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.

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 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.

This study examines the porosity behavior during the directed energy deposition (DED) of dissimilar metals S45C and H13. We analyzed the effects of deposition parameters, including laser power, feed rate, and powder characteristics, on pore formation, taking into account the unique properties of these metals. Our findings indicate that laser power is the primary factor influencing porosity. At a low power of 200 W, insufficient energy input, along with differences in thermal conductivity and chemical composition between S45C and H13, led to incomplete melting and lack-of-fusion, resulting in high porosity. As the laser power increased to 400-600 W, the melt pool stabilized, enhancing interfacial bonding and significantly reducing porosity. However, at an excessive power of 800 W, rapid melting and solidification of the powder caused gas entrapment and pore formation, which increased porosity, particularly due to the differing thermal conductivities of S45C and H13. Therefore, our results suggest that maintaining an adequate laser power of 400-600 W is essential for achieving a stable melt pool and minimizing porosity in the DED process for dissimilar S45C and H13 metals.

In this study, we comparatively analyzed the convective heat transfer performance of single-wall and double-wall Gyroid TPMS (Triply Periodic Minimal Surface) structures. Using computational fluid dynamics (CFD), we evaluated the average convective heat transfer coefficients under constant surface temperature conditions for both constant velocity and constant pressure flow. Although both structures maintained the same fluid volume, the double-wall configuration increased the surface area by approximately 1.8 to 1.9 times, resulting in enhanced heat transfer performance. Under constant velocity conditions, the double-wall structure exhibited an average convective heat transfer coefficient that was 1.3 to 1.4 times higher than that of the single-wall structure. Under constant pressure conditions, we observed an increase of 1.06 to 1.1 times. Despite the double-wall structure leading to greater pressure losses due to increased shear stress from the formation of microchannels, it still maintained improved heat transfer performance even with reduced mass flow rates under constant pressure conditions. These findings provide fundamental data for designing TPMS-based cooling systems and optimizing additive manufacturing processes.

Silicon is a key material in advanced technologies due to its thermal stability, appropriate bandgap, and wide applicability for advanced devices. Si microstructures offer enhanced surface area, thus improving performances for energy storage and biosensing applications. However, conventional top-down fabrication methods are complex, costly, and environmentally unfriendly as they rely on cleanroom facilities and toxic chemicals. This study proposed a simplified, eco-friendly bottom-up laser-based process to fabricate silicon microstructures. By controlling laser parameters during the interaction with silicon nanoparticles, diverse Si structures can be fabricated by Si nanoparticle coating and laser irradiation.

Facility Layout Problem (FLP) aims to optimize arrangement of facilities to enhance productivity and minimize costs. Traditional methods face challenges in dealing with the complexity and non-linearity of modern manufacturing environments. This study introduced an approach combining Reinforcement Learning (RL) and simulation to optimize manufacturing line layouts. Deep Q-Network (DQN) learns to reduce unused space, improve path efficiency, and maximize space utilization by optimizing facility placement and material flow. Simulations were used to validate layouts and evaluate performance based on production output, path length, and bending frequency. This RL-based method offers a more adaptable and efficient solution for FLP than traditional techniques, addressing both physical and operational optimization.

Holonic Manufacturing Systems (HMSs) are regarded as a foundation of cyber-physical production systems as they enable computers to conduct intelligent process planning, scheduling, and control by endowing manufacturing components with autonomy and collaboration. In an HMS, autonomy is realized by specifying holons that represent virtual agents of manufacturing components, while collaboration is facilitated through a communication mechanism that enables data exchange and decision making throughout a holarchy of holons without human intervention. This study presents the development of a virtualized holon model and a predictive process planning procedure using the Asset Administration Shell (AAS), i.e., a standardized model that can identify digital representation of manufacturing components to ensure interoperability. Specifically, an AAS-based information model was proposed to define operator, machine, product, and order holons. In addition, a predictive process planning procedure based on the Contract Net Protocol was developed to automatically allocate tasks while predicting task execution times. This study can contribute to the designing of an AAS- domain specific information model for HMS to increase interoperability in the holon holarchy, exhibiting the feasibility of AAS applications in predictive process planning on HMS.

This study aims to optimize the process conditions for high-density polyethylene (HDPE) additive manufacturing through a systematic analysis of key variables, including material selection, layer height, feed rate, melting temperature, and bed temperature. By exercising precise control over these variables, optimal conditions were established, which included a melting temperature of 240oC, a welding speed of 150 cm/min, and a material throughput of 5.66 kg/h. Furthermore, the process was refined by implementing a zig-zag layering method, which significantly improved the stability, bonding strength, and overall mechanical properties of the final HDPE products. The effects of these optimized process conditions were assessed through a series of mechanical tests, such as tensile tests, impact tests, and heat deflection temperature (HDT) tests. As a result, the defined process conditions yielded excellent mechanical performance, achieving a tensile strength of 21.15 MPa, an impact strength of 320 J/m, and an HDT of 93oC. Overall, this study illustrates the enhancement of HDPE additive manufacturing quality through the optimization of process conditions. The strategic implementation of these optimized variables, along with advanced extrusion module design, demonstrates the potential for producing high-quality and cost-effective HDPE products, thereby underscoring their enhanced marketability and performance potential.

