Search Results (3,292)

Industrial hydraulics has made extensive use of the electro-hydraulic actuator (EHA) system. However, prevalent limitations such as uncertainties, non-linearities, and disturbances exist in most mechanical systems and restrict the optimal operation of the EHA system. To address these issues effectively, this study proposes a particle swarm optimization (PSO)-tuned fractional-order proportional-integral-derivative (FOPID) controller. Firstly, the EHA system is modeled using system identification. Then, by modifying the fractional order of the proportional-integral-derivative (PID) algorithm, a reliable controller is established and PSO has been applied to obtain the optimal FOPID controller parameters. Finally, the comparison works are conducted through simulation and experiment between the proposed FOPID and the proportional-derivative (PD) and PID controllers. Based on the error analysis evaluated in the designed sinusoidal (0.35 Hz), point-to-point, and chaotic responses, a significant improvement of 97.7%, 98.4%, and 97.8%, respectively, has been achieved by the FOPID controller as compared with the PD controller and 64.7%, 51.8%, and 47.5%, respectively, has been accomplished in comparison to the experiment’s PID controller. Furthermore, under extreme variation conditions, the proposed FOPID controller produced significant system performance improvement by 84.3% relative to the PD controller and 36.8% relative to the PID controller. Full article

This paper presents a novel compliant XY positioner inspired by the Rana macrodactyla, incorporating a new hybrid displacement amplifier. The positioner was developed to achieve good performance characteristics, including an appropriate resonant frequency, a suitable displacement amplification ratio, effective linear motion, and a compact design enabled by its symmetric structure. The positioner was constructed based on a novel hybrid displacement amplifier that integrated a lever displacement amplifier with a double-rocker mechanism employing circular joints. It also incorporated a parallel guidance mechanism with compliant rectangular joints. In addition, an integration approach of the pseudo-rigid-body method and Lagrange principle, and finite element analysis (FEA) was employed to promptly analyze the static-dynamic attributes of the proposed mechanism. The difference between the theoretical and simulated outcomes showed a 2.2% error, which was minimal and reliable for speedily analyzing the suggested positioner’s output characteristics. Secondly, the developed positioner’s key design variables were optimized using the Firefly optimization algorithm based on analytical equations to improve the output characteristics of the platform. The optimized model of the platform was manufactured using the CNC wire-cut electric discharge machining technique. The experimental and simulated frequencies were 188.4 Hz and 204.6 Hz, respectively, resulting in an 8.6% discrepancy between the two approaches. Furthermore, the experimental validation verifies the suggested positioner’s potential use in nanoindentation systems and validates a linear input–output displacement response. The proposed mechanism demonstrates significant potential for precision positioning in micro-nano engineering systems. Full article

The purpose of this research is to design a multi-source integrated navigation algorithm based on interactive performance monitoring and intelligent decision-making. First, a hierarchical estimation and fusion architecture is developed for self-correction of navigation parameters. On the one hand, the error model of the augmented inertial navigation system is established to calibrate and compensate for systematic errors; on the other hand, the particle swarm optimization mechanism is introduced to dynamically optimize the filtering parameters, which makes the forward state estimation more accurate and maps the health level of each subsystem in real time. Secondly, the estimation error increment is used as the performance index, and an online evaluation unit based on an adaptive neuro-fuzzy inference system is embedded to realize independent and continuous performance monitoring. Finally, the evaluation unit calculated the effective ratio of navigation parameters in real time, injected the adjustment amount into the reverse correction link, and completed autonomous decision-making through process-level correction and terminal output evaluation. Results demonstrate that the method enables real-time performance evaluation and error correction, improving accuracy and reliability. The algorithm can monitor, evaluate and correct the running state of an aerospace vehicles navigation system, thereby alleviating accuracy and reliability degradation during long-duration flights and in complex environments. Full article

Developing reliable numerical tools and analytical methodologies is essential to optimize the design phase of composite structures. The quasi-static fracture behaviour of glass-fibre reinforced polymer bonded joints was evaluated for bidirectional and quasi-isotropic sequences. The specimens were bonded with a two-component epoxy-based adhesive system. The complete mixed-mode I+II fracture envelope was obtained using double cantilever beam, three-point bending End-Notched Flexure, and mixed-mode bending configurations. A data reduction scheme based on the equivalent crack method was adapted to calculate fracture energies as a function of the compliance evolution recorded during the tests. The power law energy fracture criterion was used to describe the complete mixed-mode I+II fracture envelope. The criterion proved to be very accurate in predicting the mixed-mode fracture behaviour for both series. A cohesive zone model with a trapezoidal softening law was used to validate the experimental procedure of the three test configurations. The results obtained for the fracture envelope using the power law criterion and the local strengths were used as input for the numerical simulations. The model efficiently described the full fracture envelope of both stacking sequences, with a deviations below 3% for the B series and below 7.5% for the Q series. Full article

