Abstract: The complexity of modern cyber-physical systems is steadily increasing as their functional scope expands and as regulations become more demanding. To cope with this complexity, organizations are adopting methodologies such as Model-based Systems Engineering (MBSE). By creating system models MBSE promises significant advantages such as improved traceability, consistency, and collaboration. On the other hand, the adoption of MBSE faces challenges in both the introduction and the operational use. In the introduction phase, challenges include high initial effort and steep learning curves. In the operational use phase, challenges arise from the difficulty of retrieving and reusing information stored in system models. Research on the support of MBSE through Artificial Intelligence (AI), especially Generative AI, has so far focused mainly on easing the introduction phase, for example by using Large Language Models (LLM) to assist in creating system models. However, Generative AI could also support the operational use phase by helping stakeholders access the information embedded in existing system models. This study introduces an LLM-based multi-agent system that applies a Graph-Retrieval-Augmented-Generation (GraphRAG) strategy to access and utilize information stored in MBSE system models. The system’s capabilities are demonstrated through a chatbot that answers questions about the underlying system model. This solution reduces the complexity and effort involved in retrieving system model information and improves accessibility for stakeholders who lack advanced knowledge in MBSE methodologies. The chatbot was evaluated using the architecture of a battery electric vehicle as a reference model and a set of 100 curated questions and answers. When tested across four large language models, the best-performing model achieved an accuracy of 93 percent in providing correct answers.

Abstract: Self-driving laboratories are redefining autonomous experimentation by integrating robotic manipulation, computer vision, and intelligent planning to accelerate scientific discovery. This work presents a vision-guided motion planning framework for robotic manipulators operating in dynamic laboratory environments, with a focus on evaluating motion smoothness and control stability. The framework enables autonomous detection, tracking, and interaction with textured objects through a hybrid scheme that couples advanced motion planning algorithms with real-time visual feedback. Kinematic modeling of the manipulator is carried out using the screw theory formulations, which provides a rigorous foundation for deriving forward kinematics and the space Jacobian. These formulations are further employed to compute inverse kinematic solutions via the Damped Least Squares (DLS) method, ensuring stable and continuous joint trajectories even in the presence of redundancy and singularities. Motion trajectories toward target objects are generated using the RRT* algorithm, offering optimal path planning under dynamic constraints. Object pose estimation is achieved through a vision pipeline that integrates feature-based detection with homography-driven depth analysis, enabling adaptive tracking and dynamic grasping of textured objects. The manipulator’s performance is quantitatively evaluated using smoothness metrics, RMSE pose errors, and joint motion profiles including velocity continuity, acceleration, jerk, and snap. Simulation studies demonstrate the robustness and adaptability of the proposed framework in autonomous experimentation workflows, highlighting its potential to enhance precision, scalability, and efficiency in next-generation self-driving laboratories.

Abstract: Synthetic jets, generated through the periodic suction and ejection of fluid without net mass addition, offer distinct benefits, such as compactness, ease of integration, and independence from external fluid sources. These characteristics make them well-suited for flow control and convective heat transfer applications. However, conventional sin-gle-actuator configurations are constrained by limited jet formation, narrow surface coverage, and diminished effectiveness in the far field. This review critically evaluates the key limitations and explores four advanced configurations developed to mitigate them: dual-cavity synthetic jets, single-actuator multi-orifice jets, coaxial synthetic jets, and synthetic jet arrays. Dual-cavity synthetic jets enhance volume flow rate and surface coverage by generating multiple vortices and enabling jet vectoring, though they remain constrained by downstream vortex diffusion. Single-actuator multi-orifice designs en-hance near-field heat transfer through multiple interacting vortices, yet far-field per-formance remains an issue. Coaxial synthetic jets improve vortex dynamics and overall performance but face challenges at high Reynolds numbers. Synthetic jet arrays with independently controlled actuators offer the greatest potential, enabling jet vectoring and focusing to enhance entrainment, expand spanwise coverage, and improve far-field performance. By examining key limitations and technological advances, this review lays the foundation for expanded use of synthetic jets in practical engineering applications.

