As modern warfare shifts towards electronic warfare and Intelligence, Surveillance, and Reconnaissance (ISR), there is an increasing demand for stabilized gimbal systems mounted on tracked vehicles to operate reliably in harsh combat environments. However, high-frequency disturbances generated by the vehicle can degrade the quality of the imagery produced by these systems. To mitigate image blur caused by vibration, elastomeric isolators are used; yet, their nonlinear behavior under shock and vibration makes performance prediction challenging. This study aims to model the nonlinear dynamics of these isolators and identify the optimal configuration. Numerical simulations were conducted to pinpoint leading candidate isolators, which were then tested through operational vibration and shock assessments on four isolators with varying stiffness. The selected isolator achieved vibration attenuation of 83% on the X-axis and 72% on the Y-axis. It also met a safety margin of 1.54 for the image sensor and demonstrated durability through endurance testing.

Multi-Agent Path Finding (MAPF) is an algorithm designed to identify collision-free paths for multiple agents, commonly used in fields like robotics and drone navigation. Conflict-Based Search with Continuous Time (CCBS) is particularly beneficial for real-world applications due to its capability to find paths in continuous time; however, it often experiences lengthy computation times. Although techniques such as prioritizing conflicts (PC), disjoint splitting (DS), and high-level heuristics have been implemented to reduce these times, challenges remain. To address these issues, this paper introduces methods to improve space utilization by calculating agent congestion. By optimizing space usage, we can identify paths that avoid potential collisions, even when those paths share the same cost. We propose enhancements to high-level heuristics, conflict prioritization, and low-level heuristics, as well as a method for calculating congestion in continuous time. These improvements lead to a reduction in agent collisions and a decrease in high-level expansions, resulting in a 30% increase in computational success rates compared to the existing CCBS. Incorporating space utilization into the search process significantly enhances MAPF performance.

This study assessed the accuracy and reliability of a 2D image-based deep learning algorithm for posture analysis by comparing it with a 3D motion capture system. Twenty healthy adult males participated, and nine balance parameters were measured using both methods: body tilt (ML/AP), shoulder tilt, pelvis tilt (ML/AP), knee tilt, left/right varus/valgus, and forward head posture. We evaluated agreement and reliability using root mean square error (RMSE), mean absolute error (MAE), Pearson correlation coefficients, and intraclass correlation coefficients (ICC). Most parameters exhibited RMSE and MAE within 3°, while forward head posture, pelvis tilt (AP), and varus/valgus had errors below 10°. High correlations were found for shoulder tilt (r = 0.886) and forward head posture (r = 0.681), whereas knee tilt and left varus/valgus showed lower correlations due to methodological differences. Both methods demonstrated high repeatability (3D: ICC > 0.90, 2D: ICC > 0.80), with moderate-to-high agreement between methods (ICC ≥ 0.5 for most parameters). Shoulder tilt (ICC = 0.919) and forward head posture (ICC = 0.799) showed particularly high agreement. These findings indicate that 2D image-based posture analysis can provide accurate and reliable assessments comparable to 3D motion capture, presenting a more accessible and cost-effective alternative for posture evaluation in clinical and research contexts.

This study examines the porosity behavior during the directed energy deposition (DED) of dissimilar metals S45C and H13. We analyzed the effects of deposition parameters, including laser power, feed rate, and powder characteristics, on pore formation, taking into account the unique properties of these metals. Our findings indicate that laser power is the primary factor influencing porosity. At a low power of 200 W, insufficient energy input, along with differences in thermal conductivity and chemical composition between S45C and H13, led to incomplete melting and lack-of-fusion, resulting in high porosity. As the laser power increased to 400-600 W, the melt pool stabilized, enhancing interfacial bonding and significantly reducing porosity. However, at an excessive power of 800 W, rapid melting and solidification of the powder caused gas entrapment and pore formation, which increased porosity, particularly due to the differing thermal conductivities of S45C and H13. Therefore, our results suggest that maintaining an adequate laser power of 400-600 W is essential for achieving a stable melt pool and minimizing porosity in the DED process for dissimilar S45C and H13 metals.

This paper examines the role of generative AI and large language models (LLMs) in advancing intelligent manufacturing as we transition from Industry 4.0 to Industry 5.0. We begin by analyzing the current limitations of rule-based and manufacturing data systems in facilitating flexible, human-centric production. Next, we categorize LLM utilization strategies into three methodological axes: fine-tuning domain-specific models, employing general-purpose models through prompt engineering, and utilizing retrieval-augmented generation (RAG), which includes multimodal RAG that integrates sensor and text data. For each strategy, we present representative case studies across key application areas such as asset management, maintenance intelligence, quality control, process optimization, and knowledge- and document-centric support systems. Concurrently, we explore how information modeling and ontology-based knowledge graphs can be integrated with LLMs to enhance structured manufacturing semantics, improve source traceability, and minimize hallucinations. Finally, we summarize the advantages and limitations of each approach and propose future research directions for human-centric manufacturing, including the development of trustworthy LLM pipelines, standardized data schemas, and closer integration between digital twins and LLM-based decision support systems.

