Hair-like surfaces in nature consist of high-aspect-ratio fibers with diameters below 100μm, falling to several tens of micrometers in softer hairs. These fine fiber arrays govern tactile softness, flexibility, surface texture, and mechanical response. Conventional fiber-spinning methods produce fine fibers effectively but offer limited control over the position, direction, and patterned arrangement of individual fibers. Here we propose a fused deposition modeling (FDM)-based strategy that combines melt extrusion with geometric drawing to fabricate PLA hair-like fibers. PLA melted fully at the processing temperature of 250oC, well below the thermal degradation onset near 330oC. DSC analysis showed that faster cooling suppressed thermodynamic crystallization, indicating that the final fiber structure is governed by drawing history and rapid cooling rather than by increased crystallinity. As the printing speed increased, the fiber diameter decreased nonlinearly, following D ≈ 106.3 v^-0.45, in excellent agreement with the D  v^-0.5 scaling predicted by the continuity equation. Tensile strength and modulus increased with printing speed, whereas elongation and toughness decreased, indicating drawing-induced molecular orientation. These results demonstrate that FDM can serve as a programmable platform for fabricating biomimetic hair-like fiber arrays with predictable diameter and mechanical properties.

This study proposes a human-in-the-loop framework that integrates operator observations into a large language model (LLM) to control process parameters for defect handling in fused deposition modeling (FDM) 3D printing. Fully autonomous LLM-based control handles ambiguous sensor data poorly and cannot detect abnormal conditions that lie beyond the installed sensors. Operator observations may compensate for these limitations, but their actual impact on LLM decision-making has not been sufficiently validated. We therefore implemented the proposed framework and defined experimental scenarios involving erroneous parameter injection and environmental disturbances. The framework was evaluated in terms of LLM response quality and print quality. The LLM achieved over 80% response quality on the defined evaluation metrics and generated appropriate parameter adjustments, improving print quality by more than 55% on average. Comparative experiments further revealed that, without operator observations, the LLM sometimes failed to recognize defects. These findings demonstrate the effectiveness of human–LLM collaboration and provide a practical foundation for intelligent FDM process control.

Military shelters house various high-power electronic systems that generate significant heat during operation, making effective thermal management essential for ensuring system reliability and operational stability. This study proposes a one-dimensional (1D) modeling approach for analyzing the thermal behavior of a military shelter–HVAC system using Simcenter AMESIM. The model incorporates key thermal components, such as internal heat sources, shelter structures, and HVAC performance characteristics, to accurately represent dynamic thermal behavior under realistic operating conditions. To validate the proposed 1D model, a comparative analysis was performed using threedimensional computational fluid dynamics (CFD) simulations with ANSYS Fluent, maintaining identical boundary and operating conditions. The analysis focused on the temporal and spatial variations of the internal air temperature within the shelter. Results showed a strong correlation between the AMESIM model and the CFD simulations. This approach significantly reduces computational costs and modeling complexity compared to traditional CFD-based analyses, while still providing adequate accuracy for system-level thermal performance evaluation. Consequently, the developed AMESIM-based 1D model serves as an efficient and reliable tool for the design and performance assessment of military shelter HVAC systems.

The increasing use of computational modeling and simulation (CM&S) in the medical device sector has heightened the need for ensuring simulation credibility. The ASME V&V 40 standard offers a structured framework for assessing credibility, consisting of 23 factors divided into three main categories: Verification, Validation, and Applicability. However, practical guidance for implementing these factors is still scarce. This study systematically reviewed and analyzed ten CM&S-related publications in the medical device field that utilized the ASME V&V 40 framework. It examined how each publication addressed the credibility factors and compared their implementation methods, evaluation criteria, and credibility levels. From this comparative analysis, we developed implementation strategies focused on credibility factors, field-specific characteristics, and model risk levels in real-world regulatory and development contexts. Key considerations for the practical application of each factor were identified, and recommendations for effective implementation were proposed. These findings offer practical guidance for ensuring credibility in CM&S-based medical device development, performance evaluation, and regulatory processes. By clearly demonstrating the applicability of the ASME V&V 40 framework, this work provides valuable direction for related industries and research institutions, aiming to improve CM&S credibility and promote its broader adoption in healthcare.