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The scientific interest in Triply Periodic Minimal Surface (TPMS) lattices for thermal applications has grown exponentially in recent years, largely driven by the advances in additive manufacturing. However, the lack of a transparent and reproducible selection methodology in previously published reviews hinders the clarity and comparability of findings. This paper adopts and customizes the APISSER framework, a structured and repeatable method that guides literature reviews through five steps: defining research questions, identifying sources, screening studies, extracting data, and reporting results. This approach is applied to investigate the use of TPMS structures in heat transfer applications, including heat sinks and heat exchangers. The study covers peer-reviewed journal articles from 2000 to 2024, analyzing key aspects such as application domain, topology, working fluid, flow regime, additive manufacturing method, and numerical modeling details. Results show a predominant use of numerical studies, with Gyroid and Diamond topologies being the most investigated. These structures are frequently modeled as porous media, especially for estimating pressure drops, although detailed thermal analysis often relies on full-resolution geometries. Water and air are the most common working fluids, while turbulence modeling remains limited to RANS approaches. The structured methodology adopted ensures high reproducibility and offers a quantitative foundation for the identified knowledge gaps to guide future experimental and computational research.

Abstract: The article is devoted to the synthesis of mathematical models of metalworking processes by cutting for digital counterparts of metal-cutting machines. Despite the development of modern measuring instruments, data acquisition and transmission systems, as well as the growth of computing power of modern computers, the problem with a high-quality mathematical description of the cutting process is urgent. Methods: When developing mathematical models of elastic-thermodynamic interaction, the authors relied on analytical methods of model construction, as well as on the analysis of experimental data obtained as a result of the conducted research. The STD.201-1 stand was used as measuring equipment; data processing was carried out in the Matlab 2018 mathematical software package. Results: A comparison of the results of mathematical modeling of the synthesized model and the results of measuring cutting processes on a metal-cutting machine show a high degree of convergence. The modeled and experimental graphs of the cutting force decomposed along the deformation axes and the graphs of the cutting temperature differ only in the area of the transient process (tool embedding). Conclusions: The models obtained during synthesis can become the basis for building a digital twin system.

Abstract: The ever-increasing use of diverse types of engineered nanoparticles (ENPs) in industries, medicine, and consumer products has resulted in their uncontrolled release into aquatic environments and soil-plant systems. ENPs may transform and release toxic by-products upon release, raising concerns about their environmental behavior and potential risks. However, accurately measuring the concentrations of ENP in these ecosystems remains challenging. Recent studies have highlighted the toxic effects of ENPs on various organisms, but assessing the risk in aquatic and soil-plant systems consists of a critical issue in nanoecotoxicology. ENPs interact with various environmental materials like organic matter, soil, sludge, and other pollutants. These interactions of ENPs can form complex assemblies, which may alter the toxicity and environmental fate. This study examines the interactions of ENPs in aquatic and soil-plant environments, focusing on their transformation, toxicity, and ecological impact. Identification of the knowledge gaps related to the ENP interaction and outlining the directions for future consideration for a better understanding of the environmental risks have been explained in this study. Additionally, the research addresses the challenges of evaluating nanotoxicity and highlights the need for improved environmental regulations and assessment techniques for engineered nanomaterials.

Abstract: This paper presents research aimed at detecting and quantifying backlash in the feed motion subsystem of machine tools using positioning accuracy test results. By applying shallow neural networks, we not only estimate backlash magnitude but also analyse the impact of key parameters on its occurrence. This approach enables continuous, data-driven monitoring and prediction of backlash variation, while reducing machine tool calibration time in industrial settings. The experimental study followed ISO 230-2, using two standard motion methods while monitoring machine and ambient temperature, atmospheric pressure, and humidity. The results show how experimental parameters affect detected backlash. A feed-forward neural network predicts current backlash at specific measurement points. The results demonstrate high accuracy in backlash estimation, significantly simplifying machine calibration during maintenance and providing a foundation for real-time error compensation.

