As the demand for precision in the manufacturing industry grows, Digital Twin (DT) technology is gaining attention for its potential to enhance equipment performance and process reliability. However, existing research has primarily focused on specific stages of design or operation, leaving a gap in the literature concerning DT models that can be utilized throughout the entire equipment lifecycle. To address this gap, this study proposes a method for developing a DT that employs a consistent Finite Element (FE) model across all phases of the equipment lifecycle. We utilized actual measurement data to ensure high fidelity in the FE model of previous-generation equipment, which we refer to as the Pre-DT. This Pre-DT was instrumental in improving design during the new equipment development phase. Additionally, the DT model was implemented to predict equipment status in real time using the Reduced-Order Model (ROM) method, functioning as a virtual sensor during operation. This approach was applied to the equipment development process, aligned with the asset lifecycle concept of RAMI 4.0, and was tested on an MLCC cutting equipment to validate its effectiveness.

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.

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.

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.

The demand for high-speed processing and big data has accelerated the adoption of three-dimensional integrated circuits (3D ICs), where interposers serve as essential components for chip-to-chip connectivity. However, silicon interposers using the through-silicon via (TSV) technology have structural limitations. As alternatives, glass-based interposers employing the through-glass via (TGV) technology are gaining attention. This study explored the fabrication of via holes in glass substrates using the selective laser etching (SLE) process. A spatial light modulator (SLM) was used to generate donut- shaped bessel beams by inserting an image pattern without relying on phase modulation. The machinability of via holes fabricated with these beams was compared to that of holes formed using phase-modulated beams. Effect of pulse energy on taper angle was also investigated. Hourglass-shaped holes were observed at lower pulse energies. However, taper angles approaching 90° were observed at higher energies, indicating an improved verticality.

The increasing adoption of industrial robot arms in advanced manufacturing has heightened the need for flexible trajectory planning methods that go beyond traditional offline programming (OLP) tools, which are often expensive, proprietary, and limiting. This study introduces an OLP-free pipeline designed to generate robot trajectory data and optimize paths for six-degree-of-freedom (6-DOF) robot arms using discrete reinforcement learning. Initially, five-axis NC code derived from CAD/CAM data is transformed into tool center point (TCP) trajectories through coordinate transformations. An analytical inverse kinematics solver then produces multiple joint solutions for each TCP pose, creating a discrete action space from which the learning agent can select feasible joint configurations along the trajectory. A reward function that considers variations in joint velocity and acceleration, as well as pose error, facilitates the simultaneous optimization of motion smoothness and tracking accuracy. The optimized trajectories are validated using an open-source physics simulator, showing enhanced motion stability, accuracy, and collision safety compared to conventional OLP-based paths. This proposed framework provides a flexible and cost-effective alternative to commercial OLP tools and lays a scalable foundation for future applications in automated and collaborative manufacturing systems.

The semiconductor industry is experiencing significant growth in production scale and investment, driven by rising demand for generative AI, high-performance computing (HPC), high-bandwidth memory (HBM), and high-performance/high-density chips. As a result, precision inspection and yield management at the wafer stage have become critical challenges. Probe cards, essential for verifying a chip's electrical performance, play a vital role in yield management. However, during repetitive inspection processes, probe cards absorb heat from the underlying chuck, leading to probe tip-pad alignment errors that degrade contact reliability and measurement accuracy. This situation necessitates a quantitative evaluation system based on thermo-structural coupled analysis. Additionally, the modeling process for multiple interposers and interposer housings, along with the preprocessing of contact conditions, adds complexity due to the increasing number of contact surfaces. This complexity can result in convergence issues and reduced accuracy. To address these challenges, this study employs Ansys Parametric Design Language (APDL) to enhance interposer and housing modeling, as well as contact problem resolution. It introduces a novel meshing method that positions nodes at target coordinates using point clouds, providing an effective analysis approach applicable to large, high-density probe cards and thermo-structural problems involving numerous contacts.

