibrotactile stimulation was more effective than ankle-based stimulation in enhancing proprioceptive acuity in individuals with FAI. Both interventions improved static postural stability, supporting the potential of integrated vibrotactile orthoses in FAI rehabilitation. No major practical issues were reported during the intervention. Two participants experienced minor discomfort related to the electronic housing bulk in the first week, which was resolved by week two. No further complaints regarding device weight or usability were observed. Full article

The grinding process of bearing components is a critical step in their manufacturing, as it directly impacts the functional properties of raceways and other critical surfaces. One important failure that arises during the grinding process is the appearance of waviness in the machined surface. This geometrical defect causes vibrations in operation with a consequent impact on power losses, noise and fatigue. The present work proposes an in-line detection system of waviness defects in bearing raceways. For this, the system uses accelerometers installed near the machined part and runs a detection algorithm in a local calculation unit. The results are sent over Ethernet to the central quality control of the line. The embedded algorithm uses the frequency content of the measured signal for predicting the surface quality of the final part. The prediction is performed by learning a non-parametric model describing the correspondence between the surface geometry and the measured vibration content. In order to obtain this model, a calibration process is conducted for each bearing reference, ensuring that the model accounts for the specific geometric and operational characteristics of the parts. By analyzing the correlation between accelerometer signals and harmonics, the algorithm predicts the probability of waviness occurrence. The proposed system has been implemented in a high-precision bearing production line, validating its effectiveness with multiple parts of the same reference. This approach identifies waviness during the machining process without the need for offline tests. This fact represents an improvement in the detection of defects, and it provides higher product quality and reduced operational costs. Full article

This study investigates the dynamic behaviors of re-entrant unit-cell-shaped steel frames through numerical and experimental modal analyses. Inspired by re-entrant honeycomb structures, individual frame units were modeled to explore how natural frequencies vary with beam cross-sectional dimensions and frame angles. Twenty distinct frame models—incorporating four cross-sectional sizes (4 × 4 mm, 8 × 8 mm, 12 × 12 mm, and 16 × 16 mm) and five main frame angles (120°, 150°, 180°, 210°, and 240°)—were developed using 3D modeling and finite element analysis (FEA) tools, and the first eight natural frequencies and corresponding mode shapes were extracted for each model. The results reveal that lower modes exhibit global bending and torsional behaviors, whereas higher modes demonstrate increasingly localized deformations. It is found that the natural frequencies decrease in the straight frame configuration and increase in the hexagonal configurations, highlighting the critical influence of the frame geometry. Increasing the cross-sectional size consistently enhances the dynamic stiffness, particularly in hexagonal frames. A quadratic polynomial surface regression analysis was performed to model the relationship of the natural frequency with the cross-sectional dimension and frame angle, achieving high predictive accuracy (R² > 0.98). The experimental validation results were in good agreement with the numerical results, with discrepancies generally remaining below 7%. The developed regression model provides an efficient design tool for predicting vibrational behaviors and optimizing frame configurations without extensive simulations; furthermore, experimental modal analyses validated the numerical results, confirming the effectiveness of the model. Overall, this study provides a comprehensive understanding of the dynamic characteristics of re-entrant frame structures and proposes practical design strategies for improving vibrational performance, which is particularly relevant in applications such as machine foundations, vibration isolation systems, and aerospace structures. Full article

Modulating boundary conditions offers a powerful approach to generate and control topological defects, which govern the structure and dynamics of liquid crystals. Here, we employ Langevin dynamics simulations to study defect structure formation in two-dimensional colloidal liquid crystals confined within a square cavity whose walls undergo periodic oscillation. The spatial topology of the driving boundary from single-side to global four-wall actuation directly sets the symmetry of energy input, which in turn determines its spatial gradient and distribution. By controlling boundary vibrations through amplitude and frequency, we demonstrate the emergence of novel steady-state patterns and transformations between distinct defect structures, identified via the local order parameter. Four-wall oscillation generates richer structural diversity due to its higher spatial symmetry. Structural transitions are quantified by tracking a global director angle under two driving regimes: varying amplitude at fixed frequency (f = 2.0), and varying frequency at fixed amplitude (A = 1.0). Our results establish that the manner of energy injection determined by the choice of boundary motion mode governs the emergent defect architectures, providing a general route to engineer non-equilibrium phases under confinement. Full article

In the context of the digital transformation of industrial production, the need for intelligent maintenance and repair systems capable of ensuring reliable operation of machine-tool equipment without operator involvement is growing. This present study reviews the current state and future development of diagnostic and condition-monitoring systems for metalworking machine tools. A review of international standards and existing solutions from domestic and international vendors in vibration diagnostics has been conducted. Particular attention is paid to non-intrusive vibration diagnostics, digital twins, multiparametric analysis methods, and neural network approaches to failure prediction. The architecture of the developed system is presented. The concept of the system is developed in full compliance with Russian and international standards of vibration diagnostics. At its core, the comprehensive digital twin relies on machine learning methods. The proposed architecture is a predictive-maintenance system built on interconnected digital twin realizations: the dynamic machine passport of a unit, operational data, and a comprehensive digital twin of the machine-tool equipment. The potential of neuromorphic computing on a hardware platform is being considered as a promising element for local-condition classification and emergency protection. At the current development stage, the operating principle has been demonstrated along with the integration into the control loop. The system is now at the beginning of laboratory testing. It demonstrates capabilities for comprehensive assessment of the equipment’s technical condition based on multiparametric data, short-term vibration trend forecasting using a Long Short-Term Memory network, and state classification using a Multilayer Perceptron model. The results of the system’s testing on a turning machining center have been analyzed. Full article