Abstract:

Helically coiled tube heat exchangers (HCTHEXs) are widely deployed in compact thermal systems, yet reliable effectiveness–NTU (ε–NTU) correlations for realistic fluid to fluid operation remain scarce. This work presents a comprehensive three dimensional numerical study of a vertical tube in annular shell HCTHEX under laminar flow on both coil and shell sides, with water as the working fluid in all cases. More than 2400 steady state CFD simulations in ANSYS Fluent are performed to systematically vary morpho hydrodynamic parameters, including coil pitch ratio, flow rates, and thermal boundary conditions. The numerical model is verified against established correlations for coil side Nusselt number and pressure drop, with discrepancies typically below 10%, and is then used to construct a global ε–NTU database. For each pitch ratio, three candidate ε–NTU correlations are evaluated: a power law relation in log–log space, a log quadratic polynomial in log(NTU), and a nonlinear exponential form of the type ε=1-exp⁡(-a NTU^b). The log quadratic and exponential models consistently reproduce the characteristic rising–plateau ε–NTU behavior with R²values between 0.90 and 0.98, whereas simple power laws underpredict the curvature. A global log based regression model log⁡(ε)=f[log⁡(NTU),P]captures the overall monotonic trends but attains only moderate accuracy (R²≈0.59 in ε space), highlighting the intrinsic nonlinearity of the ε–NTU–pitch surface. To overcome this limitation, generalized additive models (GAM) and bagged decision tree ensembles are trained using log⁡(NTU)and pitch as predictors. These machine learning regressors yield substantially improved agreement with the CFD data, with R²≈0.94for GAM and R²≈0.91for the ensemble, while a simple average of both predictions achieves the highest fidelity (R²≈0.95). The resulting pitch specific closed form correlations and global GAM/Ensemble surrogate provide practical tools for predicting the effectiveness of helically coiled tube heat exchangers over a broad range of morpho hydrodynamic conditions.

Abstract: Helicopter vibration is an inherent characteristic of rotorcraft operations arising from transmission dynamics and unsteady aerodynamic loading, posing challenges to flight control and longevity of structural components. Excessive vibration elevates pilot workload and accelerates fatigue damage in critical components. Leveraging advances in optimal control and microelectronics, the active vibration control methods offer superior adaptability compared to the passive techniques limited by added weight and narrow bandwidth. In this study, a comprehensive vibration analysis and optimal control framework are developed for the Bo 105 helicopter rotor blade exhibiting flapping, lead–lag, and torsional (triply coupled) motions, where a Linear Quadratic Regulator (LQR) is employed to suppress vibratory responses. An analytical formulation is constructed to estimate the blade’s sectional properties and are used to compute the coupled natural frequencies of vibration by modified Galerkin method. An orthogonality condition for the coupled flap–lag–torsion dynamics is established to derive the corresponding state-space equations for both hovering and forward-flight conditions. The LQR controller is tuned through systematic variation of the weighting parameter Q, revealing an optimal range of 102−104 that balances vibration attenuation and control responsiveness. The predicted frequencies of the vibrating rotor blade are compared with the finite element modeling results and published experimental data. The proposed framework illustrates the underlying dynamics of the triply coupled rotor-blade’s vibration, demonstrates modal vibration reduction on the order of 60–90%, and provides a theoretical benchmark for future actuator-integrated and experimental studies.

