机械工程, 本领域期刊类期刊, -365足球

the finite element model (fem) plays an important role in model-driven response reconstruction of mechanical structure. unfortunately, modelling difficulty of certain complex mechanical substructures could lead to the unavailability of precise modelling, thus impairing the reliability of the full fem. this paper presents a measurement-based adaptive framework for substructure identification to address

monitoring rotating blade vibration is critical for turbomachinery safety, and blade tip timing (btt) is one of the key non-contact measurement techniques. conventional btt methods, however, face significant limitations in accurately identifying complex multi-frequency vibrations. these methods frequently depend on sparse time-of-arrival (toa) data, resulting in undersampling of the vibration signal

the identification and analysis of nonlinear dynamics in structural systems remain critical challenges in engineering, particularly when subtle vibrational phenomena are involved. phase-based motion magnification (pbmm) has emerged as a powerful non-contact technique for visualizing fine-scale motions in complex systems, yet its application to nonlinear structural behavior remains underexplored. this

bearing system is critical components in rotating machinery, whose health directly impacts operational safety. to address faults arising from bearing wear—and to overcome the limitations of conventional deep-learning methods that struggle to exploit both time- and frequency-domain information simultaneously—we propose fd-mvllm, a novel reprogramming framework that combines a large language model (llm)

for various semi-active rotor dry friction dampers (sardfd), how to effectively suppress rotor unbalance responses with minimum normal forces can be formulated as an optimal control problem. classical optimal controllers were mostly developed in the time domain, which makes them unsuitable for the control of sardfds due to the high rotational speeds of rotor systems. in this paper, an offline deep

collaborative robotics is increasingly considered in manufacturing to improve efficiency while reducing operators physical and cognitive workloads. however, the lack of comprehensive methodologies has limited the consistent implementation of human–robot collaborative workstations across industries. existing approaches are often fragmented, require robotics expertise, and pose challenges for non-experts

large unmanned ocean platforms have extensive applications in military reconnaissance, maritime rescue, and resource exploration. the electromechanical system, as the core component of these platforms, faces numerous challenges due to harsh sea conditions, spatial constraints, and multiple monitoring points. these challenges include frequent component failures, complex multi-source data coupling, unpredictable

for degradation prediction of high-reliability equipment, accelerated degradation test data is often used to extrapolate the degradation state under normal stress levels. previous studies typically model degradation using memoryless markovian processes. however, due to the inherent degradation mechanisms and external stress influences, the degradation process often exhibits varying levels of memory

quadrotor transportation system face underactuation challenges compounded by double-pendulum dynamics and unknown payload masses. to tackle these issues, this paper develops a novel energy-coupling-based adaptive controller to address quadrotor transportation system with double-pendulum swing effects and unknown payload mass. first, a generalized payload signal is introduced to strengthen state coupling

resonance demodulation has been extensively employed for fault diagnosis in rolling element bearings due to its effectiveness in extracting fault features. achieving high diagnostic accuracy largely depends on a precise identification of the optimal resonant frequency band for demodulation. however, accuracy and robustness of existing methods still require further improvement. to address this issue

two battery pack casings with pack total energy of 12.432 kwh are 3d-printed using aluminum alloy (alsi10mg) and carbon fiber-reinforced composite filament based on polyamide 6, 12 (pa12 cf ). the vibration and shock attenuation capabilities of the three mechanical metamaterials (auxetic, lattice and x-shaped structures) applied to battery pack casing’s walls are studied. lattice structure shows better

we present a data-driven approach for modeling complex components in mechatronic systems using a physics-inspired neural network structure. this structure reflects the principles of classical first-principle modeling, particularly the separation of time and state-space variables. gated recurrent units (grus) approximate the system’s ordinary differential equations (odes), while a deep feedforward network

road roughness plays a significant role in vehicle vibrations, which directly affect passenger ride comfort. however, many existing estimation methods for road roughness rely on simplified linear suspension models and assume that suspension parameters remain constant, which significantly restricts their practical applicability. to overcome these limitations, this paper introduces a novel augmented

the advancement of industry 5.0 fosters human-centric manufacturing, aiming to enhance the well-being and needs of operators. current research on human-robot collaboration (hrc) in the assembly scene is progressively evolving toward intelligent manufacturing with a human-centric focus, particularly in safety and interaction. however, in existing hrc assembly environments, the disjointed relationship

