International Core Journal of Engineering
International Core Journal of Engineering is an international journal to promote all fields of Engineering, it considers original and timely contributions containing new results in mechanical engineering, electrical engineering, civil engineering, chemical engineering, etc. All articles published are rigorously and fast reviewed meeting the Journal Quality standards.
Course Design and Implementation of Fault Diagnosis for New Energy Vehicle Power Batteries based on Deep Learning
This paper focuses on the design and implementation of the course on fault diagnosis of power batteries for new energy vehicles, exploring the process of integrating deep learning technology with power battery fault diagnosis and transforming it into teaching content. It plans the teaching module of the diagnostic model, introduces the teaching methods and case implementation paths, and analyzes the teaching effect evaluation and the direction of course optimization. It clarifies the course positioning goals, enabling students to master the principles, fault modes, and model applications, and cultivate problem-solving abilities; it builds a content system centered on data, algorithms, etc., integrating knowledge from multiple disciplines; it designs a theory-practice balanced plan, plans progressive cases and projects, and builds a skill chain; it proposes diversified teaching assessment and resource construction ideas, establishes an assessment system, and discusses the construction of a hybrid experimental platform. The innovative features of the course lie in focusing on technology integration, emphasizing engineering practice, solving industrial problems, filling teaching gaps, providing a knowledge system, and delivering compound talents to support industrial development.
Deep Learning; New Energy Vehicles; Power Batteries; Fault Diagnosis; Course Design.
Copyright (c) 2026 International Core Journal of Engineering https://creativecommons.org/licenses/by-nc/4.0
Alignment Study of Oral Cavity Point Cloud based on Improved Deep-interaction Transformer
In digital oral treatment, the accuracy of point cloud registration has a significant impact on treatment outcomes. To address the limitations of traditional point cloud registration methods when dealing with sparse and partially missing point clouds, an improvement based on the Deep Interaction Transformer (DIT) model is proposed. This study introduces new loss functions and an oral point cloud self-attention module to enhance the registration accuracy of single-frame oral point clouds.The proposed loss functions include symmetry loss of dental curves, smoothness loss of dental surfaces, and contour consistency loss, each optimized to cater to the geometric characteristics of oral point clouds. Additionally, the designed self-attention module, named Oral Point Cloud Self-Attention (OPCSA), enables more refined information interaction through point-wise calculation of queries, keys, and values, thereby enhancing feature extraction capabilities and robustness.Experimental results demonstrate that the improved DIT model significantly outperforms the baseline model in tasks of single-frame point cloud registration for oral and dental models. Not only does this research provide a new solution for high-precision registration of oral point clouds, but it also shows promising potential in terms of methodological effectiveness and application feasibility. The approach sets a foundation for future advancements in digital dental treatment and point cloud processing technologies.
Oral Medicine; Point Cloud Registration; Deep Learning; Attention Mechanism.
Trustworthy Traceability for Logistics Chains Using Latent Fingerprints and Quality-Aware Score Fusion
Modern logistics chains involve multiple handover stages, during which evidence can be easily lost or tampered with, making responsibility tracing difficult. To address this issue, this paper proposes a quality-aware trustworthy evidence preservation and provenance method based on latent fingerprints. The method constructs an event-driven evidence package for each logistics event and adopts a hybrid storage architecture: event metadata and evidence digests are recorded on-chain, while latent fingerprint data and auxiliary materials are encrypted and stored off-chain, thereby forming a verifiable evidence chain. In addition, a quality-aware mechanism fuses latent fingerprint quality indicators with matching scores to improve evidence usability. We treat latent fingerprint usability as part of the provenance record by storing a quality-fused credibility score to support dispute investigation. Experimental results show that the proposed quality-fusion strategy outperforms a matcher-only baseline in verification performance. In the overhead evaluation, the scheme achieves higher write efficiency and lower on-chain storage footprint under typical workloads, meeting the requirements for continuous event recording and rapid trace-back in logistics operations. Moreover, the proposed method can effectively detect tampering attacks, thereby ensuring evidence integrity and enabling trustworthy traceability in logistics scenarios. Overall, the proposed approach provides a technical path that balances usability, trustworthiness, and compliance for logistics dispute evidence collection and responsibility attribution.
Latent Fingerprint; Blockchain; Traceability; Trustable Digital Evidence.