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.

Digital twin technologies in manufacturing have evolved into dynamic, data-synchronized systems that facilitate real-time monitoring and control. Given that machining involves closely interconnected multi-physics behaviors, the effectiveness of a digital twin largely relies on the accuracy and reliability of its underlying process models. This review systematically evaluates three primary paradigms for machining process modeling in digital twins: physics-based, data-driven, and hybrid approaches. Physics-based models provide interpretability and physical consistency but are hindered by high computational costs and limited adaptability to changing conditions. In contrast, data-driven models offer real-time capabilities and adaptive learning but face challenges related to data scarcity and black-box behavior. Hybrid modeling has emerged as the most promising approach, combining physical laws with machine learning through techniques such as parameter correction, physics-guided learning, and state-estimation-based intelligent control. Recent research demonstrates significant advancements in predictive performance, adaptability, and computational efficiency across various machining applications, underscoring the effectiveness of new process modeling strategies for digital twins. However, challenges remain, including multi-physics integration, model reduction for real-time deployment, and autonomous self-updating in data-limited scenarios. The review concludes that hybrid models present the most viable pathway to achieving high-fidelity, self-adaptive, and trustworthy digital twins for autonomous manufacturing.

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.

The future mobility industry is increasingly utilizing advanced tools for cutting and machining lightweight parts to enhance the fuel efficiency of automotive engines. Machining companies are turning to polycrystalline diamond (PCD) tools to boost productivity in the production of these lightweight components. PCD tools provide exceptional machining performance and a long service life, making them ideal for high-mix, low-volume production, which often involves customized requirements for various materials. To further improve efficiency, this study explores the application of metal 3D printing technology in the manufacturing of PCD tools. This technology allows for the creation of PCD tools with superior cutting performance and wear resistance, tailored for high-speed machining of lightweight materials, including complex shapes. Thus, research into this area is essential. In this study, we manufactured boring tools by brazing PCD tips onto three different laminated structures created using Fused Deposition Modeling (FDM), a method within metal 3D printing technologies. We then evaluated the fabricated boring tools through comparative machining experiments against existing sintered PCD boring tools. The results indicated that the 3D-printed solid tools demonstrated no significant differences in machining accuracy or surface quality compared to the conventional tools.

With recent development of 3D printing technology, its applications to the bio-industry are increasing. Many research studies are being done for manufacturing personalized tablets through this technology in the pharmaceutical process. In this study, to control the dissolution rate of tablets, a lattice structure was inserted into the tablet and the dissolution rate was compared. The tablet proposed in this study can be manufactured by the FDM method, adopting a lattice structure with a large surface area-to-volume ratio. Tablets containing various lattice structures were fabricated using water-soluble PVA filaments and dissolution experiments were conducted in water at 37oC. As a result, it was confirmed that the specific surface area and the mass loss rate were proportional to both the 3D lattice structure and the monolith structure. Among different structures, the diamond structure had the most active dissolution.

In this paper, the relationship between various physical and chemical dynamics included in a gas cutting process was analyzed and a mathematical model was presented. To express the gas cutting process in a formula that could reflect the physics and chemical reaction dynamics, the entire process was classified into three stages: flame spurt, metal oxidation, and metal oxide melting. Flame spurt is caused by combustion of fuel gas and oxygen. It was modeled through fluid dynamics, chemical species transport, and reaction kinetics. Metal oxidation was modeled as a chemical reaction of surface oxidation and oxide growth based on temperature and concentration of species of the metal surface obtained through flame and cutting oxygen spurt results. Finally, the melting of metal oxide was expressed as a rate equation based on melting conditions, heat flux obtained in the previous two stages, and changed properties of the metal. The presented mathematical model could analyze dynamic relationships for each stage of a gas cutting process and connect them into one process. Results of this study can be used as basic data for future finite element analysis and simulations.