the elastic ring squeeze film damper (ersfd) is a compact damping structure designed to minimize rotor vibrations in high-speed rotating machinery. comprising bulge contact interfaces and oil-filled chambers, the ersfd exhibits complex dynamic behavior, making it difficult to model and analyze due to the interaction and mutual dependencies between contact nonlinearity and the constantly varying oil

particle filter (pf) integrated with structural health monitoring (shm) has received increasing attention for evaluating structural remaining useful life, due to its advantages of fusing online shm observation to correct results and parameters of the physical damage evolution model. to apply this technique for the situation under complex service conditions, a sophisticated damage evolution model is

the reasonable design of chaotic oscillators is related to the performance of underwater signal detection. this study proposes a new grid-scroll memristive chaotic oscillator with infinite equilibrium points, and applies it to underwater signal detection. first, based on a cubic nonlinear memristor and piecewise linear functions, a novel grid-scroll memristive chaotic oscillator is designed. the oscillator

real-time monitoring of porosity defect is crucial for quality assurance during aluminum (al) alloys laser welding. optical coherence tomography (oct) offers precise keyhole depth measurement and can be utilized for porosity monitoring. this paper proposes a laser welding porosity defect monitoring method using oct. a laser welding monitoring platform based on oct is developed to measure the keyhole

train collisions can cause serious casualties and property damage, and collision testing is a key means of evaluating the crashworthiness of trains. however, due to the complexity of the test environment, it is extremely challenging to directly measure the collision force. to this end, this paper proposes a collision load identification method that combines deep learning and optimization iteration

fault detection for wind turbine gearboxes is the pivot to ensure the sustainable and stable development of wind power. the planetary ring gear is the core component in this type of equipment. however, the challenge of ring gear fault detection is the coupling of pass-through effect and failure impulses with the same period in the time and frequency domains. traditional diagnostic methods lack the

leading researchers debate the long-term influence of model-free methods that use large sets of demonstration data to train numerical generative models to control robots.

dynamic locomotion of legged robots is a critical yet challenging topic in expanding the operational range of mobile robots. it requires precise planning when possible footholds are sparse, robustness against uncertainties and disturbances, and generalizability across diverse terrains. although traditional model-based controllers excel at planning on complex terrains, they struggle with real-world

high-resolution electronic tactile displays stand to transform haptics for remote machine operation, virtual reality, and digital information access for people who are blind or visually impaired. yet, increasing the resolution of these displays requires increasing the number of individually addressable actuators while simultaneously reducing their total surface area, power consumption, and weight,

impedance-engineered passive metasurfaces represent an emerging paradigm that enables precise modulation of complex near-field energy flow between neighboring unit cells, facilitating extraordinary acoustic functionalities. however, the majority of existing works are only limited to single-function operation at a fixed frequency, unable to meet the growing demand for multifunctional applications in

deep anomaly detection (ad) models have shown powerful capacity in exploring abnormal patterns of industrial equipment. however, traditional static learning paradigms confine their application scenarios to independent and identically distributed data, unable to handle nonstationary data streams under changing working conditions. continual learning (cl) provides a solution for dynamic environments,

as one of the key components of rotating machinery, the health status of rolling-element bearings directly determines whether the equipment can function properly. due to the harsh working environment and the weak early degradation trends of rolling-element bearings, the extraction of degradation features and the accuracy of prediction are significantly challenged. to address this issue, a cumulative

in this paper, a novel method for integrating equality constraints and inequality constraints is proposed for dynamic modeling of mechanical systems. the equality constraints are used to delineate the ideal trajectory of the actuator, while inequality constraints are applied to artificially set the error boundaries of the system states with uncertainty. then, a robust control including two portions

this paper introduces a new linear cutting force model that targets explicitly battery-powered jigsaws, as existing models primarily focus on industrial-scale or cnc-controlled systems. the proposed method uses a basic yet realistic approach to model dynamic force behavior under reciprocating motion instead of previous nonlinear or empirical methods. the model combines the jigsaw’s scotch yoke mechanism

a statistical time series method for robust detection of incipient faults in rotating machinery under different operating conditions (ocs) is postulated. the method is of the unsupervised type in the sense that there is no need for measurements from the faulty machinery or any information about the fault characteristics for its training. instead, it is trained using vibration signals from a single

this paper presents the results of microscopic, nanotomographic and strength studies of metamaterial with gyroid structure with varying porosity of cocrwmo alloy obtained by laser powder bed fusion method. on the basis of the study, the effective strength parameters were determined depending on the degree of density of the structure. the developed dependence of the change in the effective value of