Real-Time Acquisition Circuit for Irregular Flexible Pressure Sensor Arrays
Irregular flexible pressure sensor arrays face critical bottlenecks of poor layout adaptability, low scanning efficiency, and high implementation cost in dynamic pressure monitoring. To address these challenges, this paper proposes a low-cost, high-real-time data acquisition system based on STM32microcontroller.For hardware, a configurable row-column addressing circuit withCD4052/CD4051analog multiplexers is designed to match compact irregular arrays, enabling0~5000g wide-range pressure sensing. For algorithm, a novel"1+4"fast scanning strategy is developed to optimize dynamic pressure detection, delivering51%higher efficiency than traditional full-traversal scanning. A Qt multi-threaded host computer is built for10Hz real-time heatmap visualization. Experimental results demonstrate that the system achieves±0.5N measurement accuracy and ≤50ms end-to-end latency under72MHz clock and 12-bit ADC configuration. This work fills the technical gap in low-cost real-time monitoring for irregular flexible arrays, and provides a reliable solution for smart medical mattresses and industrial robotic tactile perception.
Irregular Flexible Sensor Array; Fast Scanning Algorithm; Microcontroller; Host Computer Serial Communication.
Study on Fracture Initiation Mechanism of Plane Perforation
Aiming at the engineering problems of high fracture pressure, great difficulty in fracturing reconstruction, poor fracture propagation effect of conventional perforation technology and high risk of casing damage in deep shale oil reservoirs, this paper systematically studies the hydraulic fracture initiation mechanism under spiral perforation, fixed-plane perforation and plane perforation technologies by combining theoretical analysis, numerical simulation and physical simulation. Based on linear elastic fracture mechanics, a mechanical model for composite fracture initiation at perforation tips was established. The finite element method was used to analyze the influence laws of key parameters such as perforation phase angle and hole density on fracture initiation pressure, propagation morphology and casing damage. The reliability of numerical simulation results was verified by true triaxial physical model experiments, and finally the optimal process parameter scheme for plane perforation was formed. The research results show that the plane perforation technology is significantly superior to spiral perforation and fixed-plane perforation in reducing formation fracture pressure and improving fracture propagation effect; considering the fracturing effect and casing safety factor comprehensively, the optimal process parameters of plane perforation are 16 holes per meter of hole density and 30° of phase angle. Under these parameters, the formation initiation pressure is as low as 61.2 MPa, the maximum fracture width reaches 5.27 mm, and the casing Mises stress is only 551 MPa, which meets the wellbore integrity requirements. The research results can provide theoretical support and on-site guidance for the optimal design of perforation and fracturing in deep shale oil reservoirs.
Plane Perforation; Hydraulic Fracture Initiation; Perforation Parameter Optimization; Casing Damage; Numerical Simulation.
Thermal-mechanical Coupling Analysis of Split Mechanical Seals
In order to study the effects of frictional heat and medium pressure on the deformation of split mechanical seals, thermal-mechanical coupling numerical analysis of split mechanical seals was carried out. The results show that: in the temperature field, the temperature on the outer diameter side of the seal ring is lower, the highest temperature of the end face of the split dynamic ring appears at the seal end face that is in contact with the split static ring, and the highest temperature of the end face of the split static ring appears at the inner diameter of the seal end face ;Under the action of heat and force coupling, the sealing end face forms a convergent gap from the deformation of the outer diameter to the inner diameter; as the medium pressure increases, the axial deformation of the sealing ring increases, and the radial deformation of the sealing ring parting surface decreases; The change of medium pressure leads to the difference of frictional heat generated between the end faces of the sealing ring, which causes the difference of the deformation of the sealing ring.
Split Mechanical Seal; Thermomechanical Coupling; Parting Surface.
A Virtual Try-On Method based on Enhanced Feature Representation and Global Attention
Virtual try-on (VTON) synthesizes realistic images by mapping a target garment onto a person while preserving structural alignment and texture fidelity. Existing methods often struggle with complex garment deformations and fine-grained details, causing artifacts such as distortions and texture loss. To address these challenges, we propose EAG-VTON, a framework combining enhanced feature representation and global attention. Specifically, we introduce an Enhanced Appearance Flow Warping Module (EAFWM) that integrates pre-activation residual blocks and an enhanced semantic-adaptive normalization (E-SPADE) to improve garment deformation accuracy. For image synthesis, a Residual Generator with Global Attention (RGC) combines ResNetV2 blocks with a Global Grouped Coordinate Attention (GGCA) module to capture long-range dependencies and preserve structural consistency. Experiments on the VITON-HD dataset show that EAG-VTON outperforms state-of-the-art baselines in SSIM, LPIPS, and FID, demonstrating superior structural fidelity and realistic texture reconstruction.
