This study presents a Python-based design tool that utilizes a data-driven backtracking method for aspheric coefficients to effectively address thermal deformation in aspheric glass lenses produced by the Glass Molding Press (GMP) process. To achieve the nanometer-level form accuracy essential for optical communications and high-power laser applications, it is crucial to compute compensation coefficients through nonlinear least-squares fitting of the lens's measured data to the standard aspheric equation. The proposed tool enhances user-friendliness with a PyQt GUI and incorporates the lmfit library, offering unique flexibility by allowing users to select or fix specific variables among up to 22 parameters, including the radius of curvature, conic constant, and aspheric coefficient, during the fitting process.

Military shelters house various high-power electronic systems that generate significant heat during operation, making effective thermal management essential for ensuring system reliability and operational stability. This study proposes a one-dimensional (1D) modeling approach for analyzing the thermal behavior of a military shelter–HVAC system using Simcenter AMESIM. The model incorporates key thermal components, such as internal heat sources, shelter structures, and HVAC performance characteristics, to accurately represent dynamic thermal behavior under realistic operating conditions. To validate the proposed 1D model, a comparative analysis was performed using threedimensional computational fluid dynamics (CFD) simulations with ANSYS Fluent, maintaining identical boundary and operating conditions. The analysis focused on the temporal and spatial variations of the internal air temperature within the shelter. Results showed a strong correlation between the AMESIM model and the CFD simulations. This approach significantly reduces computational costs and modeling complexity compared to traditional CFD-based analyses, while still providing adequate accuracy for system-level thermal performance evaluation. Consequently, the developed AMESIM-based 1D model serves as an efficient and reliable tool for the design and performance assessment of military shelter HVAC systems.

This paper describes the calibration of a 2D vision system utilizing a high-precision robot that moves accurately along the z-axis. To achieve this, a one-axis high-precision robot was designed and manufactured, achieving a minimum travel distance of 0.007 μm during testing. The 2D vision system comprised an RGB camera, an LVDT sensor, and a laser sensor for distance measurement. Additionally, a subpixel-based algorithm was developed for the calibration and size measurement of the system. Following the calibration process with the high-precision robot, the size error was found to be within ±0.1 mm when using the LVDT sensor and ±0.2 mm with the laser sensor. Thus, the 2D vision system and calibration algorithm presented in this paper are deemed suitable for accurate object measurement by robots.

Dual-drive H-type gantry stages, powered by direct-drive linear motors, are commonly used in precision industrial applications that require high positioning accuracy over a large workspace. The cross-arm rigidly couples the two parallel axes, making strict synchronization control essential for maintaining positioning accuracy. Conventional master-master control treats the coupling effects as disturbances without accounting for the synchronization of the two motors. As a result, its performance deteriorates significantly under non-uniform load distribution, payload rotation, and directly applied torque disturbances, all of which necessitate reliable synchronization control. This study proposes a synchronization control scheme that integrates a sliding-mode controller with an uncertainty and disturbance estimator (SMC-UDE) and a feedforward torque compensator for the inertial torque induced by payload rotation. The proposed controller was implemented on a Hardware-in-the-Loop (HIL) simulator constructed with two parallel voice coil motors, and its performance was experimentally validated on the testbed. The HIL experimental results demonstrate that the SMC-UDE controller with feedforward torque-disturbance compensation achieves a quicker settling time compared to the conventional master-master control strategy. Additionally, it effectively suppresses disturbances generated by a rotary motor attached to a payload, thereby maintaining synchronization accuracy under disturbed conditions.

Direct-drive robot arms, often utilized in manipulators and humanoid robots, face challenges related to increased distal mass and reduced backdrivability due to motors being placed at each joint. This paper presents a 4-DOF elbow-wrist mechanism for a robot arm driven by wires. The design includes the upper arm, elbow, upper forearm, lower forearm, and wrist, with all actuators concentrated in the upper arm to minimize distal mass. To achieve this, the mechanism employs rolling-contact joints, forearm rotator idlers, and agonist-antagonist wire pairs. Additionally, a differential mechanism is integrated at the wrist, allowing for yaw rotation at the forearm while enabling roll and pitch rotations at a single point, thereby eliminating inter-axis offset and mimicking human wrist motion. A prototype was fabricated and evaluated, achieving an elbow flexion of 107°, forearm yaw exceeding 90° in both directions, wrist roll of 63° and 50° in each direction, and wrist pitch of 33° in both directions. The independence of each degree of freedom was validated, and torque efficiency from the upper arm to the wrist tip was measured and analyzed.

Ti-6Al-4V titanium alloy is widely utilized in aerospace components, such as torque tubes and turbine blades, due to its outstanding strength-to-weight ratio and corrosion resistance. However, controlling surface roughness during machining is challenging because the alloy's low thermal conductivity and high chemical reactivity result in unpredictable variations in Ra. Traditional contact-based measurement methods are not only time-consuming but also incompatible with in-process monitoring, creating a disconnect between production and quality control. This study introduces a CNN-LSTM hybrid model for predicting surface roughness in Ti-6Al-4V shape machining, utilizing multi-sensor CNC data. The model effectively captures spatial correlations among nine sensors and temporal dependencies in sequential operations. We implement a stratified time-series split validation that maintains chronological order while ensuring a representative distribution of Ra values, reflecting realistic deployment conditions. Data were collected from machining tests on features of a torque tube part, comprising 4,154 samples with Ra values ranging from 0.57 to 0.74 μm. The CNN-LSTM model achieved R² = 0.8512, RMSE = 0.0199 μm, and MAE = 0.0096 μm, outperforming Random Forest, XGBoost, and standalone neural networks. These results demonstrate the feasibility of non-contact, in-process surface roughness prediction in aerospace manufacturing, facilitating proactive quality control without interrupting operations.

