To behave safely and socially in human–robot shared workplaces, autonomous mobile robots (AMRs) should not only be able to detect and track people’s motions but also predict their social interaction intention (SII). In this study, we address the problem of human motion-relied SII prediction using an onboard sensor-based multi-people detection and tracking approach. In the first step, we present an integration of human detection and tracking through combining LiDAR-based leg detector and RGB-D-based YOLO human detection supported by social characteristics-oriented scan-to-track data association. A social dynamic confidence function is generated to analyze the reliability of each person tracked using the leg detector using human social norms and dynamic features, then combined with the YOLO-based confidence factor to increase the reliability of the tracking system. In the second step, we address SII prediction for individuals and groups of people using human relative distance, relative motion orientation, and social characteristics. To examine and validate the proposed methods, two sets of real-world experiments of multi-people tracking and SII prediction were designed, resulted in 95% tracking accuracy and 74% intention estimation accuracy in challenging social situations.
This study focuses on predicting the ultimate tensile strength (UTS) of friction stir welded dissimilar aluminium alloys using data-driven machine learning approaches. Two predictive models, support vector machine (SVM) and artificial neural network (ANN) were created to model the correlation between important process parameters tool rotational speed, welding speed, tool geometry (D/d ratio) and tilt angle and the achieved joint strength. Experimental data were collected, pre-processed, and used to train and evaluate the models. The results show that SVM outperforms ANN in accurately predicting UTS, with higher correlation and lower error metrics. Feature importance analysis revealed that tool rotational speed and D/d ratio are the most influential parameters affecting weld strength. Incorporating predictive modelling in FSW can significantly improve process efficiency, reduce trial-and-error experimentation, and enhance the quality of welded joints.
The paper proposes a segmented oxygen-doping process technology and discusses and analyzes the surface morphology, quality, indentation morphology, and tool wear performance of diamond films. The results show that using the segmented oxygen-doping process (first low-temperature oxygen doping for 2 h, followed by high-temperature oxygen-free deposition for 9 h), the diamond morphology is uniform and clear, the residual stress of the diamond film is 0.674 GPa (about 53% of that of the high-temperature process), and the indentation crack diameter is about 312.7 µm. In addition, XRD peak intensity results show that the samples prepared by the segmented oxygen-doping process preferentially exhibit (111) and (110) orientations. The deposition reaction rate is increased to 3.64 µm/h (about 84% of the high-temperature process). The surface wear of diamond-coated tools is reduced to 92.0 µm, and the wear trend of the coated tools is lower than that of the low-temperature and high-temperature process wear curves, improving their wear resistance.
Due to the electromagnetic vibration of the driving motor and the dynamic meshing excitation of the transmission gears, it is very prone to electromechanical coupling vibration. The superimposed vibration frequency is a significant challenge to vibration source location and vibration suppression. An investigation was performed to gain insights into the operation of a coal mining machine’s transmission system during problematic conditions. First, a math model for controlling the permanent magnet motor is built to find the control settings. The decoupling mechanism of motor current was revealed. Subsequently, time-varying mesh stiffness models were developed for both normal operating conditions and gear spalling fault scenarios. The changes in how stiffness varies over time when there are spalling faults were explained. Finally, considering factors such as gear error, tooth side clearance, and bearing stiffness, the dynamic model of the cutting part of the coal shearer was established. The influence laws of internal excitation, load impact, and spalling failure on the dynamic characteristics of the system’s electromechanical coupling were studied, and the typical features of gear spalling failure were extracted. The study’s results are crucial for comprehending how to assess the state and minimize vibrations in the transmission systems of coal mining equipment.
The transition to a circular economy in the automotive sector underscores the need for efficient and sustainable disassembly of end-of-life vehicles (ELVs). Disassembly enables recovery of high-value components and critical raw materials while reducing environmental burdens and improving workplace safety. This article presents a systematic review of global practices, challenges, and opportunities in automotive disassembly, with particular emphasis on digital innovations. Using a PRISMA-based screening and thematic analytical synthesis, 250 records were identified and 100 studies retained. The analysis is organized into three themes: (i) regional disassembly practices in Europe, North America, Asia, Africa, South America, and Oceania; (ii) recovery of high-value components such as lithium-ion batteries, catalytic converters, and lightweight materials; and (iii) reverse logistics and Industry 4.0/5.0 technologies, including Artificial Intelligence, simulation tools, augmented reality, and collaborative robotics. Findings reveal regional disparities in infrastructure, regulation, and safety standards but also highlight innovations that enhance efficiency, sustainability, and Human Factors / Ergonomics. The review underscores the importance of reverse logistics, digital traceability, and ergonomic safeguards while calling for harmonized standards such as Extended Producer Responsibility and ISO. It offers actionable insights for researchers, policymakers, and industry stakeholders aiming to align ELV management with circular economy goals.