Virtual Try-On; Garment Warping; Global Attention; Image Synthesis.
Investigation of the Laser Processing Mechanism of Tooth-Surface Texturing in PEEK-Based Involute Splines
To reveal the response behavior and formation mechanism of femtosecond laser-induced microtextures on the tooth surfaces of PEEK-based involute splines, this study selected three materials, namely PEEK5600G, PEEK5600LF30, and PEEK5600CF30. An orthogonal experimental design was adopted to systematically investigate the effects of laser average power, scanning speed, and number of scans on texture depth and width. The processed surfaces were characterized in terms of three-dimensional morphology using a DSX510 digital microscope. The experimental data were further processed, verified, and statistically analyzed using MATLAB and Origin. The results showed that the texture depth of all three materials was mainly governed by laser power. However, the three materials exhibited different response characteristics. For PEEK5600G, the texture depth was significantly affected by both laser power and scanning speed. For PEEK5600CF30, the texture depth was mainly controlled by laser power. In contrast, PEEK5600LF30 showed high sensitivity to laser power, scanning speed, and number of scans. The variation in texture width showed clear material dependence. For PEEK5600G, the texture width was mainly affected by laser power. For PEEK5600CF30, it was mainly influenced by scanning speed and number of scans. PEEK5600LF30 was sensitive to all three factors. Meanwhile, microscopic morphology analysis further showed that all three PEEK-based materials exhibited a competing material removal mechanism of ablation and melting under femtosecond laser irradiation. Ablation dominated in the central region of the groove, whereas a recast layer and molten material accumulation tended to form at the edges. Differences in the reinforcing components led to different thermophysical response characteristics, which ultimately resulted in significant differences in groove profile, edge quality, and parameter sensitivity.
Involute Spline; PEEK-based Materials; Femtosecond Laser; Surface Microtexture.
Key Issues and Modification Strategies for Layered Oxide Cathode Materials in Sodium-Ion Batteries
Sodium-ion batteries, with their advantages of abundant sodium resources, low cost, and excellent safety performance, represent a potential alternative to lithium-ion batteries for large-scale grid energy storage. The cathode material plays a crucial role in determining the battery’s energy density, cycle life, and cost. Layered transition metal oxides, due to their well-ordered layered structure, good electrical conductivity, and two-dimensional ion transport pathways, are highly promising cathode materials for sodium-ion batteries. However, their practical application is hindered by various challenges, including irreversible phase transitions, high sensitivity to air, loss of lattice oxygen under high pressure, local structural distortions, and slow sodium ion diffusion, all of which significantly impede their commercialization. This paper systematically reviews the research progress of layered oxide cathodes for sodium-ion batteries, analyzes the key challenges faced in their application, outlines effective modification strategies such as element doping, surface coating, and structural design, and discusses future research directions. The aim is to provide theoretical insights and technical support for the development and industrialization of high-performance sodium-ion battery cathode materials.
Sodium-ion Batteries; Cathode Materials; Layered Oxides; Modification.
Molecular Dynamics Simulation of Nanoparticle Collaborating to Stabilize Carbon Dioxide Foam
CO2 foam fracturing has been widely adopted in unconventional oil and gas exploration and development due to its low water consumption and effective reservoir enhancement. To investigate the impact of acidic environments caused by CO2 dissolution on foam stability, this study selected two common surfactants: Octadearyl dimethyl ammonium chloride (OTAC), α-olefin sulfonic acid (AOS), and nano-silica SiO2 particle. Using Materials Studio software to construct a foam liquid film model, this study investigates the compatibility and foam stabilization effects of cationic and anionic surfactants with nano-SiO2 particles by comparing the diffusion behavior of water molecules in neutral versus acidic environments across different systems. Revealing the Mechanism of Acidic Environment on Synergistic Effect at Molecular Level. These findings provide theoretical foundations for optimizing CO2 foam fracturing fluid formulations.
Carbon Dioxide Foam; Nanoparticles; Surfactants; Molecular Dynamics Simulation.
Research Review on Photovoltaic Monitoring Data Processing Methods
Photovoltaic power stations are subject to variable weather phenomena and equipment operational conditions during operation, resulting in a significant number of outliers and missing values in photovoltaic monitoring data. This poses substantial challenges for photovoltaic data analysis and evaluation, photovoltaic power forecasting, and photovoltaic equipment status analysis. This paper systematically outlines the fundamental principles and typical methods for anomaly detection and missing value imputation in PV monitoring data processing. It summarizes the current technical challenges and limitations in short-term PV monitoring data processing and provides insights into the key areas and research directions for future PV monitoring data processing methods.