This study proposes a deep learning-based image anomaly detection system to identify irregular defects in metallic DC fuses. In the manufacturing industry, product quality control is directly linked to productivity, and appearance-related defects, such as surface imperfections, impact competitiveness and reliability. However, conventional visual inspection and simple image-processing methods have limitations in inspection speed and precision, often struggling to accurately detect complex and diverse irregular defects. To address these challenges, this study employs Dinomaly, an unsupervised reconstructionbased Transformer model that can be trained using only normal images. The proposed method takes images of DC fuses as input, automatically detects anomalous regions, and visualizes the location and morphology of irregular defects through an anomaly map, facilitating intuitive interpretation. Additionally, various data preprocessing techniques, including adjustments for diverse illumination conditions and brightness, are applied to augment the dataset, reflecting real-world environmental variations and ensuring robust performance. Experimental results demonstrate that the proposed approach effectively detects irregular defects in DC fuse data. This study is expected to contribute to the automation of DC fuse quality inspection processes, enhance the reliability of automotive electronic components, and advance quality management in smart manufacturing.

Cable chains are essential for guiding and protecting cables in repetitive linear-motion equipment. However, during highspeed operations, inertial effects and structural deformation can lead to position overshoot beyond the intended stroke, resulting in off-path motion and increased stress concentrations in links and joints. This study assesses the structural stability of a U-shaped cable carrier under conditions of position overshoot and suggests an optimized geometry. To analyze this, a nonlinear finite element model is employed, constraining one end of the carrier while applying a prescribed overshoot displacement to the other end. Structural stability is measured using a stability index, which is defined as the maximum reaction force at the point of yielding, when the equivalent (von Mises) stress reaches the material's yield stress. A sensitivity analysis identifies the key geometric design variables, and response surface methodology is applied to find an optimal shape that maximizes the reaction force at yielding. The proposed simulation-driven workflow offers practical design guidance for enhancing the stability of cable carriers during non-ideal overshoot events.

Hand-arm vibration exposure is a recognized occupational hazard that can cause discomfort and long-term disorders. While anti-vibration gloves are commonly used to reduce these effects, their effectiveness is often limited by the stiffness of the materials and structural constraints. This study focuses on the development and evaluation of polydimethylsiloxane (PDMS)- based anti-vibration layers with varying internal structures for use in anti-vibration gloves. We prepared three types of PDMS layers: solid PDMS without pores (SPDMS), porous PDMS foam created through a sugar-leaching process (FPDMS), and a hybrid PDMS structure that combines solid and porous layers (HPDMS), all shaped like palms. These PDMS layers were integrated into glove specimens, and their vibration transmissibility was assessed using a measurement system compliant with ISO 10819:2013. Vibration transmissibility was recorded across one-third octave bands from 25 to 1,250 Hz, and frequency-weighted transmissibility values were calculated for both the M- and H-spectra. The results indicate that PDMSbased anti-vibration layers with controlled porosity can be effectively fabricated and incorporated into glove structures, and that variations in internal porosity significantly impact the measured vibration transmissibility characteristics.

This study quantitatively investigates the deviation between the ideal and effective amplification ratios of a bridge-type displacement amplification mechanism used in ultra-precision positioning systems. It proposes region-specific optimal design strategies to mitigate this deviation. A Leave-One-Out sensitivity analysis reveals that the dominant factor influencing amplification ratio deviation shifts at an ideal amplification ratio of approximately 10. In the high-amplification region (R > 10), displacement loss due to bending of the input link is identified as the primary cause of deviation. Reinforcing the input link's thickness and incorporating a pocket structure reduces the deviation to within 12.4% while minimizing resonance frequency degradation. Conversely, in the low-amplification region (R < 7), the main issue is dynamic performance deterioration caused by oversized intermediate links. Implementing a hexagonal mass-reduction design enhances the first resonance frequency by up to 28% without compromising the amplification ratio. These findings establish differentiated design guidelines based on the target amplification ratio, enabling simultaneous improvements in precision and dynamic performance for bridge-type displacement amplification mechanisms in positioning systems.

Trunk balance is essential for physical stability, but traditional assessment tools, such as force plates and 3D motion capture systems, can be inaccessible or unsafe for patients unable to stand. This study introduces a seated trunk balance training and assessment system that combines a tilting chair with an Inertial Measurement Unit (IMU). The system features a kinematic design that transmits the user's trunk movements to the chair's seat, allowing for quantitative measurement without the need for body-mounted sensors. A pilot study was conducted to evaluate technical feasibility by comparing a seat-mounted sensor (CS-IMU) with a reference sensor (T12-IMU) attached to the T12 vertebra. Assessment items included Range of Motion (RoM), rotation accuracy, and agility. Results indicated a very high correlation for lateral bending (r = 0.958) and rotation phase angle (r = 0.990), while flexion/extension showed a moderate correlation (r = 0.691). Additionally, agility tasks demonstrated consistent results in the lateral direction (r = 0.806). These findings suggest that the proposed system can effectively quantify multi-dimensional trunk movements in a seated position, making it a valuable tool for high-risk populations, such as patients recovering from acute stroke or spinal cord injuries.