This study investigated the influence of inlet velocity on the internal flow characteristics and particle separation performance of a cyclone separator. Computational Fluid Dynamics (CFD) coupled with the Discrete Phase Model (DPM) was used to predict particle trajectories and separation efficiencies under different velocity conditions. The results show that increasing the inlet velocity intensifies the swirling flow and strengthens the centrifugal force within the cyclone. As a result, the axial velocity distribution becomes more pronounced, with stronger downward flow near the wall and intensified upward reverse flow at the center. In the bottom outlet region (Z = 4.5D), clear flow asymmetry associated with the Precessing Vortex Core (PVC) effect is observed, and this phenomenon becomes more pronounced as the inlet velocity increases. Particle trajectory analysis indicates that higher velocities shorten particle residence time and promote rapid migration toward the wall, forming compact helical paths and improving separation efficiency. Analysis using an inverse weighted-sum performance index indicates that an inlet velocity of 15 m/s provides the most favorable balance among the evaluated performance parameters and represents the optimal operating condition for cyclone separator performance.

This study presents a rolling tribometer designed to quantitatively assess ball-raceway friction in ball-guided bearings, which is critical for applications such as smartphone camera actuators, where friction impacts power consumption. Following ASTM G133 standards, the tribometer was validated using LCP and PC materials under both short-cycle (10K cycles) and long-cycle (1M cycles) tests. Under short-cycle conditions, LCP exhibited an average coefficient of friction (COF) of 0.011, while PC demonstrated a COF of 0.009, both showing low variability at 2.7% and 4.4%, respectively. In long-cycle testing, LCP maintained stable friction coefficients, whereas PC experienced a significant COF increase around 200K cycles due to wear. Confocal microscopy revealed that the wear volume of PC was approximately 10 times greater than that of LCP after 1M cycles. Displacement-friction force analysis indicated increased energy dissipation in PC, attributed to wear-induced surface asperities. This rolling tribometer provides a reliable method for evaluating friction coefficients and long-term durability, yielding valuable data for optimizing actuator design and enhancing efficiency and lifespan in ball-guided mechanisms. The quantitative friction data generated can significantly improve the performance of ball-guided systems.

Inertial navigation technology originally designed for precise guidance of missiles is widely used in weapon systems. Guided missiles have become supersonic and high maneuverability with advancement of science and technology. Antivibration performance against high vibration and shock energy is accordingly required. Sensors of an Inertial Navigation System (INS) have a high sensitivity. Conversion coefficients for acceleration values and bias errors in signals must be minimized. A vibration isolator is generally applied to protect INS by attenuating the vibration and shock energy transmitted from dynamic disturbances. The stiffness and damping are changed using highly damped materials such as elastomers that must be protected from disturbances. A vibration isolator is widely used in various fields. However, it is important to understand characteristics of a vibration isolator composed of elastomer because it has nonlinearities such as hyperelasticity and viscoelastic as well as damping characteristics. In this study, a COTS vibration isolator suitable for INS was selected through theoretical approach. Response characteristics of the system in a vibration and shock environment were analyzed through FEM analysis and vibration and shock test. In addition, through repeated excitation test, reproducibility and structural stability were confirmed when the vibration isolator was installed in the system.

Digital twin technologies in manufacturing have evolved into dynamic, data-synchronized systems that facilitate real-time monitoring and control. Given that machining involves closely interconnected multi-physics behaviors, the effectiveness of a digital twin largely relies on the accuracy and reliability of its underlying process models. This review systematically evaluates three primary paradigms for machining process modeling in digital twins: physics-based, data-driven, and hybrid approaches. Physics-based models provide interpretability and physical consistency but are hindered by high computational costs and limited adaptability to changing conditions. In contrast, data-driven models offer real-time capabilities and adaptive learning but face challenges related to data scarcity and black-box behavior. Hybrid modeling has emerged as the most promising approach, combining physical laws with machine learning through techniques such as parameter correction, physics-guided learning, and state-estimation-based intelligent control. Recent research demonstrates significant advancements in predictive performance, adaptability, and computational efficiency across various machining applications, underscoring the effectiveness of new process modeling strategies for digital twins. However, challenges remain, including multi-physics integration, model reduction for real-time deployment, and autonomous self-updating in data-limited scenarios. The review concludes that hybrid models present the most viable pathway to achieving high-fidelity, self-adaptive, and trustworthy digital twins for autonomous manufacturing.