Photovoltaics; Data Processing; Missing Value Imputation; Feature Analysis.
Research on Low-Carbon Operation and Maintenance and Fire Emergency Coordination Management for Commercial Buildings in the Jidong(Eastern Hebel) Region based on BIM Technology
Under the dual imperatives of achieving carbon peaking and carbon neutrality alongside ensuring construction safety, commercial buildings in the eastern Hebei region face industry-wide challenges: fragmented low-carbon operations and maintenance (O&M) from fire emergency management, coupled with outdated management practices. As the core city in eastern Hebei, Tangshan's commercial buildings exemplify regional characteristics in climate conditions, functional layouts, and O&M models. This study examines representative commercial buildings in Tangshan, leveraging BIM technology to construct a comprehensive building information model across all disciplines. It adopts a research framework centered on problem identification-Solution Optimization-Collaborative Strategy as its research framework. Through BIM model information extraction, clash detection, and visualization analysis, it systematically identifies critical carbon emission points during building operations and fire emergency management risks. Ultimately, it proposes collaborative management strategies across three levels-scheme, information, and response-providing practical insights and technical references for green and safe management of similar commercial buildings in the Jidong region.
BIM Technology; Commercial Buildings; Low-Carbon Operations and Maintenance; Fire Emergency Management; Collaborative Management.
Molecular Dynamics Simulation Study on the Solubility, Diffusion, and Permeation Characteristics of Hydrogen-Blended Natural Gas in Polyvinyl Chloride
Against the backdrop of carbon neutrality goals, blending hydrogen into natural gas to form hydrogen-hydrocarbon mixed fuels has become a crucial pathway for building a clean energy system. However, hydrogen blending imposes specific requirements on pipeline transportation materials: while metal pipelines face risks of hydrogen-induced cracking, non-metallic pipelines, although avoiding this hazard, exhibit high gas permeability due to their polymeric nature, potentially leading to significant energy losses and safety hazards.Therefore, this study focuses on typical operating conditions of urban gas networks (temperatures of 270–310 K and pressures of 0.1–0.7 MPa). Using molecular dynamics simulations combined with Grand Canonical Monte Carlo methods, we systematically investigated the solubility, diffusion, and permeation characteristics of hydrogen and methane in polyvinyl chloride (PVC) at a hydrogen blending ratio of 10%.The results indicate that the solubility coefficients of both hydrogen and methane decrease with increasing temperature. In contrast, their diffusion coefficients increase with rising temperature and pressure. The permeability coefficient increases with temperature, primarily because the enhancing effect of the diffusion coefficient outweighs the reducing effect of the solubility coefficient.This study reveals the permeation mechanism of hydrogen-blended natural gas in PVC at the molecular level, providing a theoretical basis for the selection and safety evaluation of non-metallic pipeline materials for transporting hydrogen-blended gas.
Hydrogen-blended Natural Gas; Polyvinyl Chloride (PVC); Permeation Properties; Molecular Dynamics Simulation; Grand Canonical Monte Carlo.
Study on Synergistic Optimization of Tooth Profile and Tooth Number of Toothed Roll Crusher based on EDEM-Tavares Model
The toothed roll crusher is a key crushing equipment in the fields of mining, metallurgy and other industries, and the rationality of its structural parameters directly affects crushing efficiency, energy consumption and equipment service life. To improve the comprehensive performance of the toothed roll crusher, based on the Discrete Element Method (DEM), the EDEM software was used to construct the Tavares crushing model and Relative wear model for the simulation and optimization research on two key parameters: tooth profile and tooth number. Firstly, in accordance with the process requirements such as output and particle size, the structural parameters of the toothed roll were designed, with a roller diameter of 800 mm, length of 1200 mm, center distance of 845 mm and pitch of 20 mm. Secondly, through EDEM simulation, the wear characteristics and crushing effects of three tooth profiles (eagle beak type, spear type and trapezoidal type) under the same working conditions were compared systematically. The results show that the eagle beak tooth profile has more uniform force distribution and lower wear, and the particle size of the crushed material is mainly concentrated in the range of 15~21 mm, which meets the discharge requirements. Finally, the simulation of tooth number optimization was carried out for the eagle beak tooth profile with five working conditions set, and the analysis was conducted from the aspects of crushing efficiency and particle size distribution. The simulation results indicate that when the tooth number per ring is 45, the crushing efficiency is the highest, the complete crushing time is the shortest, and the proportion of qualified particle size in the crushed products is the largest. This study provides a theoretical basis and simulation support for the structural optimization of toothed roll crushers, and has certain engineering guiding significance for improving equipment efficiency and reducing wear.
