Recent manufacturing environments demand greater flexibility due to the increasing need for high-mix, low-volume production. While mobile and collaborative robots have made it easier to relocate equipment and change layouts, reconfiguring manufacturing cells remains challenging. Successful reconfiguration relies not only on physical layout changes but also on a deep understanding of the original design intent, operational constraints, and the empirical knowledge gained during operation. Unfortunately, this knowledge is often implicit and may depend on engineers or operators who are no longer available. To tackle this issue, this study introduces a framework for manufacturing cell reconfiguration based on the Asset Administration Shell (AAS). This framework integrates static engineering information with the operational knowledge acquired throughout construction and operation. It organizes asset specifications, operational states, manufacturing skills, and related documents into a unified structure, enabling reconfiguration decisions to reflect both system configurations and proven operating conditions. Furthermore, it connects work execution results with operational knowledge, document versions, and raw data references to enhance traceability and reproducibility post-reconfiguration. This proposed approach aims to reduce the complexity and cost of cell reconfiguration and relocation while enhancing operational flexibility, consistency, and scalability.
As smart factories evolve, maintenance manuals need to be transformed from static documents into machine-readable and reusable digital assets. However, many legacy manuals are still in unstructured formats, such as Hangul word-processor files, which complicates their updating, reusability, and adaptability to changing product configurations. This paper presents a framework for converting these legacy manuals into S1000D-based documents. It combines style-based hierarchy extraction with rule-guided multi-step transformation using a local large language model (LLM). First, the style information within the Korean documents is analyzed to identify the hierarchical structure of the manual and extract content at various document levels. Next, this extracted content is converted into S1000D XML modules through the local LLM, utilizing category-specific rule files, XML tag definitions, and example templates. To enhance structural consistency and minimize errors, different prompts and rule sets are applied based on the document hierarchy level.A case study involving a maintenance manual for a high-angle limit switch module demonstrates that the proposed method can maintain document structure while generating reusable S1000D-style outputs from legacy technical documents. This approach lays a practical foundation for creating continuously updatable and context-reconfigurable maintenance guidance in smart manufacturing environments.
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.
Secondary batteries are crucial for eco-friendly systems, but existing technologies struggle with energy density and safety issues. This study aims to develop a next-generation battery utilizing quasi-solid electrolytes (QSE), which combine the advantages of both liquid and solid electrolytes. However, QSEs often lack the mechanical strength necessary to prevent lithium dendrite growth. To address this challenge, two strategies were proposed and experimentally validated. The first strategy involves creating a QSE-separator composite (QSE-PI) by integrating QSE with a polyimide (PI) separator. Among the various options, PI with a thickness greater than 20 μm and a pore size of 2-5 μm exhibited superior electrolyte absorption and dendrite suppression. This configuration allowed for rapid lithium plating/stripping, high ionic conductivity (1.7 × 10-3 S cm-1), and excellent Coulombic efficiency (99.94%).The second strategy incorporates silica (SiO2) as a ceramic filler in the QSE-PI to enhance mechanical strength and ion transport. The addition of SiO2 disrupted polymer crystallinity, increased the amorphous regions, and effectively suppressed dendrite formation. Notably, SiO2 particles larger than 10 μm improved cycle stability, with the composite maintaining performance for over 50 cycles, compared to only 30 cycles for the version without filler.
As AI transformation expands in manufacturing, intelligent technologies are increasingly applied to CNC machine tools and machining processes. In multi-product, small-batch production environments, frequent product changes require flexible and autonomous process planning. This study proposes a standard data integration-based intelligent process planning system that automatically performs the entire process from 3D model input to NC code generation. To enable intelligent process planning, data across all stages—from feature recognition to machining execution—must be integrated into a unified flow and connected with AI-based decision-making. The proposed system uses an ISO 14649-based XML schema to sequentially link data generated by each module, ensuring standardized information flow. Based on this framework, rulebased feature recognition, constraint-based process planning, and machine learning-based cutting condition optimization are implemented. A prototype system was developed to validate the approach, automatically generating NC code for industrial parts and performing actual CNC machining. Experimental results confirmed the feasibility and validity of the proposed system. This study demonstrates that standardized data integration combined with AI technologies can enable autonomous, flexible, and efficient process planning for advanced manufacturing environments.
Despite the increasing focus on the mental health of older adults and active senior populations, assessment tools still lag behind those for physical health monitoring. To bridge this gap, this study introduces an AI chatbot-based multimodal stress monitoring system that utilizes emotion recognition and heart rate variability (HRV). The system analyzes chatbot conversations, video, audio, and heart rate signals to assess facial expressions, speech emotions, and HRV, allowing for stress evaluation and user stratification into risk groups. Negative emotions are quantified and combined with HRV data to generate a stress score. Facial and speech emotion models were trained on the RAVDESS, CREMA, and TESS datasets, yielding 21,000 augmented samples through a BiLSTM network. Additionally, a deep learning-based HRV model utilized data from smartwatches to predict stress levels. By integrating facial, vocal, and HRV features through weighted fusion, the system produces a comprehensive stress index that categorizes users Healthy, Caution, Risk. This approach facilitates continuous monitoring at home, supporting early detection for preventive care and informed clinical decision-making.