This study independently develops a software system with adaptive compatibility to the hardware platform. Based on this, wire arc additive manufacturing (WAAM) of 4043 aluminum alloy thin-walled components is conducted, and forming process parameters are optimized. According to the average melt width and residual height of single-pass single-layer deposits, the optimal parameters are determined as welding speed 8 mm/s and wire feeding speed 3.5 m/min. The optimized interlayer cooling time is set to 240 s for the bottom layer, 120 s for the middle layer, and 60 s for the top layer. Microstructural observations reveal banded fusion lines at adjacent layer interfaces, with denser interlayer regions featuring developed dendrites. Si phase granulation occurs in the upper region; the middle region has smaller grains dominated by heterogeneous grains, while the upper region consists mainly of columnar grains. Microhardness reaches a minimum of 74.7 HV in delamination zones and rises to 80.4 HV in interlayer regions, with fluctuations gradually weakening as layer thickness increases.
Inconel 625 alloy is widely recognized for its excellent performance in corrosive and abrasive environments, making it a preferred material in challenging applications. To further enhance its resistance to corrosion and wear, this study investigates the application of plasma-sprayed coatings using Chromium Carbide Cr 3 C 2 and Stellite. Both coated and uncoated Inconel 625 specimens were subjected to high-temperature sliding wear tests using a pin-on-disc tribometer. The wear behavior was evaluated by examining the worn surfaces through scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy. Additionally, the hardness of all specimens was measured using a Vickers hardness tester. Results from microhardness tests revealed a significant increase in hardness due to the coatings. The Cr 3 C 2 -coated sample showed an increase in Vickers hardness (HV) by 42.6%, while the Stellite-coated sample exhibited a 32.7% rise in HV, compared to the uncoated Inconel 625 specimen, which recorded a hardness of 505 HV. SEM analysis confirmed a strong adhesive bond between the coating materials and the substrate, indicating good coating integrity. Furthermore, wear tests conducted at varying temperatures demonstrated different wear behaviors. Notably, the Stellite-coated samples performed better under high-temperature conditions, showing improved wear resistance compared to both the Cr 3 C 2 coated and uncoated samples. These findings suggest the potential of such coatings for enhancing the durability of Inconel 625 in extreme working environments.
This paper proposes a hybrid material sill beam structure combining high-strength steel outer panels with carbon fiber reinforced plastics (CFRP) reinforcement plates to enhance the stiffness of the vehicle underbody. Through multi-objective optimization, the crashworthiness and lightweight performance of the underbody ring frame are improved. First, a finite element model for the white body and full vehicle of an electric vehicle is established for frontal impact testing, with model accuracy verified through experiments. A multi-level optimization approach for CFRP sill beam reinforcement plates based on T700/WP-R2300 composite material is proposed, encompassing free-dimension optimization, dimensional optimization, and layup sequence optimization. The optimized sill beam assembly is integrated with components such as the A-pillar and torsion box to form the lower body frame ring. A multi-objective optimization model based on the NSGA-II algorithm was established to optimize component thicknesses. The TOPSIS method was employed to obtain the optimal ranking of the Pareto solution set. Optimization results demonstrate that the optimized design achieves a 29.2% weight reduction in both side lower sill beams and a 10.73% weight reduction in the lower body frame ring. Concurrently, acceleration at the right B-pillar decreased by 6.67%, along with a 4.53% reduction in intrusion at the junction between the right lower A-pillar and lower sill beam. Significant lightweighting objectives were achieved while maintaining the frontal crash performance of both the body-in-white and the complete vehicle.
