This study presents the conceptual design and numerical dynamic analysis of a bowl-type vibratory finishing machine through CAD development and simulation. The methodology integrates a literature review of process and design variables, definition of operating requirements, selection of the abrasive system, mechanical sizing of critical components, and formulation of a numerical model for modal and transient dynamic analysis. The proposed machine was configured as a bowl-type system with eccentric-mass excitation, spring suspension, and a compact support structure. The specific contribution of this work lies in integrating, within a single design-oriented framework, the definition of the main machine variables, the mechanical sizing of critical components, and the modal and transient dynamic analysis of a proposed architecture prior to prototyping. The modal assessment retained ten modes, with the closest relevant frequencies remaining separated from the nominal 25 Hz excitation within the adopted screening framework. For the refined transient case, synchronized X(t) and Y(t) histories permit direct reconstruction of the bowl-edge X–Y trajectory. Over the selected late-time interval of 0.6–1.0 s, the directional half-range displacements were 1.119 mm in X and 1.095 mm in Y, while least-squares harmonic amplitudes at 25 Hz were 1.047 and 1.046 mm, respectively. These values provide directional structural-response metrics for numerical screening and a reference for subsequent experimental radial-amplitude comparison. A targeted spring-stress sensitivity study yielded local von Mises maxima of 504.84, 2111.2, and 2978.2 MPa across progressively refined global and local adaptive configurations; the large changes in magnitude and hotspot location indicate pronounced sensitivity of the spring-end peak to local adaptive refinement. Accordingly, this quantity is treated as a model-dependent local hotspot. The abrasive-loaded configuration is used as a first-order structural-inertial representation of operating mass for machine-level screening.
This study compares three classical classifiers—support vector machine (SVM), random forest (RF) and k-nearest neighbors (kNN)—for bevel gear fault diagnosis based on maximal overlap discrete wavelet transform (MODWT) features. Tri-axial vibration signals were acquired for four pinion conditions (healthy, chipped tooth, broken tooth and worn gear) at four speeds and four loads, giving 64 recordings. The sym8 wavelet was selected objectively by combining the maximum energy to Shannon entropy ratio with a correlation criterion. After wavelet-domain thresholding, 75 multi-domain features were reduced to 12 by Fisher score ranking and correlation pruning, repeated inside every validation fold. Under leave-one-out cross-validation, the radial basis function SVM reached 98.4% accuracy (95% confidence interval, 91.7–99.7%), against 92.2% for kNN and 90.6% for RF, but these differences are not statistically significant (exact McNemar test, p = 0.125). When an entire speed was withheld from training, the ranking reversed: RF reached 93.8%, against 81.3% for kNN and 76.6% for SVM, and the RF–SVM difference was significant (Holm-adjusted p = 0.003). Withholding a load level affected the three classifiers similarly (85.9–89.1%). The most suitable classifier therefore depends on whether the training data cover the operating envelope of the monitored machine.
During cycling, riders are exposed to vibration, but literature lacks a synthesis of its effects. This work aims to systematically review existing research on the effects of exposure to vibration during cycling. PubMed and Scopus were consulted on 29 August 2025 using keywords related to vibration and cycling. Of the 4141 records identified, 32 met the inclusion criteria (bias risk: low in 25 studies, moderate in 6, high in 1). The results were categorized according to the investigated outcomes: transmissibility (6 studies), transmitted acceleration (12), metabolic performance (16), neuromuscular activity (8), biomechanics (1), comfort (3), and vibration perception (2). Rider position, cycling speed, and crank cycle phase influence the transmission of vibration. Exposure limits defined by European Directive 2002/44/EC were exceeded, even under non-demanding cycling conditions. Vibration increases muscle activation and may impair cycling performance. Overall, bicycle design should minimize vibration exposure to enhance comfort, performance, and safety.
This study presents a Digital Twin-based Structural Health Monitoring framework for the assessment of existing concrete gravity dams. The proposed methodology integrates distributed sensing, automated signal processing, finite element modelling, model calibration and validation, degradation sensitivity analysis, and digital visualization within a unified monitoring framework. A network of inertial sensors is deployed along the dam to characterize its dynamic and quasi-static response. Hourly monitoring records are processed to identify natural frequencies using Fast Fourier Transform, Power Spectral Density, and Peak Picking techniques, while acceleration measurements are also used to derive displacement and inclination indicators. A 3D finite element model developed in ANSYS Mechanical APDL (2026 R1) is calibrated by iteratively updating the effective Young’s modulus and density until the relative error between numerical and experimentally identified modal frequencies is below 5%. Independent validation using inclination measurements resulted in relative differences between 0.74% and 4.92%. The calibrated Digital Twin is subsequently used to simulate progressive stiffness degradation and establish relationships between degradation, modal properties, and structural deformation. The resulting model-informed thresholds are integrated into a digital monitoring platform for automated condition assessment and anomaly detection. The framework provides a physically interpretable approach for complementing conventional dam safety inspections with long-term quantitative structural health information.
The vibration damping behavior of monolayer and bilayer graphene is investigated through molecular dynamics simulations, with particular emphasis on the effects of excitation frequency and interlayer interactions on mechanical energy dissipation. Graphene nanoplates with in-plane dimensions of approximately 200 Å × 200 Å are considered, with the bilayer system arranged in an AB-stacking configuration. Atomic interactions are described using a reactive force field (ReaxFF), enabling the dynamic response and associated energy dissipation to emerge directly from the underlying atomistic interactions without introducing phenomenological damping parameters. A localized harmonic out-of-plane displacement with an amplitude of 0.4 nm is applied to the central region of the graphene nanoplate at excitation frequencies of 0.001, 0.01, 0.1, and 1 THz. Energy dissipation is quantified directly from the area enclosed by the resulting reaction force–displacement hysteresis loops, providing an energy-based characterization of the equivalent damping response. The results reveal a strongly frequency-dependent damping response and a non-monotonic effect of the second graphene layer. At 1 THz, the equivalent damping ratio reaches approximately 47.4% for monolayer graphene and 24.0% for bilayer graphene, despite comparable absolute cycle-energy values, demonstrating that the normalized damping response is strongly influenced by the dynamic reference energy. Overall, the results show that interlayer coupling can substantially modify the frequency-dependent vibration and energy-dissipation response of graphene rather than producing a uniform increase in damping.