Toothed Roll Crusher; Discrete Element Method; EDEM Simulation; Tavares Model; Tooth Profile Optimization; Tooth Number Optimization; Wear Analysis.
Stochastic Comparisons of Order Statistics from a Sample Following the Power Lomax Distribution
For independent random variables following the Power Lomax distribution in the two-dimensional case: If two independent sets of Power Lomax samples share the same scale parameter, and the matrix formed by their shape parametersαandβsatisfies a specific chain majorization order, the lifetimes of series systems can be compared under the hazard rate order. Under the same conditions, the lifetimes of parallel systems can be compared under the reverse hazard rate order. These results imply that, under specific constraints, we can systematically evaluate the lifetime properties of different system architectures.
Order Statistics; Power Lomax Distribution; Hazard Rate Order; Reverse Hazard Rate Orde.
High-Voltage Test Technology and Fault Handling of Power Transformers
High-voltage tests are critical in the handover and maintenance of substations, which can detect the insulation strength of transformers, identify potential faults, and ensure the normal operation and safety of transformers. High-voltage test technology for power transformers refers to testing power transformers under certain high voltage to verify whether their insulation performance meets requirements, discover hidden troubles, and take corresponding treatment measures. This technology can effectively guarantee the quality and reliability of power transformers and improve their operation efficiency and safety. This paper analyzes and discusses high-voltage test technology and fault handling of power transformers.
Power Transformer; High-Voltage Test; Insulation Performance; Fault Handling; Operation Efficiency.
Design of a Discharge Output Assembly for Blasting Controllers with Automatic High-Voltage Detection
Aiming at the industrial pain points that traditional blasting control equipment lacks an independent discharge output assembly and multi-channel delay initiators are short of core supporting modules, a discharge output assembly for blasting controllers with automatic high-voltage detection adapted to multi-channel delay initiators was designed. The assembly adopts a modular architecture, consisting of an output voltage circuit, a comparison and detection circuit, a dual-channel assembly interface and a discharge control unit, and is powered by a lithium battery. It can realize accurate automatic detection and real-time feedback of the discharge voltage of high-voltage capacitors as well as precise control of discharge on-off. This assembly fills the market gap, solves the problems of no voltage detection, no state feedback and poor stability of traditional circuits, and is convenient for production, installation and maintenance. It can significantly improve the discharge stability of blasting controllers, provide core technical support for the marketization of multi-channel delay initiators, and has prominent engineering application value.
Blasting Controller; Discharge Output Assembly; Automatic High-Voltage Detection; Modular Design; Multi-Channel Delay Initiator; High-Voltage Capacitor.
Research on the Coupling Mechanism of Multi-Hazards and the "Five-in-One" Collaborative Prevention and Control Technology in the Syncline Axis Working Face
The syncline axis area, influenced by the convergence of water in structurally low-lying zones, enhanced formation sealing properties, and the superimposed effects of stress concentration and mining disturbance, is prone to the co-enrichment and chain amplification of multiple hazards within the same region, including water disasters, fires, goaf hazards, and toxic and harmful gases such as gas and hydrogen sulfide. Based on the 9401 working face of Hengsheng Coal Industry, this study integrates structural control, mining-induced fracture evolution, and the reconstruction characteristics of seepage and ventilation flow fields to reveal the coupling mechanism of multi-hazards in the syncline axis. A "Five-in-One" closed-loop collaborative prevention and control system, encompassing "Prediction-Early Warning-Precontrol-Prevention and Control-Collaboration," has been established. Field applications demonstrate that during the mining process through the syncline axis, the gas environment in the goaf remained stable, water inflow was relatively low, and the impact of toxic and harmful gases was minimal, ensuring safe extraction in the complex section of the syncline axis.
Syncline Axis; Multi-Hazard Coupling; Tube Bundle Monitoring; Collaborative Prevention and Control.
Research Progress on the Application of Solid Waste-Based Auxiliary Cementitious Materials in Concrete
This paper provides a systematic review of research into the use of solid waste-based supplementary cementitious materials in concrete. It focuses on analysing how typical solid waste materials, such as fly ash, slag and steel slag, influence the workability, mechanical properties and durability of concrete when used as supplementary cementitious materials. The paper summarises the mechanisms of action of these materials and identifies current research challenges, outlining future research directions. The review aims to inform the utilisation of solid waste resources and the green development of high-performance concrete.