Early fault vibration signals of rolling bearings are non-stationary and nonlinear, with weak fault signatures easily masked by noise. Traditional denoising methods (e.g., wavelet thresholding, empirical mode decomposition (EMD)) struggle to accurately extract effective features. Although variational mode decomposition (VMD) overcomes mode mixing, its core parameters rely on empirical selection, making it prone to local optima and limiting its denoising performance. To address this critical issue, this study aims to propose a hybrid model with adaptive parameter optimization and efficient denoising capabilities, enhancing the signal-to-noise ratio (SNR) and feature discriminability of early fault signals in rolling bearings. The novelty of this work is reflected in three aspects: (1) An improved edge strategy whale optimization algorithm (IEWOA) is proposed, incorporating six enhancements to balance global exploration and local exploitation. Using the minimum average envelope entropy as the objective function, the IEWOA achieves adaptive global optimization of VMD parameters. (2) A hybrid framework of “IEWOA-VMD + dataset-specific wavelet thresholding for secondary denoising” is constructed. The optimized VMD first decomposes signals to separate noise and effective components, followed by secondary denoising, ensuring both adaptable signal decomposition and precise denoising. (3) Comprehensive validation is conducted across five models using two public datasets (Case Western Reserve University (CWRU) and Paderborn Universität (PU)). Key findings demonstrate that the proposed method achieves a root-mean-square error (RMSE) as low as 0.00013–0.00041 and a Normalized Cross-Correlation (NCC) of 0.9689–0.9798, significantly outperforming EEMD, traditional VMD, and VMD optimized by single algorithms. The model effectively suppresses noise interference, preserves the fundamental and harmonic components of fault features, and exhibits strong robustness under different loads and fault types. This work provides an efficient and reliable signal preprocessing solution for early fault diagnosis of rolling bearings. Full article

We present a rapid, energy-efficient, and ecofriendly route for the synthesis of alkaline earth titanate perovskites—CaTiO₃, SrTiO₃, and BaTiO₃—using an affordable, commercially available CO₂ laser engraver, commonly found in makerspaces and small-scale workshops. The method involves direct laser irradiation of compacted pellets composed of low-cost, abundant, and non-toxic precursors: TiO₂ and alkaline earth carbonates (CaCO₃, SrCO₃, BaCO₃). CaTiO₃ and BaTiO₃ were synthesized with phase purities exceeding 97%, eliminating the need for conventional high-temperature furnaces or prolonged thermal treatments. X-ray diffraction (XRD) coupled with Rietveld refinement confirmed the formation of orthorhombic CaTiO₃ (Pbnm), cubic SrTiO₃ (Pm3⁻m), and tetragonal BaTiO₃ (P4mm). Raman spectroscopy independently corroborated the perovskite structures, revealing vibrational fingerprints consistent with the expected crystal symmetries and Ti–O bonding environments. All samples contained only small amounts of unreacted anatase TiO₂, while BaTiO₃ exhibited a partially amorphous fraction, attributed to the sluggish crystallization kinetics of the Ba–Ti system and the rapid quenching inherent to laser processing. Transmission electron microscopy (TEM) revealed nanoparticles with average sizes of 50–150 nm, indicative of localized melting followed by ultrafast solidification. This solvent-free, low-energy, and highly accessible approach, enabled by widely available desktop laser systems, demonstrates exceptional simplicity, scalability, and sustainability. It offers a compelling alternative to conventional ceramic processing, with broad potential for the fabrication of functional oxides in applications ranging from electronics to photocatalysis. Full article

Gas-insulated transmission lines (GILs) are increasingly deployed in dense urban power networks, where complex construction activities may introduce external mechanical impacts and pose risks to pipeline structural integrity. However, existing GIL monitoring approaches mainly emphasize electrical and gas-state parameters, while lightweight solutions capable of rapidly detecting and localizing impact-induced structural anomalies remain limited. To address this gap, this paper proposes an intelligent end-edge-cloud monitoring system for impact anomaly detection in GIL pipelines. Numerical simulations are first conducted to analyze the dynamic response characteristics of the pipeline under impacts of varying magnitudes, orientations, and locations, revealing the relationship between impact scenarios and vibration mode evolution. An end-tier multi-parameter intelligent sensor is then developed, integrating triaxial acceleration and angular velocity measurement with embedded lightweight computing. Laboratory impact experiments are performed to acquire sensor data, which are used to train and validate a multi-class extreme gradient boosting (XGBoost) model deployed at the edge tier for accurate impact-location identification. Results show that, even with a single sensor positioned at the pipeline midpoint, fusing acceleration and angular velocity features enables reliable discrimination of impact regions. Finally, a lightweight cloud platform is implemented for visualizing structural responses and environmental parameters with downsampled edge-side data. The proposed system achieves rapid sensor-level anomaly detection, precise edge-level localization, and unified cloud-level monitoring, offering a low-cost and easily deployable solution for GIL structural health assessment. Full article