Abstract: This study investigates the dynamic behavior 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. The first eight natural frequencies and corresponding mode shapes were extracted for each model. The results revealed that lower modes exhibit global bending and torsional behaviors, whereas higher modes demonstrate increasingly localized deformations. The natural frequencies decrease by approximately 180° 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 between natural frequencies, cross-sectional dimensions, and frame angles, achieving high predictive accuracy (R² > 0.98). This regression model provides an efficient design tool for predicting vibrational behavior and optimizing frame configurations without extensive simulations. Experimental modal analyses validated the numerical results, confirming the effectiveness of the modeling approach. Overall, this study offers a comprehensive understanding of the dynamic characteristics of re-entrant frame structures and proposes practical design strategies for improving vibrational performance, particularly in applications such as machine foundations, vibration isolation systems, and aerospace structures.

Abstract: Predicting the remaining useful life (RUL) of machines is important for safe and efficient operation, but short signal spikes near the start of faults often disturb prediction results. This study proposes a spike-guided, multi-stage method that combines data learning with a simple physical update. A convolution block filters spike signals using envelope energy and basic variance checks, and a two-path predictor joins time features with a physical correction term. Tests on the NASA turbofan and PRONOSTIA bearing datasets showed that the method cut RMSE by 14–21% and raised the early-warning score by about 18% compared with other deep learning models. The spike check step also reduced false alarms and kept the trend of wear smoother over time. These results show that short bursts hold key signs of early faults and that adding physical rules helps keep forecasts more stable. The method can help with early maintenance planning in factory systems, though wider tests under more working conditions are still needed.

Abstract: Industrial remaining useful life (RUL) prediction is often affected by sudden spikes and irregular signal changes that make degradation patterns unstable. To solve this problem, this study presents a consecutive threshold learning (CTL) method that can find and remove short-term spikes while keeping the main trend of equipment wear. The approach uses a simple time-based prediction model built on TC-WaveNet and adjusts the spike level with dynamic thresholds that follow signal energy and time order. Tests on the C-MAPSS and other industrial datasets showed that CTL cut false spike alarms by 27% and reduced mean absolute error by 15% compared with normal deep models without thresholds. In addition, the model provides gradient-based maps that show how faults develop over time, making the results easier to understand for engineers. Overall, CTL improves the stability and clarity of RUL prediction and can be applied to real-time condition monitoring and maintenance planning in industrial systems.

Abstract: In the presented research, aluminum-doped zinc oxide (AZO) thin films were synthe-sized on high-power transmission lines using the RF magnetron sputtering process. The impact of deposition power (160 W to 280 W) and deposition pressure (2 Pa to 5 Pa), on the structural, surface, wettability and anti-icing properties of the films was thoroughly investigated. Key characteristics like material composition, wettability, an-ti-icing behavior, and average crystal size were analyzed. The optimization of wetta-bility and anti-icing performance was carried out using two-factor, four level design of the Taguchi method. Considerable variation in water contact angle from 92.3° to 123.6°, has been observed suggesting an enhancement in hydrophobic nature with op-timized condition. Anti-icing tests demonstrated that the coated surface delayed ice accumulation by approximately 4.56 times compared to the uncoated surface. X-ray diffraction (XRD) analysis was carried out to confirm notable changes in the intensity of the (002) peak along the c-axis, directly correlating with grain size modification. The change in surface roughness was studied using AFM and results were compared to es-tablish relation between surface roughness and average grain size. Overall, the find-ings highlight the critical role of deposition parameters and their interactions in modi-fying the surface and structural properties of AZO thin films, which demonstrates their potential application for improving the anti-icing performance of transmission lines.

Abstract: This research examines the feasibility of recovering and recycling condensate water, a waste byproduct generated by Atlas Copco ZR315 FF industrial air compressors utilizing oil-free rotary screw technology with integrated dryers. Given the growing severity of global water scarcity, finding alternative water sources is essential for sustainable industrial practices. This study specifically evaluates the potential of capturing and treating compressed air condensate as a viable method for water recovery. The investigation analyzes both the quantity and quality of condensate water produced by the ZR315 FF unit. It contrasts this recovery approach with traditional water production methods, such as desalination and atmospheric water generation (AWG) via dehumidification. The findings demonstrate that recovering condensate water from industrial air compressors is a cost-effective and energy-efficient substitute for conventional water production, especially in water-stressed areas like Morocco. The results show a significant opportunity to reduce industrial water usage and provide a sustainable source of process water. This research therefore supports the application of circular economy principles in industrial water management and offers practical solutions for overcoming water scarcity challenges within manufacturing environments.

