This study evaluates the load and moment characteristics of composite leaf springs used in the front suspension of a 4.0- ton gross vehicle weight (GVW) light commercial van through CarSim-based vehicle dynamics simulations. Carbon fiber composite (CFC), glass fiber composite (GFC), and hybrid composite (HC, carbon 20%: glass 80%) leaf springs were fabricated with identical geometry using a prepreg compression molding (PCM) process. Spring constants obtained from four-point bending tests were incorporated into the vehicle dynamics model. Dynamic responses were analyzed under flatroad driving, acceleration, braking, cornering, and speed bump conditions. The results indicate that the GFC leaf spring achieved a 61.5% weight reduction compared to a conventional steel spring while maintaining equivalent vertical load and roll moment responses. The HC exhibited improved roll suppression and pitch stability, whereas the CFC demonstrated excessively high stiffness, limiting its applicability to heavy-duty vehicles. Furthermore, the GFC maintained stable dynamic performance after low-velocity impact damage of 20 and 80 J, with stiffness remaining within ±5% of the steel reference. These findings confirm that composite leaf springs, particularly those made from glass fiber composites, provide a practical and durable alternative to steel leaf springs for light commercial vehicle suspension systems.

The nano satellite industry has transitioned to low-cost development, driven by private companies and research organizations in the NewSpace era. Can-Satellite offers a budget-friendly alternative to traditional cube satellite manufacturing and testing. This study focuses on enhancing the reliability of small satellite designs by analyzing the vibration stability of PLA plates, the primary structure of a Can-Satellite, produced through Fused Filament Fabrication (FFF) 3D printing. Quasi-static, modal, and random vibration analyses were conducted using Finite Element Analysis (FEA) with ANSYS to evaluate stacking directions along the x, y, and z axes and optimize structural stability. The findings indicate that the y-axis laminated structure exhibits superior vibration endurance, effectively reducing issues during launch. This research contributes to improving the reliability of Can-Satellites and enhances manufacturing efficiency for cube and micro-satellite projects. Additionally, it supports the advancement of educational satellites and domestic small satellite technology.

Accurate 3D human pose reconstruction from a single RGB image remains challenging due to scale ambiguity and perspective distortions. Current single-view methods primarily rely on learned priors or kinematic constraints, but they often struggle to maintain geometric consistency with the physical scene. This results in horizon alignment drift and instability when rendered in metric environments. To overcome these limitations, this study introduces a vanishing-point-driven framework that integrates scene geometry into the pose correction process. Under the Manhattan-world assumption, dominant vanishing points are detected to estimate the ground plane and recover the camera orientation with high precision. A lightweight 3D pose estimation network generates initial joint coordinates in camera-centric space. These coordinates are then refined through a VP-based ground-alignment transformation, which resolves scale ambiguity and minimizes geometric drift. The corrected poses are normalized to physical scale and streamed to NVIDIA Omniverse^TM for real-time digital-twin visualization. Experiments conducted on indoor scenes from the NYU Depth V2 dataset demonstrate sub-pixel accuracy in vanishing-point localization and significant improvements in geometric alignment between the reconstructed poses and the true scene layout. This confirms the effectiveness of the proposed approach for single-view digital-twin human modeling.

Accurate localization in industrial environments is challenging due to factors such as dust and reflections that degrade perception. To overcome these limitations, we propose an environment-independent localization method that relies solely on ultra-wideband (UWB) positioning. Our system employs LiDAR-SLAM in an offline stage to create a global map frame and calibrate the transformation between this frame and the UWB anchors. During operation, the robot estimates its position using a Kalman filter applied to UWB measurements transformed into the map frame. This paper presents a preliminary feasibility study conducted in an office-like environment to verify the core calibration and localization pipeline. The results show that the proposed method effectively aligns UWB positions with a pre-built SLAM map, achieving a 94% reduction in root-mean-square error (RMSE) compared to raw UWB measurements when validated against LiDAR-SLAM ground truth. This initial verification establishes the technical viability of the framework and lays the groundwork for future validation in harsh, large-scale industrial settings.

This study presents an optimization framework for designing novel retainer rings (NRR) in chemical mechanical planarization (CMP) to enhance the uniformity of material removal rates (MRR). To improve optimization efficiency, we developed a finite element method (FEM) model alongside a Metamodel of Optimal Prognosis (MOP). The NRR outperformed the reference retainer ring (RRR) in our simulations. We classified simulation cases based on the pressure application area: long (LC), middle (MC), and short (SC). The MOP was constructed using Latin hypercube sampling and refined through an adaptive approach to achieve high accuracy while minimizing computational costs. Optimization was performed using an evolutionary algorithm, generating Pareto fronts for analysis. We evaluated representative designs based on MRR distribution and non-uniformity. Ultimately, Design 2-LC was identified as the optimal choice. The results indicate that the proposed framework effectively enhances MRR uniformity while reducing optimization time.

This study introduces a wire-spring based planar gravity compensation mechanism and evaluates its performance through both analysis and experiments. The mechanism features three pulleys, one spring, and one wire, all arranged in a planar configuration for compact installation within a robotic arm. A linear approximation of the target gravitational torque was derived using the least-squares method, allowing for the determination of spring stiffness and initial tension. Experimental results indicated that the proposed mechanism reduced the maximum torque by approximately 63%. However, the measured slope was gentler than the theoretical model due to friction losses. Additional tests that varied spring stiffness (k) and initial wire tension (A) confirmed that k primarily influences the slope of the compensation torque, while A affects its intercept. This finding suggests that compensation performance can be tailored to specific requirements by adjusting these parameters. The study successfully demonstrates a compact and lightweight mechanism and experimentally validates its tunability through design adjustments. Future research will focus on reducing friction, extending the mechanism to multi-degree-of-freedom systems, and validating performance under dynamic conditions for applications in collaborative and medical robots.