Angular contact ball bearings exhibit superior performance, and are extensively utilized in high-precision instruments. Nonetheless, prolonged interaction among the bearing's components might result in gradual wear and defects. Firstly, the dynamic properties of the bearings are investigated by thoroughly analyzing the entire contact process of a defective ball within the outer or inner ring area. A composite fault dynamics model of the ball-outer ring is established, taking into account thermal deformation, time-varying displacement, and impact excitation conditions. The impact of composite defects on the system's nonlinear vibration is analyzed using phase trajectory, Poincar & eacute; diagram, and bifurcation diagram. After all, experimental methods are employed to acquire vibration data. Results show amplitude increases upon ball and outer ring faults, with timedomain vibration signals exhibiting clear periodicity. Then, the frequency domain graphic facilitates the identification of frequency components related to the ball and outer ring, including fb, fo, as well as frequencies associated with the cage, such as nfc and mfb +/- nfc. Finally, the maximum error when comparing the simulated results with the experimental data are 2.04%. It provides invaluable information for fault identification and health assessment of angular contact ball bearings.
With the growing volume and complexity of crewed space missions, astronauts face heavy workloads and safety risks when performing inspection, logistics, and experimental operations inside confined space station cabins. To address this, we propose a spherical modular self-reconfigurable free-flying robot specifically designed for micro-gravity cabins. Guided by TRIZ theory, a compact mechanical architecture is developed that reconciles the contradictions among thrust, docking precision, and volume constraints. The robot employs six orthogonally arranged ducted-fan thrusters for full six-degree-of-freedom maneuvering and a hybrid mechanical–electromagnetic docking mechanism that enables reliable multi-robot assembly within large pose and position tolerances. A unified Lagrangian model is then established for both the free-flying platform and the post-reconfiguration multi-body system with an attached manipulator. To cope with nonlinear, strongly coupled dynamics and parameter uncertainties, a T–S fuzzy neural network PID controller is designed to realize on-line tuning of PID gains. Comparative simulations with classical PID and fuzzy-PID schemes show that the proposed controller significantly shortens settling time, reduces overshoot, and improves steady-state accuracy, verifying the effectiveness and robustness of the overall structural design and control framework for intra-vehicular service robots.
Exhaust gas recirculation (EGR) is widely implemented in internal combustion engines to reduce pollutant emissions. The traditional way of introducing EGR is by creating a homogeneous air-exhaust gas mixture in the engine intake system. This paper proposes a novel technique of EGR based on the direct injection of exhaust gases into the cylinder. Such an approach can create exhaust gas stratification which offers benefits compared to the traditional technique. To gain insight of the consequence of the novel approach, 3D-CFD numerical simulations were employed. Results showed a high velocity EGR jet, emanating from the three EGR inlets, which crosses the cylinder and hits the junction piston-cylinder liner on the opposite side. The jet then continued its course creating a clear stratification of EGR inside the cylinder along the liner surface. Moreover, simulation results suggested a direct advantage of the proposed approach, as a significant increase of turbulence was observed and it persisted up to spark timing when compared to a homogeneous EGR approach, with the net consequence of a faster burning rate for the stratified approach. Overall, the results show that the proposed approach modifies the in-cylinder flow and turbulence intensity, which in turn changes the flame development.
Agricultural tractor traction performance prediction is crucial for reducing fuel consumption and improving operation quality in modern agriculture. To address the limitations of traditional empirical modeling and regression methods in predicting tractor traction performance, where model accuracy and computational efficiency are often constrained by multidimensional nonlinear characteristics, this study introduces a Physics-informed Neural Network (PINN) framework. The proposed approach integrates the nonlinear approximation capability of neural networks with prior knowledge from traction dynamics, and incorporates physics-based constraint terms into the loss function to enhance physical consistency and generalization. Unlike conventional black-box data-driven models, the PINN framework simultaneously fits observed data and enforces consistency with physical laws during training, which prevents physically implausible predictions. Experimental results show that the PINN achieves high accuracy, particularly in predicting traction power and fuel consumption rate, with an average coefficient of determination (R2) above 95% across four output indicators. In addition, the predicted trends are highly consistent with measured data, demonstrating the potential of this method to provide strong technical support for the optimization and design of agricultural machinery power systems.