underwater vehicles can achieve effective drag reduction by utilizing air layers retained within superhydrophobic microstructures. however, maintaining the stability of the gas-liquid interface under shear forces or high pressure remains a significant challenge. inspired by salvinia, a bioinspired structured surface (bss) with multilayered structures was proposed for underwater drag reduction, featuring

multiscale modeling of pore collapse and hotspot formation is essential for understanding shock sensitivity and initiation in high explosives, yet the mechanisms governing size-dependent behavior across scales remain insufficiently elucidated. to this end, an atomistically informed dislocation plasticity model incorporating size effect is developed for cyclotetramethylene tetranitramine (hmx), which

this study introduces a network graph entropy-based constitutive model for soft materials with polymer network structures, grounded in the concept that the quasi-static stretching of a random walk polymer chain consisting of monomers leads to the formation of sequentially connected tension blobs through tension propagation. however, this transformation is conceptualized as a reversible assembling process

the pantograph-catenary system’s dynamic train-grid coupling critically determines traction current transmission quality in electrified railway development. with high-speed railway operational targets reaching 400 km/h, experimental constraints necessitate complete reliance on computationally intensive numerical simulations for dynamic performance evaluation while existing intelligent contact force

this paper proposes a novel approach for the diagnosis of bearing faults in the presence of coexisting electrical anomalies. a simplified dq-model is developed to simulate localized spalling on the outer race, with torque disturbances explicitly incorporated to represent the mechanical defect. unlike finite element and magnetic models, which are often too computationally intensive for real-time industrial

most civil and mechanical structures exhibit nonlinear stochastic behaviour, which is difficult to model accurately, but necessary for conventional model-based structural health monitoring techniques. although various methods have been developed to estimate nonlinear systems, they require information about the external excitation and are susceptible to sensor noise and modelling inaccuracies. this

sparse system identification of nonlinear dynamic systems is still challenging, especially for stiff and high-order differential equations for noisy measurement data. the use of highly correlated functions makes distinguishing between true and false functions difficult, which limits the choice of functions. in this study, an equation discovery method has been proposed to tackle these problems. the

parameterized random fields play an important role to simulate complex uncertain physical models. in this kind of random fields, statistical properties of the classical random fields, e.g. marginal distributions and covariance functions, may be also related to (random) parameters. in-depth analysis and efficient simulations of parameterized random fields still require further research. this paper presents

titanium alloy slender shafts have wide application on the aerospace, automotive and medical devices. however, they are prone to bending deformation during cutting process due to their low rigidity, resulting in poor machining accuracy and efficiency. a novel parallel ultrasonic vibration-assisted cutting (puvc) system is proposed using two tools to simultaneously cut the workpiece and applying tangential

journal bearings are increasingly being used to support planetary gears in wind turbine gearboxes (wtg) due to their high load capacity and compact radial dimensions. however, the intermediate oil return groove (iorg) structure of planetary gear journal bearings (pgjb) increases the risk of contact and further complicates the coupling effects between elastohydrodynamic (ehd) lubrication and gear meshing

in hexagonal close-packed (hcp) metals, basal-prismatic (bp) boundaries initially accommodate plastic deformation but later induce strain/stress concentration upon coherency loss. although their migration alleviates localization, the underlying atomic-scale mechanisms remain elusive. this study unveils the atomic-scale mechanisms governing incoherent bp boundary migration in ti-al alloys through molecular

studying bridge overloading warnings is crucial for structural integrity and safeguarding lives and property. however, current methods often fail to provide early alerts for overloaded vehicles. this paper introduces an innovative methodology for early bridge overloading warning utilizing a combination of mechanical, data and physical models. in this study, a bridge located upstream of a road (p-bridge)

ultrasonic welding is a pivotal technique for joining carbon fiber reinforced thermoplastic polymers (cfrtp), with broad applications across various industries. despite its importance, current methods for predicting the quality of ultrasonic welding joints are complex, and there is a pressing need for accurate, non-destructive, and cost-effective methods to detect damage during service. this study

delaminations in composite laminates pose a critical challenge for civil aircraft structural health monitoring (shm) due to their hidden progression and severe mechanical degradation effects. this study develops a novel deep learning framework frcnet for automatic delamination imaging through wavefield analysis. the frcnet adopts a configuration similar to fully convolutional network, while innovatively

this study presents a novel method for in situ monitoring of the condition of elastohydrodynamically (ehl) lubricated contacts, as occurring in rolling bearings and gears, using a capacitance-based evaluation of the electrical charging behavior. a nonlinear regression model is applied to segmented portions of the voltage response curve following a defined voltage pulse. this enables accurate extraction