Manufacturing systems are increasingly required to operate in high-mix, low-volume production environments, where process flexibility is crucial. One effective way to achieve this flexibility is through the use of multiple processing alternatives (MPA), allowing a product to be produced using different process plans or component structures. In MPA environments, scheduling decisions must address both the selection of processing alternatives for each product and the execution order of the resulting production tasks. Additionally, processing times often vary due to machine conditions and process variability, further complicating scheduling. This study introduces a dual-network-based deep reinforcement learning method for scheduling in manufacturing systems with multiple processing alternatives. The framework utilizes two Q-networks to learn both the selection of processing alternatives and the dispatching rules. Computational experiments demonstrate that the proposed method effectively reduces both the average makespan and its variability compared to a genetic algorithm-based approach, particularly as the problem size increases, showcasing its effectiveness in the face of processing time uncertainty.

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.

The increasing use of computational modeling and simulation (CM&S) in the medical device sector has heightened the need for ensuring simulation credibility. The ASME V&V 40 standard offers a structured framework for assessing credibility, consisting of 23 factors divided into three main categories: Verification, Validation, and Applicability. However, practical guidance for implementing these factors is still scarce. This study systematically reviewed and analyzed ten CM&S-related publications in the medical device field that utilized the ASME V&V 40 framework. It examined how each publication addressed the credibility factors and compared their implementation methods, evaluation criteria, and credibility levels. From this comparative analysis, we developed implementation strategies focused on credibility factors, field-specific characteristics, and model risk levels in real-world regulatory and development contexts. Key considerations for the practical application of each factor were identified, and recommendations for effective implementation were proposed. These findings offer practical guidance for ensuring credibility in CM&S-based medical device development, performance evaluation, and regulatory processes. By clearly demonstrating the applicability of the ASME V&V 40 framework, this work provides valuable direction for related industries and research institutions, aiming to improve CM&S credibility and promote its broader adoption in healthcare.

Magnetic gears transmit torque via non-contact electro-magnetic coupling, which eliminates mechanical contact and significantly reduces wear, backlash, and noise compared to traditional mechanical gears. These benefits make magnetic gears particularly appealing for high-precision, high-reliability applications. However, achieving both high torque density and high gear ratios necessitates an optimized structural design that promotes efficient magnetic flux distribution while minimizing leakage and saturation. This study focuses on a hollow-type magnetic gear for collaborative robots that offers a high gear ratio. It employs topology optimization in conjunction with finite element analysis (FEA) to enhance torque density and efficiency. Key design variables, such as the geometry of the ferromagnetic core and the arrangement of permanent magnets, were optimized to increase average torque and reduce torque ripple and electro-magnetic losses. A prototype based on the optimized model was fabricated, and its performance was validated using a conventional direct torque measurement system. Experimental results were compared with simulation predictions to evaluate accuracy and analyze loss characteristics. The findings demonstrate the effectiveness of the proposed optimization approach and provide practical guidelines for designing high-efficiency magnetic gears suitable for advanced drive systems, including electric mobility and renewable energy applications.

We present an automated incasing process designed to replace traditional manual packaging of dried seaweed. This system consists of three key components: a cage mechanism that compresses and transfers six bundles, a handling device for stacking the bundles, and a collaborative robot that performs the box incasing operation based on sensor input. The handling device utilizes pneumatic actuators and a wire-linked folding plate to minimize interference within the confined box space, while also allowing for adjustable dimensions to accommodate seasonal variations in bundle size. Field validation was carried out under continuous input conditions using a conveyor. The collaborative robot followed a predefined sequence triggered by a presence sensor, effectively grasping, stacking, compressing, and transferring bundles without causing product damage. Experimental results indicated that the system successfully incased 72 bundles per box with stable performance and reliable placement. These findings demonstrate the feasibility of replacing labor-intensive operations with collaborative robotic automation in seafood packaging, highlighting opportunities for enhanced consistency, ergonomics, and productivity.

Ethanol poses a significant threat to driver safety, as its effects vary with blood alcohol concentration (BAC). Common methods for estimating BAC include breath alcohol analysis, which calculates BAC from the alcohol concentration in exhaled breath, and direct blood sampling. However, these methods have notable limitations. This study aims to classify alcohol concentration using non-invasive optical signal data obtained from biomimetic samples with varying alcohol levels. To replicate the high scattering characteristics of biological tissue, scattering effects were induced in the samples, and absorbance was measured using near-infrared (NIR) wavelengths, which penetrate biological tissue more deeply. A Random Forest (RF) model was trained using the measured absorbance values to classify alcohol concentration levels. The Area Under the ROC Curve (AUC) for each concentration level indicated effective model learning, and the classification results on the test set demonstrated statistically significant accuracy. These findings suggest that the RF model can classify alcohol concentrations non-invasively and without the loss of samples. Furthermore, incorporating additional optical properties beyond absorbance may improve the accuracy of future non-invasive alcohol concentration classification models.

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.

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.

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.