In the intelligent operation and maintenance of core process equipment in semiconductor manufacturing, the thickness and morphological parameters of sub-micron multilayer transparent films on wafer surfaces are critical quality indicators that govern device performance and yield. White Light Interferometry (WLI) enables the high-precision measurement of thin-film thickness and topography parameters, and is widely deployed in high-precision manufacturing fields such as semiconductors. However, when measuring sub-micron multilayer transparent films, WLI faces challenges including low computational efficiency, severe parameter coupling, and non-unique solutions, making it difficult to meet the demands of high-throughput online inspection. To address these issues, this paper proposes a deep learning-based method for decoupling and optimizing WLI signals through inverse modeling. The approach establishes an innovative hybrid intelligent framework: first, a training dataset is generated based on interferometric system modeling and simulation; then, a classification model is employed to intelligently categorize the acquired interference signals, decomposing the complex multimodal inversion problem into several sub-problems with simpler patterns. Next, for each signal category, a specialized closed-loop deep network model is designed and trained. This network integrates an inverse prediction network with a forward reconstruction network in series. During training, both prediction error and reconstruction error are jointly used as the loss function, ensuring solution uniqueness and physical consistency, thereby enhancing inversion reliability and robustness. This research provides an effective solution for high-precision online optical measurement of complex thin-film structures, offering significant theoretical value and broad industrial application prospects. Full article

The long-term reliability of selective laser melting (SLM) equipment is constrained by the latent degradation and coupled faults of multiple underlying hardware systems under continuous high-load operations. Transitioning from passive defect detection to proactive prognostics and health management (PHM) is crucial to overcome bottlenecks in industrial applications. Accurately inferring future multi-system hardware states faces three major challenges: feature alignment difficulties under variable-length printing cycles, graph topology collapse under strong industrial noise, and dimensional conflicts along with error cascading divergence during the joint optimization of microscopic physical trajectories (continuous regression) and macroscopic system anomalies (discrete classification) in end-to-end prediction. To address these issues, this paper proposes a decoupled spatiotemporal graph autoregressive network (DS-GAN). First, a multi-scale feature pooling and degradation gating injection mechanism is constructed to align highly variable-length high-frequency sequences and adaptively integrate macroscopic health priors. This approach achieves feature decoupling under physical boundary constraints. Second, a restricted residual graph evolution mechanism is introduced to regulate dynamic coupling drift based on static physical topologies, effectively suppressing feature divergence in the spatial dimension. Finally, a heterogeneous multi-task autoregressive decoder based on homoscedastic uncertainty is designed; this decoder helps reduce the impact of accumulated errors along the time axis during multi-step forecasting. Long-sequence forward inference on a real SLM continuous printing dataset demonstrates that DS-GAN achieves an overall accuracy of 97.48% for multi-system anomalies while strictly limiting the global false alarm rate to 1.85%. Furthermore, quantitative results reveal the physical inertia mechanism within the prediction horizon, demonstrating that the model maintains high fidelity for physical trajectories with a Macro-RMSE of 0.062, even under a maximum predictive horizon of 20 s. This study provides a reliable theoretical and engineering framework for dynamic coupling correlation analysis and proactive fault warning of multi-systems in complex industrial equipment. Full article

Solder joints in aerospace, nuclear, and defense electronics are increasingly exposed to ionizing radiation, which creates serious reliability issues. Despite its importance, a comprehensive understanding of the underlying mechanisms and how they vary across radiation types remains lacking. This systematic literature review synthesizes 24 peer-reviewed studies (2000–2025) to evaluate radiation-induced microstructural evolution and mechanical degradation in Sn-based solder alloys. Following PRISMA guidelines, data were analyzed across gamma, electron, proton, neutron, and alpha irradiation, covering Sn–Pb, SAC305, AuSn20, SnBi, and related systems. Gamma irradiation predominantly accelerates interfacial diffusion kinetics, resulting in IMC thickening, oxidation, and shear strength reductions. However, the effects are dose-dependent, where low doses temporarily harden the material, while higher doses make it brittle. Lead-free SAC305 alloys exhibit greater radiation sensitivity than Sn–Pb systems, showing accelerated IMC growth, defect accumulation, and lattice instability. Proton and neutron irradiation induce displacement-dominated damage, including defect clustering and Kirkendall voiding. Combination of electron irradiation and thermal cycling produces synergistic degradation exceeding single-stressor effects. By integrating experimental findings with atomistic and multiscale modeling insights, this review links defect generation, interfacial instability, and mechanical degradation using established radiation-enhanced diffusion (RED) principles. The results clarify radiation-type-dependent damage mechanisms, highlight alloy-specific tolerance differences, and identify critical gaps in multi-stressor testing and predictive lifetime modeling. The mechanistic insights presented here serve as a foundation for optimizing solder alloy design and strengthening qualification procedures for radiation-prone electronic applications. Full article