The performance prediction and grain burn-back analysis of rocket motor are important steps in the designing of a solid propellant rocket motor. The grain burn-back analysis of the solid grain identifies the burning surface area at each burning step in order to predict pressure-time history of the rocket motor. In this study, the shape of propellant grains was conveniently designed based on a solid modeling program of conventional purpose and the internal ballistics analysis was performed using a Matlab code which was developed to analyze the grain burn-back for this shape model. Upon carrying several analyses for rocket motors, it was confirmed that the developed code is suitable and useful.

This paper reports the effectiveness of the introduction of NGDG-SOFC (Natural Gas-Fueled Distributed Generation Solid Oxide Fuel Cell) as a solution to social problems that could arise in the unification era due to the power shortage in North Korea. Under the actual operating conditions of the plant, a stack that operates at a voltage of 33.87 V and current of 31.24 A was modeled with a gross output of 1.06 kW and a net output of 1.00 kW considering the balance of plant (BOP) consumption power. Considering the average primary energy consumption in the ASEAN countries in 2020, 2,870 MW was estimated as the amount of power generation required in North Korea. Also, the gross area of the plant and the annual fuel cost were estimated. Consequently, it is concluded that the area of 861 km2 which corresponds to 0.71 percent of the gross area of North Korea, and fuel cost of about 1,474 million $/year are required. The introduction of NGDG-SOFC plants is believed to follow the global trend of renewable energy and resolving the power shortage in North Korea in an eco-friendly manner.

Chemical mechanical planarization (CMP) is a wafer planarization process that uses chemical reactions initiated by slurry and mechanical actions by pad asperity. The progression of CMP causes temperature deviation on the pad surface. Increase in process temperature results in increased material removal rate (MRR). So, pad temperature distribution is closely related to With-In Wafer Non-Uniformity (WIWNU). In this study, the pad temperature distribution is modelled from the energy perspective and slurry supply location is suggested to reduce temperature deviation. An energy supplying expression was created by setting the micro area and substituting the applied pressure, relative velocity, and process time. The energy and temperature distributions were observed as quite consistent and the temperature peak matched well with highest friction heat point (HFHP). Based on the model expression, the slurry injection position was set to the center of pad, the HFHP and wafer center, and change in temperature distribution was measured. A comparative analysis was carried out employing the existing method that uses multiple nozzles rather than single nozzles and the deviation was reduced by about 18.5% when slurry was supplied to the HFHP for a single nozzle and by 24.7% when the largest flow rate was supplied for multiple nozzles.

Chemical mechanical planarization (CMP) is a semiconductor process which is necessary for multi-layer interconnection structure. CMP pad is a consumable used in the process and with numerous asperities on the surface that wear out by the load applied from the contact with the wafer. Also, it has a patterned wafer, the step height is gradually removed by contact of the asperities with upper and lower layers. The contact state would be different according to the step height reduction. Likewise, depending on the pattern size at the specific step height, the maximum radius of the asperity curvature differs whether it reaches the down area. In this study, the height distribution of asperities was expressed as a function of time and asperity height taking into account the wear of asperities, and based on the Greenwood-Williamson theory, a mathematical model for material removal rate considering pattern size was derived. The consistency of the novel model is verified with the CMP experiment conducted using oxide patterned wafers, and the experimental data were compared with the residual step height using theoretical removal rate. The root mean square error of the step height reduction was 19.84 nm.

Despite the importance of the usage stage in life cycle assessment (LCA), there is a lack of comprehensive studies on the usage stage modeling. Based on the literature review, this paper establishes a general framework of the usage stage modeling by redefining existing models and proposing new models. The proposed computational framework can provide the overview of the current research as well as lead researchers and practitioners to consider proper modeling techniques. The framework includes the representative usage scenario method, usage context modeling, and time series usage modeling. Also, future research directions are suggested with the proposed computational framework.