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.
Pad conditioning restores degraded pad surfaces after wafer polishing in chemical mechanical planarization (CMP) using diamond-embedded conditioner discs. However, conditioning also causes pad cutting, thickness reduction, and profile deformation. While previous studies mainly focused on reducing pad cut rate (PCR) and improving profile uniformity, the fundamental cutting mechanism between conditioner cutting edges and the pad remains unclear. This study investigates the cutting mechanism using CVD conditioner discs with different cutting edge densities under varying conditioning loads to control contact area and load distribution. PCR and pad profile analyses revealed that cutting behavior is primarily governed by the load applied to individual cutting edges. Higher localized loads increased the contribution of cutting to overall material removal. In the pad edge region, where the conditioner partially overhangs the pad, altered contact geometry caused a transition in cutting mode. In this region, the number of active cutting edges had a greater influence than the load per edge. These findings clarify the cutting interactions between CVD conditioner edges and pad surfaces during conditioning and provide a physical foundation for optimizing conditioning parameters to improve pad management in CMP processes.
Deep-sea optical windows must withstand extreme hydrostatic pressure while maintaining optical transmittance, requiring a balance between mechanical rigidity and optical performance. Increasing thickness enhances structural strength but reduces transmittance. This study proposes a design method for deep-sea optical windows using domestically developed sapphire. Three-point bending tests were conducted on sapphire and silicon specimens, and B-criterion strength was derived using Weibull distribution to account for brittle material properties. Optical transmittance measurements established key design characteristics. Using theoretical formulations for rectangular planar optical windows under uniform external pressure, the initial design was based on experimentally derived sapphire properties. Finite element analysis of the optical window assembly confirmed sufficient structural stability margins above critical thresholds. Linear interpolation was applied to evaluate the continuous design space across discrete thickness values. A compromise solution was identified that satisfies both structural rigidity and transmittance objectives. By integrating experimental material characterization with numerical analysis, this study provides an effective framework for determining the optimal thickness of deep-sea optical windows and confirms the applicability of domestically developed sapphire as a reliable optical window material for high-pressure underwater environments.
In the rapidly evolving e-commerce industry, high-quality and consistent product images are essential for engaging consumers. Traditional manual photography often lacks consistency, while advanced robotic solutions can be overly complex and expensive for standardized cataloging. This paper details the design and validation of a 4-DOF (Degrees of Freedom) automated system for standardized product photography. The system employs a modular, fixed-platform architecture that adjusts camera height, tilt angle, object rotation, and perspective translation. An integrated control system facilitates automatic pose planning based on object size, ensuring efficient operation. We quantitatively evaluated the system's performance using metrics for perspective consistency and repeatability. Experimental results across various product types showed high stability, with minimal variance in bounding box area ratios from different viewpoints. The system exhibited exceptional repeatability in random trials, consistently achieving pair Intersection over Union (IoU) values above 0.90. This high level of geometric precision confirms the system's reliability for capturing uniform, multi-angle product images. Ultimately, this capability allows for the scalable and automated production of high-quality visuals for e-commerce.
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 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.
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.
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.
The development of high-performance lithium-ion battery electrodes necessitates reducing the content of conductive additives while preserving excellent electrochemical properties. In this study, multi-walled carbon nanotubes (CNTs) with approximately 3 to 7 walls were synthesized and characterized using transmission electron microscopy (TEM), scanning electron microscopy (SEM), and thermogravimetric analysis (TGA) to confirm their morphology and purity. Electrical conductivity was assessed through powder resistivity measurements. CNT dispersions were prepared by ultrasonic treatment using N-methyl-2-pyrrolidone (NMP, 95 wt%), a dispersant (2 wt%), and CNTs (3 wt%). Thin films coated on glass slides showed surface resistances of 36 Ω/sq, indicating superior electronic conductivity compared to conventional carbon black. The optimized CNT dispersion was then mixed with NCM613 cathode active material and a binder to create electrodes containing only 1 wt% conductive additive. For comparison, reference electrodes were also prepared using conventional carbon black at a loading of 2.2 wt%. Electrochemical testing revealed that the CNT-based electrodes achieved comparable cycling stability, rate capability, and capacity retention, despite having a lower conductive additive content. These results demonstrate the feasibility of reducing conductive additive loading to 1 wt% by utilizing highly conductive CNTs, thereby increasing the proportion of active material and enhancing overall energy density.
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