Hydrogen/diesel dual-fuel (HDDF) engines are being investigated as a promising solution for reducing carbon dioxide and providing a transition to zero-carbon fuels. However, modeling these engines presents challenges due to the complex interactions between the two fuels. Computational fluid dynamics modeling is often inaccurate across various operating conditions, while conventional data-driven approaches frequently face issues related to a lack of interpretability. Furthermore, based on the complexity of the system, there is a shortage of accurate physics-based modeling. This study explores temporal Kolmogorov–Arnold networks (T-KANs) as an alternative to conventional machine learning (ML) models. T-KANs offer a structured framework for function approximation that can efficiently learn the underlying dependencies with historical data while ensuring interpretability. A T-KAN model is developed and trained using experimental HDDF engine data, demonstrating its ability to predict performance metrics, including indicated mean effective pressure and nitrogen oxides. A comparative analysis of seven different models of well-known ML methods highlights the advantages of T-KANs in terms of accuracy, generalization, and computational efficiency. Given its computational efficiency and a coefficient of determination of 0.956, the T-KAN with a 10-lookback is suitable for a model-based controller. This configuration effectively utilizes historical data while operating independently of sensor feedback.
The microclimate inside a layer house is critical to the well-being and egg production performance of laying hens. The objective of this study was to evaluate the feasibility of the computational fluid dynamics approach in simulating the indoor temperature and velocity inside an enriched-colony layer barn. While the current investigation focuses on an empty barn case, it establishes baseline data and conditions, providing a foundation for future numerical model improvements to incorporate the effects of a fully populated layer house. The results of this study could support the assessment of alternative ventilation schemes, including adjustments to the position and number of inlet baffles and exhaust fans, as well as the addition of inlet flaps and evaporative cooling pads, using airflow and temperature distribution patterns in layer houses.
To address the inefficiency, high labor intensity, safety hazards, and skill dependency of traditional manual welding methods for small-diameter pipelines, this study proposes a newly designed internal welding robot tailored for such pipelines. The robot's key components—including its mobility system, clamping mechanism, welding gun lifting and rotating units, and wire feeding system—were optimized using SolidWorks 3D modeling. These designs enable the robot to move autonomously within the pipe and perform high-quality automated welding through precise mechanical coordination. Virtual simulations using ADAMS software were conducted to evaluate the robot's clamping motion, climbing ability, and traction performance, confirming the stability and reliability of its movement inside pipelines. Simulation results show that the robot can adapt to complex working conditions. This design significantly enhances welding automation, reduces reliance on manual labor, and offers an efficient solution for small-diameter pipeline welding.
The growing scarcity and rising costs of fossil fuels are accelerating the global transition toward energy-efficient, lowemission industrial systems. Cement plants reject large amounts of thermal energy through exhaust gases, offering an opportunity for heat recovery. This study investigates the thermodynamic performance of a diffusion-absorption refrigeration (DAR) system enhanced with an ejector and a compressor (DAR-EC) and powered by residual heat from a cement kiln. The system is modelled using the M2EP framework (mass, energy, exergy, and performance) and optimized through a particle swarm algorithm. The particle swarm optimization algorithm identifies optimal pressure and concentration levels that maximize coefficient of performance, subject to constraints on the low-temperature heat source. The working fluid is a ternary H2O-NH3-H2 mixture, enabling continuous circulation between the evaporator and absorber. Integrating the ejector and compressor increases the operating pressure at the evaporator outlet and the absorber inlet, thereby improving cycle performance. A sensitivity analysis to temperature, concentration, and pressure variations is performed. Results show that the DAR-EC configuration significantly increases the cooling capacity and raises the coefficient of performance from 0.27 (DAR) to 0.49 (DAR-EC). Exergy analysis additionally reveals that generator and absorber irreversibilities dominate system losses. The model is validated against published data and demonstrates its suitability for industrial heat-recovery applications. The findings provide a decision-support basis for implementing DAR-EC systems to valorize waste heat. Given that this model uses a water-ammonia mixture, the heat source temperature must be between 80 and 200 degrees C.