Machine vision and machine learning are playing an increasingly important role in manufacturing and autonomous vehicle applications. Reliable operation of such systems strongly depends on proper sensor calibration. One possible calibration method is to move a calibration target using a robotic arm; however, commercially available robotic manipulators are often too expensive for educational and research purposes. The aim of this research is therefore to develop a low-cost robotic arm for teaching and research applications. The paper presents the workflow of a systematic robotic design process, including conceptual design, functional decomposition, CAD modeling, simulation, optimization possibilities, and manufacturing aspects. Preliminary results of the CAD design and simulation process are also discussed. The reaction torques in the simulation were calculated as 0 Nm at Joint 1, −8.52 Nm at Joint 2, −1.37 Nm at Joint 3, 0 Nm at Joint 4, and 2.64 Nm at Joint 5 using a 0.5 kg load and the weight of the arms. Different drive system concepts are evaluated and compared in tabular form according to the principles of systematic design. The paper concludes by identifying the main limitations of the current design and outlining further research tasks. Full article

The rapid growth of large language models (LLMs) has made training energy efficiency a major systems and sustainability challenge. Training requires substantial computation, memory, communication, and electrical energy, yet the literature often treats runtime, floating-point operations, memory, communication, cost, energy, and carbon as interchangeable indicators of efficiency. This survey critically reviews 54 research papers and one supporting software tool across measurement and carbon accounting, model and numerical efficiency, memory and communication, distributed planning, GPU power control, carbon-aware scheduling, lifecycle design, and reliability. We classify evidence as direct, modeled, simulated, reported-comparison, or enabling, and interpret results only within their stated workload, hardware, quality condition, and measurement boundary. The synthesis shows that individual mechanisms are mature, yet cross-paper comparison remains difficult because studies use inconsistent boundaries, quality targets, platforms, and accounting methods. We, therefore, propose a quality-aware reporting framework centered on energy to target quality, explicit boundaries, uncertainty, and reproducibility. The survey establishes an evidence-disciplined framework for evaluating energy-efficient LLM training and identifies integrated, quality-aware, whole-system energy optimization as the principal research direction. Full article

3D Concrete Printing (3DCP) represents a pivotal advancement for establishing sustainable extraterrestrial infrastructure. This review synthesizes recent progress in lunar regolith-based composites, analyzing the interplay between material formulation, processing parameters, and numerical simulation. While challenges such as extreme thermal cycling, vacuum conditions, and resource scarcity necessitate novel material designs, significant strides have been made in optimizing rheology and deposition fidelity. Crucially, the integration of multi-scale modeling, including Discrete Element Method (DEM), Finite Element Method (FEM), and Computational Fluid Dynamics (CFD), has proven essential for predicting structural performance. Looking ahead, the future of lunar construction depends on bridging current gaps through the development of robust, self-sensing, and autonomous printing systems. Realizing durable habitats requires a strategic shift toward interdisciplinary convergence, combining materials science with robotics to create scalable solutions capable of withstanding the harsh lunar environment. This work outlines the pathway toward reliable, formwork-free additive manufacturing essential for future planetary exploration. Full article

This paper addresses the performance degradation caused by delayed channel state information (CSI) feedback in vehicular networks by introducing full-duplex (FD) technology into vehicle-to-vehicle (V2V) communications. We jointly optimize power control and spectrum allocation, designing an FD-V2V interference management algorithm aimed at maximizing the ergodic sum rate and the minimum ergodic rate of vehicle-to-infrastructure (V2I) links, respectively. Furthermore, an adaptive full-duplex V2V (AFD-V2V) interference management algorithm is proposed to dynamically select the optimal duplex mode. The proposed algorithm maximizes system performance while guaranteeing V2V link reliability by integrating large-scale fading information, statistical properties of small-scale fading, and delayed CSI of small-scale fading from non-directly connected base station links. The simulation results demonstrate that the AFD-V2V scheme significantly outperforms traditional half-duplex V2V (HD-V2V) in both the ergodic sum rate and the minimum ergodic rate. Full article