Accurate detection of structural damage is essential for ensuring the safety and reliability of bridges. However, traditional vibration-based approaches often struggle to capture rich feature representations and adequately model spatial dependencies among sensors. This study proposes a novel bridge damage detection framework, LGSTA-GNN, which integrates local–global spatiotemporal learning with graph neural networks. The framework first extracts multi-scale temporal–frequency features using a multi-scale feature extraction module. A local graph feature extraction module then models intrinsic spatial relationships through graph convolutions, while a global graph attention module adaptively captures inter-sensor dependencies by emphasizing structurally informative nodes. A benchmark dataset generated from a scaled bridge model under progressive damage states is used to evaluate the proposed method. Extensive experiments demonstrate that LGSTA-GNN outperforms multiple graph neural network variants and conventional deep learning techniques, achieving superior accuracy, precision, recall, and F1-score. The confusion matrix and t-SNE visualization further verify its enhanced discriminative capability and robustness. Ablation studies confirm the contribution of each module, highlighting the effectiveness of global attention in identifying subtle structural deterioration. Overall, LGSTA-GNN provides an effective and interpretable solution for intelligent bridge damage detection, with strong potential for practical structural health monitoring and real-time safety assessment. Full article

With the trend toward integrated development in electric vehicles, thermal management components are becoming more compact and highly integrated. This evolution, however, leads to complex spatial layouts of high- and low-temperature fluid circuits, causing localized heat accumulation and unintended heat transfer between channels, which compromises cooling efficiency. Concurrently, these compact components must possess sufficient mechanical strength to withstand operational loads such as vibration. Therefore, designing structures that simultaneously suppress heat transfer and ensure mechanical intensity presents a critical challenge. This study introduces Triply Periodic Minimal Surface (TPMS) and Body-Centered Cubic (BCC) lattice structures as multifunctional solutions to address the undesired heat transfer and mechanical support requirements. Their thermal and mechanical performances are analyzed, and a feedforward neural network model is developed based on CFD simulations to map key structural parameters to thermal and mechanical outputs. A dual-objective optimization approach is then applied to identify optimal structural parameters that balance thermal and mechanical requirements. Validation via CFD confirms that the neural network-based optimization effectively achieves a trade-off between heat transfer suppression and structural strength, providing a reliable design methodology for integrated thermal management systems. Full article

Transition metal (TM)-doped zinc oxide (ZnO) and zirconium dioxide (ZrO₂) nanocrystals exhibit complex correlations between crystal structure, defect chemistry, vibrational properties, and magnetic behavior that are strongly governed by synthesis route and dopant incorporation mechanisms. This review critically summarizes recent progress on Fe-, Mn-, and Co-doped ZnO and ZrO₂ nanocrystals synthesized by wet chemical, hydrothermal, and microwave-assisted hydrothermal methods, with emphasis on synthesis-driven phase evolution and apparent solubility limits. ZnO and ZrO₂ are treated as complementary host lattices: ZnO is a semiconducting, piezoelectric oxide with narrow solubility limits for most 3d dopants, while ZrO₂ is a dielectric, polymorphic oxide in which transition metal doping may stabilize tetragonal or cubic phases. Structural and microstructural studies using X-ray diffraction, electron microscopy, Raman spectroscopy, and Mössbauer spectroscopy demonstrate that at low dopant concentrations, TM ions may be partially incorporated into the host lattice, giving rise to diluted or defect-mediated magnetic behavior. When solubility limits are exceeded, nanoscopic secondary oxide phases emerge, leading to superparamagnetic, ferrimagnetic, or spin-glass-like responses. Magnetic measurements, including DC magnetization and AC susceptibility, reveal a continuous evolution from paramagnetism in lightly doped samples to dynamic magnetic states characteristic of nanoscale magnetic entities. Vibrational spectroscopy highlights phonon confinement, surface optical phonons, and disorder-activated modes that sensitively reflect nanocrystal size, lattice strain, and defect populations, and often correlate with magnetic dynamics. Rather than classifying these materials as diluted magnetic semiconductors, this review adopts a synthesis-driven and correlation-based framework that links dopant incorporation, local structural disorder, vibrational fingerprints, and magnetic response. By emphasizing multi-technique characterization strategies required to distinguish intrinsic from extrinsic magnetic contributions, this review provides practical guidelines for interpreting magnetism in TM-doped oxide nanocrystals and outlines implications for applications in photocatalysis, sensing, biomedicine, and electromagnetic interference (EMI) shielding. Full article