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.

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 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.

This paper examines the role of generative AI and large language models (LLMs) in advancing intelligent manufacturing as we transition from Industry 4.0 to Industry 5.0. We begin by analyzing the current limitations of rule-based and manufacturing data systems in facilitating flexible, human-centric production. Next, we categorize LLM utilization strategies into three methodological axes: fine-tuning domain-specific models, employing general-purpose models through prompt engineering, and utilizing retrieval-augmented generation (RAG), which includes multimodal RAG that integrates sensor and text data. For each strategy, we present representative case studies across key application areas such as asset management, maintenance intelligence, quality control, process optimization, and knowledge- and document-centric support systems. Concurrently, we explore how information modeling and ontology-based knowledge graphs can be integrated with LLMs to enhance structured manufacturing semantics, improve source traceability, and minimize hallucinations. Finally, we summarize the advantages and limitations of each approach and propose future research directions for human-centric manufacturing, including the development of trustworthy LLM pipelines, standardized data schemas, and closer integration between digital twins and LLM-based decision support systems.

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.

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.

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.

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.

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.

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.

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.