The preventive conservation of ground-level archaeological sites requires not only protection from direct weathering but also effective control of the surrounding microclimate. Conventional archaeological coverings generally fail to provide adequate environmental stability while generating significant physical, visual, and environmental impacts. This study presents an integrated methodology for the design, optimization, and validation of a lightweight double-layer covering conceived as a sustainable environmental protection system rather than a passive protective shelter. The proposed workflow integrates parametric optimization of geometry and materiality, Life Cycle Assessment (LCA), environmental monitoring, Computational Fluid Dynamics (CFD) simulations, and experimental validation. The methodology was applied to the 1st c. CE Roman Tomb of the Two Families, located within the Carmona Archaeological Complex (Spain), where the environmental performance of the covering was evaluated through more than one year of in situ monitoring and numerical simulations. The results demonstrate that the proposed solution effectively moderates the site’s microclimate while significantly reducing environmental impacts and material consumption compared with conventional protective structures. The close agreement between monitored and simulated data validates the proposed design methodology and confirms the reliability of the environmental models. This research demonstrates that lightweight archaeological coverings can be conceived as a sustainable environmental protection infrastructure, providing a potentially transferable methodological framework for preventive conservation that integrates structural efficiency, environmental control and sustainability from the earliest stages of the design process. Full article

Sensor fault diagnosis for direct-drive electric vehicle in-wheel motor systems faces significant challenges under complex operating conditions. Specifically, signal disturbances such as noise interference, model uncertainty, and environmental factors often cause residual distortion, masking fault features and increasing false alarm rates. To address these challenges, a fault diagnosis method based on data-driven subspace identification and residual anti-interference filtering is proposed. Firstly, a state space model of the permanent magnet brushless DC motor is established, and a subspace identification algorithm is combined to construct a discrete system model considering disturbances. An adaptive residual filter is designed to suppress the influence of disturbances on residual signals, and predictive time-domain rolling optimization of residual estimation is introduced. Furthermore, the maximum likelihood ratio is used to construct adaptive thresholds to enhance fault sensitivity and robustness. The bench and real vehicle experiments show that this method can effectively extract fault features under noise and uncertainty interference, significantly improve the fault localization accuracy of current and speed sensors, reduce false alarm rates, and provide technical support for the reliable operation of direct-drive motor systems. Full article

As a core surface facility in natural gas development, the operational efficiency of a gas gathering and transportation system depends largely on the precise configuration of its parameters. However, the complex structure of such a system and the large number of components make real-time data acquisition and accurate monitoring difficult under all operating conditions, so numerical simulation has become the standard way to represent its behavior. At present, the input parameters of professional simulation software still rely heavily on manual empirical settings, and parameter accuracy is therefore difficult to guarantee. If the manually set parameters deviate significantly from the actual operating conditions, the reliability of the simulation results is seriously compromised and subsequent decisions may be misleading. To address this problem, the present work uses Pipesim as the simulation platform and formulates parameter calibration as a weighted least-squares optimization problem. For a network with a fixed topology, the key component parameters are calibrated jointly. The objective is to minimize the deviation between the simulated and the field-measured values of the selected operating variables; pressure and flow rate are used in the case study, while the temperature term is retained in the general formulation. The Pipesim solver is proprietary and does not expose analytical derivatives of its outputs with respect to the calibrated parameters, so the model is solved with particle swarm optimization (PSO) combined with a continuation-projection encoding that handles continuous, discrete-gear and Boolean parameters. On the network examined here, the proposed procedure reduces the calibration effort and outperforms manual tuning in both accuracy and time. It therefore provides a practical reference for the design and operation management of gas gathering and transportation systems. Full article

Seeding constitutes a key crop-production operation that governs seed spatial arrangement, crop population structure, and potential yield formation, and forms the foundation of precise, efficient, and eco-friendly farming. However, field soil properties, regional climate, and crop agronomic requirements exhibit strong spatio-temporal heterogeneity. Conventional seeding operations based on manual experience and fixed preset parameters cannot meet the demands of large-scale precision agriculture. Enabled by progress in precision agriculture, intelligent sensing, artificial intelligence, and autonomous machinery, modern intelligent seeding systems integrate precision seed metering, high-precision environmental perception, and closed-loop dynamic self-regulation. Such systems can improve plant-spacing uniformity and enable precise seeding-depth control under standard open-field conditions, yet face noticeable performance limitations in GNSS-denied complex environments including dense crop canopies and greenhouses. This review outlines the evolutionary trajectory of seeding machinery and summarizes research progress regarding precision seeding, multi-functional equipment integration, multi-source information perception, and intelligent decision-making. Integrated design principles covering mechanical optimization, electronic control, and perception-driven decision systems are elaborated. Four developmental phases of seeding equipment are identified: mechanical precision operation, electronic intelligent regulation, multi-functional module integration, and intelligent cognitive integration. Current intelligent seeding technologies are constrained by limited adaptability to complex farmland conditions, unstable multi-source data fusion, insufficient long-term operational reliability, and high deployment costs across diverse scenarios, restricting their broad field-scale adoption. Future research should combine agronomic knowledge with artificial intelligence to improve environmental awareness and autonomous decision-making capability, develop low-cost, high-reliability integrated seeding equipment, and support the construction of intelligent agricultural machinery systems. Full article