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.

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.

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.

Dual-drive H-type gantry stages, powered by direct-drive linear motors, are commonly used in precision industrial applications that require high positioning accuracy over a large workspace. The cross-arm rigidly couples the two parallel axes, making strict synchronization control essential for maintaining positioning accuracy. Conventional master-master control treats the coupling effects as disturbances without accounting for the synchronization of the two motors. As a result, its performance deteriorates significantly under non-uniform load distribution, payload rotation, and directly applied torque disturbances, all of which necessitate reliable synchronization control. This study proposes a synchronization control scheme that integrates a sliding-mode controller with an uncertainty and disturbance estimator (SMC-UDE) and a feedforward torque compensator for the inertial torque induced by payload rotation. The proposed controller was implemented on a Hardware-in-the-Loop (HIL) simulator constructed with two parallel voice coil motors, and its performance was experimentally validated on the testbed. The HIL experimental results demonstrate that the SMC-UDE controller with feedforward torque-disturbance compensation achieves a quicker settling time compared to the conventional master-master control strategy. Additionally, it effectively suppresses disturbances generated by a rotary motor attached to a payload, thereby maintaining synchronization accuracy under disturbed conditions.

The knee adduction moment (KAM) is widely used as a surrogate measure of medial knee loading and is a primary target of gait modification. Toe-in and toe-out modifications of the foot progression angle are commonly assumed to reduce KAM peaks by displacing the center of pressure (COP), but this mechanism has not been firmly established. This study therefore aimed to clarify the mechanism underlying KAM reduction during these modifications. Healthy adults walked under normal, toe-out, and toe-in conditions. KAM, the moment arm of the ground reaction force (GRF), GRF magnitude, GRF angle, and COP were analyzed at the first and second KAM peaks. Toe-in and toe-out walking predominantly reduced the first and second KAM peaks, respectively. The moment arm was the primary determinant of both peaks (R² > 0.90). Contrary to conventional assumptions, the reduction in moment arm depended largely on changes in GRF angle, whereas the contribution of COP was minimal or even opposed the change in moment arm. These findings provide a mechanistic basis for foot progression modification strategies and should help improve their clinical effectiveness.

In this work, Mg1-xZnxO thin films were deposited on glass substrates by the pneumatic spray technique at 450°C using a 0.15 M precursor solution of magnesium acetate and zinc acetate. The effect of Zn content (x = 0, 0.3, 0.5, and 0.7%) on the structural, morphological, optical, and electrical properties of the films was examined. XRD analysis showed that all films adopted a cubic MgO structure with diffraction peaks from the (111), (200), and (220) planes, and the crystallite size grew from 8.12 to 12.49 nm as the Zn content increased. SEM images indicated that moderate Zn incorporation improved film homogeneity, whereas higher Zn contents promoted agglomeration and surface roughening. The films transmitted well in the visible region, and the optical band gap widened from 3.56 to 3.93 eV at x = 0.3. The Urbach energy also rose with Zn content, reaching a maximum of 0.695 eV at x = 0.7, which indicates greater structural disorder. FTIR spectra confirmed that Zn incorporation modifies the chemical bonding network. The sheet resistance increased markedly with Zn content, demonstrating the strong influence of Zn doping on the optical and electrical properties of MgO thin films.

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.

This study proposes a hybrid wire arc directed energy deposition (WA-DED) process that uses 3D sand-printed supports to overcome the limitations of low-angle overhang fabrication. WA-DED is a metal additive manufacturing process offering high deposition rates, cost efficiency, and suitability for large-scale components. However, because of its high heat input and molten pool instability, low-angle overhang and hollow structures remain difficult to fabricate, as the molten metal tends to collapse under gravity and thereby degrade geometric accuracy and surface quality. To address this issue, sand-printed supports were introduced. The supports provide tailored mechanical constraint and guide the solidification of the molten pool during deposition. Experiments were conducted to evaluate the feasibility of the proposed process at various overhang angles. The results show that the hybrid process markedly improves deposition stability and enables the fabrication of low-angle and curved overhang structures that conventional WA-DED cannot produce. These findings confirm the effectiveness of sand-supported WA-DED and highlight its potential for industrial applications requiring complex geometries, such as aerospace, marine, and energy components.

This paper describes the calibration of a 2D vision system utilizing a high-precision robot that moves accurately along the z-axis. To achieve this, a one-axis high-precision robot was designed and manufactured, achieving a minimum travel distance of 0.007 μm during testing. The 2D vision system comprised an RGB camera, an LVDT sensor, and a laser sensor for distance measurement. Additionally, a subpixel-based algorithm was developed for the calibration and size measurement of the system. Following the calibration process with the high-precision robot, the size error was found to be within ±0.1 mm when using the LVDT sensor and ±0.2 mm with the laser sensor. Thus, the 2D vision system and calibration algorithm presented in this paper are deemed suitable for accurate object measurement by robots.