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 proposes a systematic data preprocessing algorithm tailored for AI-based modeling of manufacturing data from a roll-to-roll (R2R) lithium iron phosphate (LFP) battery electrode coating process. The preprocessing strategy specifically addresses process characteristics and spatiotemporal inconsistencies in sensor data, significantly improving data quality for machine learning applications. Utilizing the refined dataset, machine learning models were created to predict coating-related characteristics, resulting in high explanatory power and low prediction errors. This framework effectively illustrates the potential of data-driven modeling for reliable predictions and quantitative analysis of coating uniformity in battery manufacturing.

This paper presents an advanced robotic automation framework that combines an impedance-based compliance controller with an imitation learning network for high-precision peg-in-hole assembly. The framework is characterized by three key features. First, it employs an impedance-based compliance controller to ensure stable contact. This approach enables the robot to adapt flexibly to external contact forces, functioning like a spring-damper system to prevent potential damage. Second, domain randomization is applied to both geometric and visual properties within a high-fidelity simulation environment. This strategy effectively narrows the reality gap, enhancing robustness against environmental uncertainties and visual disturbances. Third, the framework utilizes an action-chunking-transformer (ACT) network to predict precise action sequences based on multimodal data, reducing compounding errors in trajectory generation and improving assembly success rates. Each feature is supported by specific advancements, such as real-time force feedback integration, diverse simulation scenario generation, and multimodal sensor fusion. Extensive experiments conducted in various unseen environments demonstrate the framework's effectiveness, confirming its suitability for complex assembly tasks that require high adaptability and precision under diverse conditions.

Among 3D printing techniques, fused deposition modeling (FDM) is known for its design flexibility, rapid fabrication, and the ability to produce complex geometries without molds. However, weak interlayer adhesion often results in poor mechanical strength along the build (Z) direction, limiting its use in structural applications. Instead of altering printing parameters or switching technologies, we propose a simple microwave-irradiation post-treatment to enhance interlayer bonding in FDM-printed parts. By optimizing microwave power and exposure time, we significantly improved interlayer fusion while maintaining the original geometry. Cross-sectional microscopy before and after treatment confirmed markedly improved interlayer bonding (Unbonded interfacial area fraction: 56.82% → 15.51%; -41.31 percentage points, -72.7%). Correspondingly, the Z-direction tensile strength increased from 42.38 to 49.11 MPa (+6.73 MPa, +15.9%). This straightforward post-processing method effectively addresses a key limitation of FDM, thereby expanding its potential for structural and industrial applications.

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 evaluates the structural design and safety of the CanSat in launch environments. The CanSat serves as an educational replica satellite, allowing users to experience the design and operation of small satellites. To ensure stable operation during launch, the structural analysis and design must consider external forces, including vibration and acceleration loads. We determined the material properties for the structure and conducted modal and random vibration analyses, comparing the results with launch environment data from NASA, ECSS, Falcon 9, and Soyuz-2. Additionally, we performed an acceleration load analysis using actual data from CanSat launches during competitions. The modal analysis indicated that the first natural frequency was 65.34 Hz, which exceeds the required threshold. The random vibration and acceleration load analyses further confirmed the structural safety of the design. While the data from NASA and ECSS were conservatively set, reflecting higher vibration intensities, the Falcon 9 and Soyuz-2 launch vehicles provided relatively lower vibration environments due to differences in their designs. Overall, the results demonstrate that the CanSat's structural integrity is maintained under the conditions analyzed for Falcon 9 and Soyuz-2.

Gearboxes used in the drivetrain of intelligent robots are key mechanical components that play a significant role in determining the performance of modern robotic systems. Gearboxes employing the planetary gear mechanism, known to achieve a wide range of reduction ratios while remaining relatively cost-effective, have recently been adopted in robot drivetrains. In this paper, we utilize domestic technology to fabricate a gearbox using a compound planetary gear mechanism and conduct an evaluation of eight performance aspects of the developed gearbox through the fabrication of a dynamometer and a jig. The dynamometer comprised of the gearbox, input motor, input-output torque sensors, and a powder brake. By driving the input motor and applying braking force with the powder brake, we compare input torque sensor values with output torque sensor values to derive results. A test jig is created, consisting of an input motor, gearbox, and encoder sensor, for the measurement of inverse operation characteristics and backlash. By conducting a performance evaluation of the developed high-strength, high-reduction-ratio compact planetary gearbox, we validate the potential of the testing system and extend the scope of domestic gearbox technology development.