Abstract: This study develops a closed-form solution to predict pressure-driven stress and displacement fields in a thick-walled, functionally graded (FG), incompressible, multi-layer hyperelastic cylinder made from Polyvinyl Chloride (PVC), subjected to internal pressure. The exact solution ensures incompressibility, which finite element methods (FEM) may not guarantee. Properties vary smoothly through the thickness using a Mooney–Rivlin model. Two cases are examined: bi-layer and tri-layer cylinders, where the properties in the second layer of the bi-layer case are 50% lower than the first, and in the tri-layer case, the second- and third-layers’ properties are 30% and 60% lower, respectively. Two material grading conditions are considered: in the first, properties at the largest radius are 1.2 times those at the smallest radius, and in the second, they are 0.8 times. Gradation is modeled using an exponential-logarithmic function. The field equations reduce to a nonlinear scalar condition for the integration constant governing radius mapping, leading to explicit solutions for radial displacement and radial, tangential, and axial stresses under internal pressure. Both analytical and FEM solutions yield identical results, with errors under 1% in all cases. The analysis recovers homogeneous limits and provides conditions where continuous gradation reduces stress concentrations compared to discretely layered baselines.

Abstract:

The hard-bottom layer in paddy fields significantly impacts the driving stability, operational quality, and efficiency of agricultural machinery. Continuously improving the precision and efficiency of unmanned, precision operations for paddy field machinery is essential for realizing unmanned smart rice farms. Addressing the unclear influence patterns of hard-bottom contours on typical scenarios of agricultural machinery motion and posture changes, this paper employs a rice transplanter chassis equipped with GNSS and AHRS. It proposes methods for acquiring motion state information and hard-bottom contour data during agricultural operations, establishing motion state expression models for key points on the machinery antenna, bottom of the wheel, and rear axle center. A correlation analysis method between motion state and hard-bottom contour parameters was established, revealing the influence mechanisms of typical hard-bottom contours on machinery trajectory deviation, attitude response, and wheel trapping. Results indicate that hard-bottom contour height and local roughness exert extremely significant effects on agricultural machinery heading deviation and lateral movement. Heading variation positively correlates with ridge height and negatively with wheel diameter. The constructed mathematical model for heading variation based on hard-bottom contour height difference and wheel diameter achieves a coefficient of determination R² of 0.92. The roll attitude variation of the agricultural machinery is primarily influenced by the terrain characteristics encountered by the rear wheels. A theoretical model was developed for the offset displacement of the antenna position relative to the horizontal plane during roll motion. The accuracy of lateral deviation detection using the posture-corrected rear axle center and bottom of the wheel center improved by 40.7% and 39.0%, respectively, compared to direct measurement using the positioning antenna. During typical vehicle entrapment events, a segmented discrimination function for entrapment states was developed when the terrain profile steeply declines within 5 seconds and roughness increases from 0.008 to 0.012. This method for analyzing how hard-bottom terrain contours affect the position and attitude changes of agricultural machinery provides theoretical foundations and technical support for designing wheeled agricultural robots, path-tracking control for unmanned precision operations, and vehicle-trapping early warning systems. It holds significant importance for enhancing the intelligence and operational efficiency of paddy field machinery.