This study proposes a deep learning-based image anomaly detection system to identify irregular defects in metallic DC fuses. In the manufacturing industry, product quality control is directly linked to productivity, and appearance-related defects, such as surface imperfections, impact competitiveness and reliability. However, conventional visual inspection and simple image-processing methods have limitations in inspection speed and precision, often struggling to accurately detect complex and diverse irregular defects. To address these challenges, this study employs Dinomaly, an unsupervised reconstructionbased Transformer model that can be trained using only normal images. The proposed method takes images of DC fuses as input, automatically detects anomalous regions, and visualizes the location and morphology of irregular defects through an anomaly map, facilitating intuitive interpretation. Additionally, various data preprocessing techniques, including adjustments for diverse illumination conditions and brightness, are applied to augment the dataset, reflecting real-world environmental variations and ensuring robust performance. Experimental results demonstrate that the proposed approach effectively detects irregular defects in DC fuse data. This study is expected to contribute to the automation of DC fuse quality inspection processes, enhance the reliability of automotive electronic components, and advance quality management in smart manufacturing.

Cooperative 3D printing (C3DP) with multiple robotic manipulators can reduce build time through parallel deposition, but it requires layer partitioning that accounts for collision clearance, workload balance, G-code toolpath compatibility, and interlayer boundary alignment. This study presents a Voronoi- and graph-based layer partitioning framework for C3DP. STL geometry and G-code were integrated into layer-aligned data, and a 65 mm collision clearance was defined from the measured end-effector collision radius as the minimum separation preventing collisions between robots approaching non-adjacent Voronoi cells. Each layer was divided into Voronoi cells so that non-adjacent cells could be treated as collision-free regions. Cell adjacency and toolpath-based processing time were modeled as a weighted graph, and adjacent cells were clustered into workload-balanced task regions. Interlayer seed offsets staggered the partition boundaries, and graph coloring identified regions that could be printed simultaneously.The framework was evaluated by workload-balance simulations and printing experiments. Balance deteriorated when clusters were excessive relative to graph nodes. In experiments, the end-effector separation always exceeded the 65 mm clearance. Partitioned printing reduced the layer printing time from 70.063 to 63.57 min, a 9.3% reduction, and the second layer covered the preceding partition boundary, confirming the staggered-boundary implementation.

Hand-arm vibration exposure is a recognized occupational hazard that can cause discomfort and long-term disorders. While anti-vibration gloves are commonly used to reduce these effects, their effectiveness is often limited by the stiffness of the materials and structural constraints. This study focuses on the development and evaluation of polydimethylsiloxane (PDMS)- based anti-vibration layers with varying internal structures for use in anti-vibration gloves. We prepared three types of PDMS layers: solid PDMS without pores (SPDMS), porous PDMS foam created through a sugar-leaching process (FPDMS), and a hybrid PDMS structure that combines solid and porous layers (HPDMS), all shaped like palms. These PDMS layers were integrated into glove specimens, and their vibration transmissibility was assessed using a measurement system compliant with ISO 10819:2013. Vibration transmissibility was recorded across one-third octave bands from 25 to 1,250 Hz, and frequency-weighted transmissibility values were calculated for both the M- and H-spectra. The results indicate that PDMSbased anti-vibration layers with controlled porosity can be effectively fabricated and incorporated into glove structures, and that variations in internal porosity significantly impact the measured vibration transmissibility characteristics.

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.

With the recent development of 3D printing technology, various 3D printing materials have been developed and used. To utilize 3D-printed products with mechanical parts, studies on friction and wear characteristics according to relative motion between materials are required. However, tribology studies on 3D-printed materials are limited compared to those of the existing materials for mechanical parts. In this study, the frictional and wear characteristics are identified through a reciprocating wear test in non lubricated conditions between the Polylactic Acid (PLA) and Polyethylene Terephthalate Glycol (PETG) printed in the Fused Deposition Modeling (FDM) method. In the wear test between the same materials, the friction coefficient and wear rate were higher in the PLA than in the PETG, and PLA was deposited on the block due to high frictional heat. In the wear test of the PLA block and PETG bump, the wear of the PLA block decreased compared to the wear test between the same materials, but the wear of the PETG bump tended to increase. Therefore, it seems that the 3D-printed PETG may be more advantageous in terms of friction and wear than 3D-printed PLA during relative movement in a non lubricating condition.

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.