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.

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.

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.

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.

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 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 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.

The formation of a hat-profile is significantly influenced by springback and the final cross-sectional geometry, both of which are sensitive to die profile design. This study introduces a scalar-based artificial neural network (ANN) surrogate model combined with genetic-algorithm (GA) optimization to enhance die and process design efficiency. An automated ABAQUS finite-element workflow was established to generate 900 design cases. For each case, seven scalar geometric and angle responses characterizing the post-forming cross section were extracted and used to train a multilayer perceptron. This network maps four die design variables to the final geometry. The surrogate model demonstrated high predictive accuracy, with geometric and angular errors remaining small and coefficients of determination (R²) nearing 1.0. This enabled quick evaluation of new designs without the need for additional finite-element analyses. By integrating the ANN surrogate within a GA, optimal die geometries were identified that reduce springback while meeting target dimensions, showcasing the proposed framework as an effective AI-driven design tool for sheet-metal forming.

Chemical Mechanical Polishing (CMP) is a crucial process in advanced semiconductor manufacturing, essential for achieving global planarization of the wafer surface, which directly impacts device performance and yield. Uniform material removal across the wafer is vital; however, non-uniformity frequently occurs, even with nominally uniform applied pressure. A prevalent issue is the edge effect, where the removal rate at the wafer edge significantly differs from that at the center, resulting in reduced uniformity and compromised device reliability. To tackle this challenge, this study explores the effectiveness of a multi-zone pressure-controlled carrier in enhancing polishing uniformity. Conventional single-zone carriers can only influence a narrow region of approximately 5–7 mm at the wafer edge, leading to limited improvements in nonuniformity of about 3%. In contrast, the multi-zone carrier allows for precise pressure control over a broader range, extending from 3 mm to 20 mm from the wafer edge. Experimental results show that this approach reduces non-uniformity to below 3% while effectively addressing edge removal deficiencies. These findings underscore the significant potential of multi-zone carriers to improve CMP process precision. Consequently, the proposed method is anticipated to enhance both productivity and quality in semiconductor fabrication.

The automotive painting process is complex, featuring hybrid serial-parallel lines and unplanned repair operations, which makes production forecasting challenging. This study introduces an AI-driven predictive framework designed to estimate future work-in-process (WIP) in paint shops, with the goal of improving production management efficiency. We collected and preprocessed historical operational data through noise reduction and process filtering. Several machine learning and deep learning models were trained and validated. To ensure transparency, we utilized explainable AI (XAI) techniques. The proposed system proved feasible for deployment on a web-based monitoring platform, facilitating real-time decision-making in manufacturing environments.

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.

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 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.