Solid Waste Utilization; Supplementary Cementitious Materials; Concrete; Mechanical Properties; Durability.
Trajectory Optimization of Industrial Welding Robots based on Artificial Hummingbird Algorithm
In industrial welding scenarios, the smoothness of welding trajectories, uniformity of energy distribution, and control of thermal deformation directly determine weld quality and production efficiency. Traditional trajectory planning algorithms struggle to balance robustness, real-time performance, and executability under multi-objective constraints, exhibiting problems such as unstable convergence and a tendency to fall into local optima. This paper proposes the application of the Artificial Hummingbird Algorithm (AHA) and its improved variants to the trajectory optimization of industrial welding robots, constructing a multi-objective optimization framework to achieve closed-loop optimization of weld feature perception - trajectory generation - execution. By introducing chaos mapping in the population initialization phase, the global search capability and convergence stability of the algorithm are enhanced. A simulation model is built based on the Matlab platform, combining visual perception and robot kinematic models to model and optimize high-dimensional multi-objective constraints (smoothness, heat input, energy distribution, etc.). The research results show that the improved Artificial Hummingbird Algorithm can effectively enhance the smoothness of welding trajectories and the uniformity of energy distribution, reduce thermal deformation and residual stress, improve the consistency of weld quality and production efficiency, and provide an efficient and concise trajectory optimization scheme for industrial welding in complex environments.
Artificial Hummingbird Algorithm; Industrial Welding Robot; Trajectory Optimization; Multi-objective constraint; Chaos Mapping.
Research Progress on Warm-Mix and Odor-Reduction Technologies for CR/SBS Composite Modified Asphalt
SBS-modified asphalt has been widely used in the construction of high-grade highways because of its excellent high- and low-temperature performance, fatigue resistance, and durability. However, SBS-modified asphalt still suffers from problems such as relatively high cost and insufficient storage stability. Crumb rubber (CR)-modified asphalt can realize the resource utilization of solid waste and shows certain advantages in improving asphalt elasticity and high-temperature performance, but it also has shortcomings such as poor high-temperature storage stability, high construction viscosity, and susceptibility to phase separation. To simultaneously satisfy the requirements of pavement performance, storage stability, and resource utilization, CR/SBS composite modification has gradually become a research focus. Existing studies have shown that the composite modification of CR and SBS can significantly improve the high-temperature rutting resistance, low-temperature cracking resistance, and fatigue performance of asphalt and asphalt mixtures through the synergistic effects of rubber elasticity and polymer network structure. However, it also leads to problems such as increased system viscosity, elevated construction temperature, and increased emissions of volatile organic compounds (VOCs). Warm-mix technology can reduce energy consumption and emissions by lowering construction temperature, while odor-reduction technology can suppress the release of asphalt fumes and hazardous volatile substances at the source. This paper systematically reviews the domestic and international research status of CR/SBS composite modified asphalt, warm-mix technology, and asphalt odor-reduction technology, with emphasis on the performance enhancement mechanisms of multicomponent composite systems, VOC emission reduction pathways, and existing key issues. The prospects for the synergistic application of warm-mix additives and odor-reducing agents in CR/SBS composite modified systems are also discussed, with the aim of providing a reference for the development and engineering application of environmentally friendly, high-performance composite modified asphalt materials.
CR/SBS Composite Modified Asphalt; Warm-Mix Technology; Odor-Reduction Technology; VOCs; Fume Emission Reduction.
Optimization Design of Machine Tool Beam based on Orthogonal Experiment
To enhance the natural frequency of the machine tool beam system and ensure its structural performance, it is crucial to achieve a rational layout of rib structural characteristics. This study utilized the 3D design software SolidWorks for the construction of a three-dimensional model of the machine tool beam, followed by comprehensive modal analysis using Ansys finite element analysis software. In assessing the design merits of the machine tool beam, this research selected the natural frequency as the key objective function and considered initial design variables such as the number of rib segments, rib thickness, rib width, and intermediate span distance for systematic multi-objective optimization design. To determine the optimal combination of design variables, orthogonal experimental design was employed, comprising four factors and three levels. Through rigorous data analysis methods, critical factors influencing beam performance and their optimal levels were identified, thereby establishing an optimal experimental simulation scheme. Subsequent to obtaining the optimal solution, this study further developed a response surface regression model aimed at achieving lightweight design while maintaining static and dynamic beam performance, thereby balancing quality and performance. By employing this research methodology, the study not only provides a scientific basis for optimizing the design of machine tool beams but also offers valuable insights for research and practice in related fields. Results indicate that the optimal configuration-"outer wall thickness of 48mm, rib thickness of 36mm, rib width of 37mm, 9 segments, and intermediate support distance of 690mm"-improved the first-order natural frequency by 23% compared to the original design. Compared to the optimized data post orthogonal experiments, the mass was reduced by 3.6%. This study expands the application scope of orthogonal experimental design and response surface testing, providing beneficial methodological references for the design of other machine tool components.