Conventional search-and-rescue and reconnaissance robots often fail to navigate post-disaster environments with high barriers or narrow gaps due to their cumbersome form factor. To mitigate the inherent limitations of existing robots—including excessive weight, inferior obstacle-surmounting performance, and poor impact resistance, this study proposes a novel throwable spherical deployable robot system inspired by the morphological characteristics of armadillos and octopus tentacles. The robot’s structure was designed to transform from a spherical configuration to an omnidirectional mobile vehicle through cable-driven mechanisms. Kinematic modeling and simulation of the flexible deployment units were conducted to validate their motion rationality and reliability. A mechanical model of the flexible units was established, revealing that the bending performance of the flexible joints correlate with their strain energy. Force analysis was conducted on the robot in its deployed configuration, and a functional prototype was fabricated to verify the practical feasibility of the proposed design. Experimental results indicate that the developed throwable spherical deployable robot exhibits excellent impact resistance performance; its height is reduced by 50% in the deployed state, which allows it to access narrower crevices, while its omnidirectional mobility boosts operational maneuverability of the robot in complex environments.
Magnetic gears, utilizing permanent magnet fields for contactless power transmission, offer significant advantages for wind power applications, including no mechanical loss, elimination of lubrication, and inherent overload protection, addressing key limitations of conventional gearboxes. However, their adoption is hindered by common topological drawbacks: weak magnetic field modulation, low torque density, structural complexity, magnetic saturation, and excessive flux leakage. To overcome these limitations, this study proposes a modulation-enhanced monotonic magnetic ring double-modulated three-rotor coaxial magnetic gear. Simulation analysis first compared modulation schemes and validated the proposed gear’s feasibility against a traditional coaxial design. Subsequently, single-factor and Plackett–Burman screening tests identified critical factors significantly influencing torque performance. These factors were then analyzed using Box–Behnken testing, establishing a corresponding response surface model. Following model accuracy verification, multi-objective optimization was performed. Results demonstrated substantial improvements: torque density increased by 19.05%, inner rotor torque fluctuation reduced to 2.74%, and outer rotor torque fluctuation reduced to 2.24%. A trade-off was observed, with intermediate rotor torque fluctuation rising to 17.8%. This work presents a novel structural concept for magnetic transmission design, highlighting the enhanced performance of the proposed topology and underscoring the significant potential of advanced magnetic gears in future wind power transmission systems.
Aluminum and its alloys are widely used in industrial fields such as aerospace and automotive due to their excellent rigidityto-weight ratio. However, the oxide layer that forms on the surface of aluminum poses challenges for powder metallurgy processes, particularly during sintering. Material extrusion additive manufacturing, a powder-binder-based process, offers advantages of rapid prototyping and low cost but remains largely unexplored for aluminum systems. This study investigates each step of the material extrusion process for AlSi10Mg powders. Feedstocks formulated with 65 vol.% of powder, 25-27 vol.% of paraffin wax, 2 vol.% of stearic acid, and 6-8 vol.% of ethylene-vinyl acetate were printed to produce simple and complex geometries. It was established that the particle size distribution is a critical parameter driving the feedstock behavior and stabilizing its printability. Printed parts underwent thermal wick-debinding at 250 degrees C for 2 h under argon followed by liquidphase sintering varying from 565 to 575 degrees C for 2 h in a nitrogen atmosphere, using sacrificial magnesium granules as an oxygen getter. The sintered parts reached a relative density up to 98% and exhibited an adequate microstructure. However, further optimization is required to mitigate warping during sintering and improve dimensional stability.
The blades of vertical-axis turbines (VATs) operate in curved and nearly circular flow. Compared to a uniform flow, the curved streamlines alter the aerodynamic coefficients of the airfoil. As a result, measuring and predicting the aerodynamic coefficients of an airfoil in curved flow poses a challenge. In this work, we first present a methodology to properly determine the aerodynamic coefficients (lift, drag, and moment) of a NACA 0015 airfoil attached at the quarter-chord in steady curved flow at Rec = 6 & times; 106 using blade-resolved CFD with a "key-hole mesh domain". By varying the airfoil's angle of attack and the airfoil's chord to turbine's radius ratio (c/R), the aerodynamic coefficient curves are obtained and presented. The results show that the curvature effects on the aerodynamic coefficients are significant. It is observed that the drag coefficient is closely related to the moment coefficient of the airfoil and the moment coefficient about the airfoil's rotation axis. Furthermore, the Coriolis effect is shown to be responsible for reducing the drag coefficient values compared to those in a uniform flow. The findings of this investigation will help develop improved models based on actuator lines (Actuator Line Method) for the prediction of VATs performance.
