The control fin is a key component in a guided missile's propulsion system, stabilizing the missile's attitude and maintaining its flight trajectory under high-speed conditions. Such components demand high mechanical strength and thermal stability. However, traditional control fin designs have primarily focused on external geometry, overlooking opportunities to enhance performance through internal structural design.To address this limitation, this study proposes a design approach that integrates lattice structures within the control fin using metal additive manufacturing. A body-centered cubic (BCC) lattice was selected, with strut diameter and unit cell aspect ratio defined as the primary design variables. Finite element analysis in Abaqus was used to evaluate structural behavior, analyzing stress and displacement distributions based on variations in these lattice parameters. Manufacturability and lightweight characteristics were also assessed. Results indicate that increasing the strut diameter improves structural stability, with stress predominantly concentrated near lattice joints. Building on these findings, a non-uniform lattice design, derived from the uniform lattice analysis, was applied, demonstrating improved stress distribution and overall structural performance. This approach shows that lattice-based internal structures, enabled by metal additive manufacturing, can significantly enhance the structural performance of guided missile control fins while achieving substantial weight reduction.
This study employed the Taguchi method to determine the optimal milling parameters for cast steel, aiming to minimize surface roughness. The analysis indicated that the feed rate was the most significant factor, with lower feed rates resulting in improved surface finish. In contrast, spindle speed and radial depth of cut had minimal impact, while axial depth of cut and tempering temperature emerged as crucial determinants. A linear regression model accounted for 87.46% of the variance in surface roughness. The predicted optimal conditions closely aligned with experimental results, yielding only a 2.93% error and achieving an average surface roughness of 3.033 μm. These findings provide a predictive framework for achieving the desired surface quality in milling processes.
This paper introduces a real-time gait phase detection system and algorithm utilizing non-contact distance sensing, specifically designed for wearable robotic applications. Two Time-of-Flight (ToF) sensors are positioned on the outer heel and the fifth metatarsophalangeal (MTP) joint to monitor foot-to-ground distance throughout the gait cycle. These sensors are housed in overshoe-type modules to minimize interference with natural walking and allow for easy attachment to various types of footwear. The proposed method segments gait phases using a lightweight, thresholdbased algorithm that is both computationally efficient and physically interpretable. Experimental validation with a healthy subject shows that the system reliably detects stance and swing phases, producing temporal patterns that are comparable to those of conventional pressure-based systems. Importantly, the system provides continuous data even during swing phases, facilitating smoother transitions for control systems. The simplicity and wearability of the hardware indicate its potential for real-time control in lower-limb wearable robots, gait assistance devices, and ambulatory monitoring systems.
Optical motion capture (OMC) systems are widely used in rehabilitation, sports, and robotics to obtain accurate segment attitudes. However, OMC marker data can be lost due to occlusions, and reliably recovering data for long-duration missing intervals remains challenging. This study proposes a method for recovering missing markers using inertial measurement unit (IMU) signals and rigid-body constraints. We implemented two recovery methods and validated their performance. The proposed method (M1) combines inter-marker distance constraints with an acceleration constraint, while the comparison method (M2) combines inter-marker distance constraints with a tilt constraint. M1 demonstrated superior performance, with an average root mean squared error that was 3.10 and 3.99 mm lower than that of M2 for the 30 and 180 s missing intervals, respectively. This performance difference arises because M1 directly utilizes measured IMU signals, whereas M2 incurs additional uncertainty due to attitude estimation errors. Furthermore, the proposed method maintained reliable performance even during long-duration missing intervals, as it operates independently of past data, preventing recovery error accumulation. These results demonstrate the feasibility of the proposed IMU-based method for recovering longduration missing marker data in OMC systems.
This study experimentally investigates the laser-assisted diamond turning of high-hardness sapphire to enhance its precision machinability for defense optical components. Sapphire is an attractive material for applications such as transparent armor, sensor windows, and optical apertures due to its excellent mechanical strength, thermal and wear resistance, and outstanding optical transparency. In this research, precision cutting tests were performed on a diamond turning machine, and the resulting surfaces were characterized using a white-light interferometric profilometer. At an optimal laser power of 5 W, the surface roughness and form accuracy improved to 28.8 nm Ra and 191 nm RMS, respectively, demonstrating that laser assistance can significantly enhance surface quality. Microscopic observations after processing revealed a noticeable reduction in tool wear under laser-assisted conditions, which is likely to improve process stability and extend tool life. However, both insufficient and excessive laser power resulted in degraded surface quality compared to conventional turning, underscoring the importance of optimizing laser power. These findings highlight the potential for process optimization in laser-assisted diamond turning to improve the precision and reliability of sapphire machining, contributing to the future development of advanced manufacturing technologies for high-precision defense components.
Fused deposition modeling (FDM) is a popular technique for polymer additive manufacturing. However, the hygroscopic nature of thermoplastic filaments can lead to moisture-related defects during extrusion. When moisture is absorbed and vaporizes inside the nozzle, bubbles form, resulting in voids within the extrudate and deposited roads. This can compromise inter-road bonding and diminish mechanical performance. This study examines how the initial moisture content of ABS filaments affects the tensile behavior of parts fabricated by FDM. ABS filaments were conditioned to seven different moisture levels through water immersion for periods ranging from 0 to 12 hours, with moisture content quantified using the loss-in-weight method (ASTM D6980). ASTM D638 Type I specimens were printed under consistent processing conditions and tested in tension (n = 5 per condition). The results showed that ultimate tensile strength (UTS) decreased as filament moisture content increased, with a maximum reduction of 9.3% observed at 0.69% moisture compared to the dried condition (0.05%). Ductility was assessed by measuring strain at break, and its relationship with moisture content is illustrated in Fig. 4, along with statistical analysis (one-way ANOVA and post-hoc comparisons). These findings offer valuable insights for moisture management and quality control in ABS FDM processes.
Due to the high risks of manual labor in the steel industry, there is a growing demand for robot-based solutions to replace traditional manpower. Steel companies aim to reduce on-site personnel, minimize accidents, and enhance productivity. This study develops a robotic system to monitor conveyors in ironmaking and detect potential bearing failures. Rollers on belt conveyors contain bearings that emit abnormal noise when worn or damaged. Traditional manual inspection requires workers to approach each roller and listen directly, posing safety risks and inefficiencies. The proposed system detects faulty bearings more quickly and accurately by localizing abnormal sounds. The system comprises a manipulator with a microphone on its end-effector. The microphone collects sound along the conveyor as the manipulator moves to detect noise sources. Once an abnormal bearing is located, faster and more accurate maintenance becomes possible. This robotbased inspection method improves safety, inspection speed, and productivity.
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 OmniverseTM 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.
First
Prev