This study examines a 2kW photovoltaic (PV) support structure, highlighting the vulnerability of conventional metal frames to corrosion and strength degradation in harsh environmental conditions. To overcome these challenges, we propose using pultruded fiber-reinforced polymer (PFRP) members as an alternative structural material. An optimal design framework is established to identify efficient PFRP cross-sections. The study aims to determine lightweight cross-sectional dimensions for box sections (columns and girders) and C-sections (purlins) while maintaining structural safety. We evaluate structural performance using the allowable stress design (ASD) method, incorporating safety factors recommended by the American Association of State Highway and Transportation Officials (AASHTO). Finite element analysis (FEA) assesses critical design constraints, including buckling, material failure, and serviceability deflection limits. From the feasible designs, we select the lightest cross-sectional configuration that meets all safety requirements. The results demonstrate that PFRP members can significantly reduce weight while ensuring structural safety, thus validating their potential as an alternative to conventional metal photovoltaic support structures.

Alcohol acts as a central nervous system depressant and is classified as a psychoactive drug that impairs cognitive alertness and motor coordination. Driving after alcohol consumption slows reaction time in emergency situations and increases the risk of collisions. Although various technologies have been developed to measure alcohol concentration, many suffer from limitations such as sensitivity to environmental factors (e.g., temperature and humidity), hygiene concerns, and the need for periodic calibration. This study proposes an optical method for measuring alcohol concentration using near-infrared (NIR) spectroscopy. Statistical analyses were conducted across multiple wavelength regions to identify wavelengths with significant correlations to alcohol concentration. As a preliminary step, single alcohol solution samples were prepared using distilled water and ethanol. The optical properties of the samples were analyzed in the NIR wavelength range, and statistical indicators including the coefficient of determination (R²), p-value, and coefficient of variation (CV) were evaluated. The results identified specific wavelengths with statistical significance, and the application of mathematical modeling at these wavelengths enabled accurate estimation of alcohol concentration. This approach demonstrates the potential for non-invasive alcohol concentration measurement while minimizing hygiene and infection-related concerns for users.

Flexible electronics are becoming the next generation of devices due to their advantages, such as mechanical flexibility, eco-friendliness, large-area applicability, and scalability for mass production. However, solution-based manufacturing processes are prone to defects like discontinuities and local smudging, which can significantly degrade both device quality and yield. To tackle these challenges, rapid and accurate defect classification is crucial for real-time diagnosis during manufacturing. This study investigates the impact of data scale and key training hyperparameters on the performance of deep learning–based defect diagnosis models, using a dataset of conductive pattern defects in flexible electronics. We specifically examine how the number of training images affects model accuracy and generalization, and we analyze how adjustments to hyperparameters—such as L2 regularization and dropout—influence model performance in data-limited scenarios. Our findings offer insights into optimal training strategies tailored to different data scales and learning constraints, providing practical guidelines for designing and developing AI-based defect diagnosis models for flexible electronic devices.

Here in, a high-quality automotive camera lens was developed based on an ultra-precision diamond turning core and cyclic olefin polymer (COP) injection molding process. To improve surface roughness and achieve the accuracy of plastic injection molding lens, systematic mold core machining process was developed and demonstrated using the diamond turning machine. The cutting tool path was generated by using NanoCAM 2D, and it was partly revised to prevent interference between the cutting tool and the workpiece. After the initial machining using the generated tool path, the compensation-cutting process was conducted based on the measured surface profile of an initially machined surface. After two times of compensation machining, the fabricated core mold showed a shape error of 100 nm between peak to valley (PV) and Arithmetic mean roughness (Ra) of 3.9 nm. The performance of the fabricated core was evaluated using an injection molding test. Injection molded aspheric plastic lens showed contrasts that were higher than 55% at 0.0 F, 30% at 0.3 F, and 20% at 0.7 F without any moiré phenomenon that meets the specification for automotive vision module with 1MP and 140° field of view.

This paper presents a tiltable cable-suspended aerial manipulation (SAM) system designed to improve the utility of aerial manipulators in industrial settings. Although drone-robot arm systems have shown promise, suspended configurations encounter notable stability challenges, particularly during inclined operations. To tackle these challenges, we performed simulation-based analyses focusing on the system's kinematics, dynamic response, and thrust requirements under tilted conditions. We utilized Monte Carlo sampling and forward kinematics to assess the workspace and manipulability. The findings indicated that each propeller needs to generate over 32 N of thrust to maintain stable control. Additionally, simulation experiments showed that the system can uphold its attitude and execute end-effector motions effectively, even in the presence of disturbances. This study establishes a foundational verification step toward developing a physical SAM system capable of safe and robust operation in inclined scenarios.