Abstract: This study examines how the clarity of frequency-domain characteristics in vibration signals influences the performance of deep learning models for bearing fault classification. Two datasets were used: the CWRU benchmark dataset, which exhibits distinct and easily separable spectral signatures across fault modes, and a custom low-speed bearing dataset in which small defects do not significantly alter the frequency spectrum. To enable a clear and interpretable comparison, we deliberately employed simplified CNN and LSTM ar-chitectures with a single core layer. This design choice allows us to directly attribute per-formance differences to the inherent learning mechanisms of each architecture rather than the complexity of the models. Our representation analysis reveals that LSTM-F achieves the highest accuracy when the dataset contains clearly distinguishable spectral patterns, as in the CWRU case. In con-trast, CNN-S outperforms both LSTM models in the experimental datasets, where fault-induced frequency characteristics are weak or ambiguous. Representation analyses further reveal that LSTM-F relies on consistent frequency-indexed patterns, whereas CNN-S captures more complex time–frequency interactions, making it more robust under low-separability conditions. These findings demonstrate that the optimal deep learning architecture for bearing fault classification depends on the degree of frequency separability in the data. LSTM-F is pref-erable for severe faults with distinct spectral features, while CNN-S is more effective for minor defects or systems exhibiting complex, weakly discriminative frequency behavior.

Abstract: Designing hyperelastic porous microstructures under finite strain is challenging because bending, buckling, contact, and densification interact to produce nonconvex and one-to-many relations between topology and response. We present HyperDiff, a conditional diffusion framework that reformulates inverse design as probabilistic sampling rather than deterministic regression. A compact B-spline encoding of the target force--displacement curve captures the system’s energy-evolution trend, providing temporal and mechanical context that guides the denoising process toward physically consistent configurations with the desired multi-stage deformation behavior. The workflow integrates Gaussian random field (GRF)-based topology generation, constitutive calibration, large-deformation finite-element simulations, and quasi-static compression experiments. Across held-out and interpolated targets, the generated microstructures accurately reproduce sequential deformation stages (bending-buckling-densification) and global responses, with deviations typically below 10%, while preserving manufacturability and one-to-many design diversity. The current implementation focuses on two-dimensional unit cells under quasi-static compression, yet the framework is extensible to 3D, multi-resolution, and multi-physics systems. By combining physics-aware conditioning with generative sampling, HyperDiff establishes a practical front end for mechanics-based design workflows, applicable to programmable soft actuators, impact-energy absorbers with tunable plateaus, and rapid exploration of nonlinear architected materials for soft and deformable systems.

Abstract: Due to a number of advantages, such as the high power-to-weight ratio of the system, the possibility of easy control and the freedom of arrangement of the system components on the machine, hydrostatic drive is one of the most popular methods of machine drive. The actuators in such a system are hydraulic cylinders that convert fluid pressure energy into mechanical energy for reciprocating motion. One disadvantage of conventional actuators is their weight, so research is being conducted to make them as light as possible. Directions for this research include the use of modern engineering materials such as composites and plastics. This paper presents the possibility of using new lightweight yet strong materials for the design of a hydraulic cylinder. The base of the hydraulic cylinder were designed and subjected to FEM numerical analyses. The base was made of PET. In addition, a composite cylinder made of wound carbon fibre was subjected to numerical analyses and experimental validation. The numerical calculations were verified in experimental studies. To improve the reliability of the numerical calculations, the material parameters of the composite materials were determined experimentally instead of being taken from the manufacturer's data sheets.

Abstract: This study presents a composite material that combines shape-memory function with a thermal self-fusing mechanism, designed for use in semi-rigid wearable structures. The material was prepared using a polyurethane-based matrix containing heat-activated bonding agents. When heated to 70 °C, the stiffness changed from 2.8 MPa to 24.5 MPa. A total of 30 samples were tested. The shape recovery reached over 96% within three minutes, and the bonding strength after fusion reached 4.8 MPa. Mechanical tests showed that the material maintained stable stiffness and bonding performance after 100 heating cycles, with less than 5% change in stiffness and less than 10% loss in bonding strength. Compared to control samples without bonding function, the proposed system showed better structural recovery and durability. These results suggest that this material may be used in wearable systems such as exoskeleton joints or support devices that require repeated stiffness changes and reliable reattachment between parts.