Crossing Beam; First Order Frequency; Orthogonal Experimental Method; Response Surface.
Active Disturbance Rejection Controller for Servo System Current Loop based on Linear Extended State Observer
This paper first introduces the advantages of the active disturbance rejection controller and the standard active disturbance rejection controller model. The active disturbance rejection controller is applied to the current loop of the permanent magnet AC servo control system, and a current loop active disturbance rejection controller composed of a linear extended state observer and a state error feedback rate is designed. It is introduced into the current loop of the servo system for performance analysis. Secondly, in terms of dynamic response, improving the current feedback and updating the PWM duty cycle can enhance the dynamic performance. Finally, the anti-interference performance of the current loop in the AC servo system was analyzed. The experimental results verified the robustness of the new servo system current loop active disturbance rejection controller to motor parameter perturbation.
Current Loop Active Disturbance Rejection Controller; Linear Extended State Observer; State Error Feedback Rate; Parameter Perturbation.
Wind Turbine Blade Defect Detection Method based on Improved YOLOv11
Aiming at the problems of low efficiency of manual inspection, easy missed detection of small target defects, and severe interference from complex backgrounds in wind turbine blade defect detection for wind power operation and maintenance scenarios, an improved YOLOv11 model integrating feature enhancement and dynamic sample optimization is proposed. Firstly, the local perception unit of images is constructed through superpixel texture analysis, and the texture entropy of each region is quantified combined with the Gray Level Co-occurrence Matrix (GLCM) to realize the adaptive localization of potential defect regions of blades. Secondly, the Coordinate Attention (CA) module is embedded in the backbone network to dynamically generate spatial weight masks, which strengthens the network's focusing ability on high-entropy defect regions and suppresses the artifact interference caused by complex blade textures and field environments. Meanwhile, the weighted Bidirectional Feature Pyramid Network (BiFPN) is designed to replace the original multi-scale fusion structure, which optimizes the feature aggregation process and enhances the edge consistency representation of small-sized defects. In addition, the detector head structure is optimized and the Intersection over Union (IoU) loss function is introduced. Combined with the multi-category focal loss, the penalty coefficient for easy and hard samples is dynamically adjusted according to the distribution of training samples, which alleviates the model bias caused by the long-tail distribution of industrial data and category imbalance. Experimental results show that compared with the original model and mainstream detection models such as Faster-RCNN, SSD and YOLO series, this method achieves better performance in detection speed and accuracy, and effectively improves the detection performance of the system in complex scenarios such as noise interference and low illumination.
Wind Turbine Blade; Defect Detection; YOLOv11; Feature Fusion; Attention Mechanism.
Effect of TiO2 Nanoparticles on Wetting and Friction Wear Properties of Bionic Textured UHMWPE for Nepenthes
To address material adhesion and wear issues of equipment in the food and pharmaceutical fields, pitcher plant’s crescent-shaped inner wall structure was used as the bionic prototype. Textured UHMWPE/TiO₂ composites with TiO₂ mass fractions of 0–20wt% were prepared via solution blending-molding-ultraviolet laser texturing. The regulation mechanism of TiO₂ content on the material’s wettability and tribological properties was investigated. The results show that the blank textured UHMWPE exhibits a water contact angle of 132.5° and a friction coefficient as low as 0.13. After introducing TiO₂, the water contact angle fluctuates in a “decrease-rise-fall” trend, and the hydrophobicity is weakened overall. The friction coefficient increases with the rise of TiO₂ content; the 20wt% group shows a 346.2% increase compared with the blank textured group, and the high-content groups (15–20wt%) present severe fluctuations in friction curves. The wear loss increases significantly with increasing TiO₂ content, and the wear mechanism gradually shifts from adhesive wear to abrasive wear-dominated. This study provides key references for the component optimization of bionic UHMWPE composites.
Pitcher Plant-bionic Texture; TiO₂/UHMWPE Composite; Wettability; Tribological Property.