Additive manufacturing, a key enabler of Industry 4.0, is revolutionizing the automatic landscape in manufacturing. The primary challenge in manufacturing innovation centers on the implementation of smart factories characterized by unmanned production facilities and automated management systems. To overcome this challenge, the adoption of 3D printing technologies, which offer significant advantages in standardizing production processes, is crucial. However, a major obstacle in complete automation of additive manufacturing is an inadequate placement of support structures at critical locations, which remains the leading cause of print failures. This study proposed a novel algorithm for accurate detection of island regions known to be critical areas requiring support structures. The algorithm can compare loops on two consecutive layers derived from STL files. In contrast to conventional GPU-based image comparison methods, our proposed CPU-based algorithm enables high-precision detection independent of image resolution. Experimental results demonstrated the algorithm's efficacy in enhancing the reliability of 3D printing processes and optimizing automated workflows. This research contributes to the advancement of smart manufacturing by addressing a critical challenge in the automation of additive manufacturing processes.

This paper shows results of research on transparent electrode manufacturing processes using thermal imprinting and IPL technique. By using an IPL process instead of the existing heat sintering process, the sheet resistance value was reduced to about 1/ 10. Additionally, sintering time could be reduced from 1 hour to 1 ms. As a result of measuring the transmittance to determine the excellence of the transparent electrode produced in this way, it was confirmed that it had a high transmittance of 94.4% compared to the substrate with a very high bending stability compared to the existing ITO transparent electrode. These results show that the transparent electrode manufacturing method proposed in this study is very useful.

Predicting elastic modulus of a porous structure is essential for applications in aerospace, biomedical, and structural engineering. Traditional methods often struggle to capture complex relationships between material properties, design variables, and mechanical behavior. This study employed artificial neural networks (ANNs) to predict the elastic modulus of a porous structure based on various material and design parameters. An ANN model was trained on a dataset generated via finite element analysis (FEA) simulations, covering diverse combinations of material properties and design variables (e.g., porosity, structure types). The model demonstrated high accuracy in predicting the elastic modulus on a separate test dataset. Key findings included identification of significant design variables influencing the elastic modulus and the ANN model"s ability to generalize predictions to new data. This approach showcases that ANN is a powerful tool for designing and optimizing porous structures, providing reliable mechanical property predictions without extensive experimental testing or complex simulations. The proposed method can enhance design efficiency and pave the way for developing advanced materials with tailored mechanical properties. Future research will extend the model to predict other mechanical properties and incorporate experimental validation to verify ANN predictions.

Additive manufacturing (AM) technology, also known as 3D printing, is a highly promising technology that can drive innovation in various industrial areas, including the nuclear industry. Although the nuclear industry is traditionally conservative when it comes to adopting new technologies, it is crucial that AM technology is eventually applied for a variety of reasons. To overcome the barriers that currently hinder the adoption of AM in the nuclear industry, it is essential to ensure the reliability of AM products. One key factor is ensuring that AM products have mechanical properties equivalent to those of traditionally manufactured products. This paper presents the results of mechanical property tests conducted on additive manufactured specimens of stainless steel 316 L after heat treatment. We performed tensile tests, hardness tests, and microstructure analysis on specimens produced using two types of metal AM technologies: powder bed fusion (PBF) and directed energy deposition (DED). The results of the tests indicate that certain weaknesses, such as anisotropy and brittleness, in AM products can be improved through three types of heat treatments. In particular, AM products produced using the PBF method and subjected to heat treatments show potential for application in the nuclear industry in terms of materials.

Recent advancements in additive manufacturing (AM) have made it possible to create compact heat exchangers (HXs) with complex geometries. This study introduces a new approach that uses Triply Periodic Minimal Surface (TPMS)-based designs for HXs. Mathematical filtering techniques are incorporated to optimize the local morphology changes. The goal of the proposed mathematical filtering method is to improve the flow characteristics and heat exchange capability of TPMS HXs by modifying the structure’s morphology at the inlet and outlet regions. This modification facilitates flow selection and reduces pressure drop. The HX design includes cylindrical flow domains at the inlet and outlet regions. Three different HX designs with varying inlet/outlet domains (through-hole, half-hole, and taper-hole) were fabricated using polymer AM and DLP 3D printing. These designs were then tested for pressure drop. Among the three designs, the taper-hole configuration showed the best flow characteristics, with a 50% reduction in pressure drop compared to previous studies. The taper-hole design was then replicated using metal AM technology, resulting in a 70-125% improvement in heat exchange capacity compared to previous studies.