Investigation of a Microchannel Reactor for the Synthesis of Low Molecular Weight K-Type Perfluoropolyether Methyl Esters
This study demonstrates the synthesis of perfluoropolyether (PFPE) acyl fluoride from hexafluoropropylene oxide (HFPO) using a microchannel reactor. The reaction was conducted under low-temperature conditions with diethylene glycol dimethyl ether as the solvent and tetramethylethylenediamine (TMEDA) as the catalyst. The resulting acyl fluoride was esterified with methanol at room temperature to obtain K-type PFPE methyl ester, and its molecular weight was determined. The polymerization degree of the product was precisely regulated by systematically varying the microchannel length, HFPO flow rate, and reaction temperature. Compared to traditional systems, the microchannel reactor offers enhanced control over reaction parameters and can be seamlessly integrated into continuous production lines, underscoring its strong potential for the scalable synthesis of low-molecular-weight K-type PFPEs.
Hexafluoropropylene Oxide; Microchannel Reactor; Polymerization; Esterification; K-type PFPE Methyl Ester.
Research on Precise Analysis and Location of Server Memory Faults
Current server memory fault diagnosis suffers from low efficiency, insufficient location accuracy, and high misclassification rate, making it difficult to meet the high reliability requirements of high-density computing and big data processing scenarios. This paper sorts out the core types and inducing factors of server memory faults, constructs a multi-dimensional fault analysis index system, embeds a table corresponding to fault characteristics and location methods, and explores the precise fault analysis process and key points of location technology, providing theoretical reference and practical guidance for improving the efficiency and accuracy of server memory fault diagnosis and reducing operational risks.
Server Memory; Fault Analysis; Precise Location; Fault Diagnosis.
Modeling of Feed Energy Consumption in Fused Deposition Modeling Print Head
This paper proposes a mechanism-based energy consumption modeling method for manufacturing processes. It investigates the energy consumption characteristics of the printhead stepping motion during Fused Deposition Modeling (FDM) and constructs a corresponding energy consumption model through experiments. The power variations and energy consumption levels during the printing process of a typical part were tested and evaluated, with results validating the effectiveness of the constructed model and the proposed method.
Fused Deposition Modeling; Mechanism Modeling; The Print Head of 3D Printer.
Research on Load Forecasting based on Optimized Support Vector Machine Model
Accurate prediction of building energy consumption plays a vital role in energy scheduling, conservation, and emission reduction. However, conventional forecasting methods often struggle to capture nonlinear patterns effectively, leading to limited prediction accuracy. Focusing on rural residential buildings in Beijing, this study develops a Support Vector Machine (SVM) model based on measured data, and introduces Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to optimize the model's hyperparameters. The results demonstrate that the traditional SVM model achieves a coefficient of determination (R²) of only 72.3%, with a mean absolute percentage error (MAPE) of 9.27% and a root mean square error (RMSE) of 0.92 kW. After optimization, the GA-SVM and PSO-SVM models significantly outperform the baseline: their R² values increase to 85.6% and 92.7%, respectively, while MAPE is reduced by 3.85 and 6.02 percentage points, and RMSE decreases by 0.39 kW and 0.53 kW, respectively. The findings underscore the effectiveness of optimization algorithms in enhancing SVM-based load forecasting and provide a reliable reference for energy management in similar building contexts.
Support Vector Machine Model; Genetic Algorithm; Particle Swarm Optimization; Load Forecasting.
Three-Dimensional Design of a Rear-Drive Self-Balancing Electric Wheelchair Chassis
This study presents the design of a rear-drive self-balancing electric wheelchair chassis. Based on the characteristics of practical application scenarios and the requirements for medical device products, the overall structure and layout of the chassis were determined. Key components including the chassis structure, drive system, self-balancing linear actuators, and central frame were designed. The overall three-dimensional modeling and virtual assembly were carried out using the 3D modeling software SolidWorks. Mechanical analysis of the chassis frame was performed using the finite element analysis software ANSYS to optimize the structural design. Static and dynamic characteristic analyses of the chassis structure were conducted based on ANSYS. The results indicate that the chassis structure design is reasonable, with no resonance occurring during operation. Under static full-load and uniform-speed full-load conditions, the structural strength meets the design requirements for practical work. Through a combination of theoretical analysis and prototype testing, the various performance characteristics of the self-balancing wheelchair chassis system were verified. The experimental results demonstrate that the chassis possesses good motion control performance, stability, and comfort, satisfying the requirements for practical operation.
Wheelchair; Chassis; ANSYS; Finite Element Analysis; Self-balancing.