3D printing is emerging as a promising solution for the automotive industry, as it offers economic advantages in smallbatch, high-variety production and mitigates climate impact by eliminating mold fabrication. This study compares the carbonemission reduction potential and economic feasibility of fused deposition modeling (FDM)—the most widely used polymer 3D printing process—with those of conventional injection molding at the actual component level. The analysis shows that the environmental burden of mold manufacturing in injection molding is substantial, confirming the advantage of FDM in low-volume production. Specifically, for production volumes below 645 units, FDM performs better in reducing carbon emissions. These findings indicate that FDM can serve as a sustainable alternative for low-volume manufacturing in automotive applications.

This study examined how knee sleeve application affects the knee adduction moment (KAM), knee adduction angle (KAA), and knee muscle activation during gait in eleven healthy individuals. Participants completed walking trials under four conditions: a control condition without a sleeve (Normal) and three sleeve conditions with distinct compression characteristics (Motion, Slim, and Strong). KAM, KAA, and surface electromyography (EMG) from five lower limb muscles were compared across conditions. Neither KAM nor KAA differed significantly among the conditions (p > 0.05). In contrast, knee extensor EMG was lower under specific sleeve conditions than under Normal (p < 0.05). Because muscle forces contribute substantially to knee contact force during gait, this reduced activation may indicate lower internal knee joint loading. These findings suggest that, in healthy individuals, certain knee sleeves may alter neuromuscular strategies without producing detectable changes in KAM or KAA, although further validation is needed.

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.

Variable inlet guide vane (VIGV) control is among the most energy-efficient flow regulation methods for axial pumps because it adjusts the inlet swirl angle directly while preserving high hydraulic efficiency. However, unlike rotational-speed control, whose performance curves scale straightforwardly through the affinity laws, VIGV control alters the intrinsic shape of the head-flow (H-Q) curve at each vane angle and therefore requires angle-specific prediction. This study proposes a transition-informed Gaussian process regression (TI-GPR) model that augments standard GPR by explicitly incorporating the gradient sign-reversal point of the S-shaped characteristic curve through adaptive region splitting and sigmoid-based blending. A four-factor evaluation covering CV strategy, training-data sparsity, input dimensionality, and model type shows that, even when only Q–H data are available (2D input), TI-GPR lowers the relative MAPE by 17.525% under sparse interpolation and by 35.938% under extrapolation relative to the baseline GPR model. Adding valve-position information (3D input) improves the accuracy of both models further, and TI-GPR retains its advantage. These results demonstrate that a minimal structural modification embedding the stability-gradient transition boundary can yield substantial predictive gains, particularly in data-scarce regimes.

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 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 study investigated the ultrasonic fatigue behavior of ABS and of a high-strength photopolymer resin (Rigid Black) fabricated by digital light processing (DLP) additive manufacturing. The dynamic elastic modulus of both materials was measured so that specimens could be designed to satisfy the 20 kHz resonance condition. ABS specimens were CNC-machined, whereas Rigid Black specimens were DLP-printed and post-cured. Thermal effects were minimized by compressed-air cooling with a 0.3 s/3 s duty cycle. S-N curves showed that fatigue life increased as the stress amplitude decreased for both materials. ABS exhibited higher fatigue strength and a more consistent life distribution, which is attributed to its homogeneous microstructure. Rigid Black showed lower fatigue strength with greater scatter, reflecting the anisotropy and interfacial inhomogeneity introduced by layer-by-layer fabrication. Fractographic analysis revealed that ABS underwent mixed-mode ductile-fatigue fracture through crazing, whereas Rigid Black failed in a brittle manner, with directional crack propagation driven by process-induced defects. These results confirm the feasibility of ultrasonic fatigue evaluation for DLP-printed polymer components and provide a basis for assessing the durability of additively manufactured parts.

Demand for high-performance bonded magnets with complex geometries is growing in electric motors, sensors, and energy devices. In lithography-based composite manufacturing (LCM), the magnetic properties of anisotropic NdFeB composites depend strongly on particle alignment during curing. This study compares the magnetic characteristics of 70 wt% NdFeB composites fabricated under magnetic-field-assisted and non-aligned conditions. An in situ alignment module was integrated into the manufacturing platform to induce directional particle orientation. The magnetic flux distribution was analyzed in ANSYS Maxwell, and particle alignment behavior was simulated by discrete element method (DEM) modeling in ANSYS Rocky. Vibrating sample magnetometry confirmed that magnetic-field-assisted processing enhances the anisotropic magnetic response, in agreement with the simulation predictions.

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 nonadjacent 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.

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.

Trunk balance is essential for physical stability, but traditional assessment tools, such as force plates and 3D motion capture systems, can be inaccessible or unsafe for patients unable to stand. This study introduces a seated trunk balance training and assessment system that combines a tilting chair with an Inertial Measurement Unit (IMU). The system features a kinematic design that transmits the user's trunk movements to the chair's seat, allowing for quantitative measurement without the need for body-mounted sensors. A pilot study was conducted to evaluate technical feasibility by comparing a seat-mounted sensor (CS-IMU) with a reference sensor (T12-IMU) attached to the T12 vertebra. Assessment items included Range of Motion (RoM), rotation accuracy, and agility. Results indicated a very high correlation for lateral bending (r = 0.958) and rotation phase angle (r = 0.990), while flexion/extension showed a moderate correlation (r = 0.691). Additionally, agility tasks demonstrated consistent results in the lateral direction (r = 0.806). These findings suggest that the proposed system can effectively quantify multi-dimensional trunk movements in a seated position, making it a valuable tool for high-risk populations, such as patients recovering from acute stroke or spinal cord injuries.

This study quantitatively investigates the deviation between the ideal and effective amplification ratios of a bridge-type displacement amplification mechanism used in ultra-precision positioning systems. It proposes region-specific optimal design strategies to mitigate this deviation. A Leave-One-Out sensitivity analysis reveals that the dominant factor influencing amplification ratio deviation shifts at an ideal amplification ratio of approximately 10. In the high-amplification region (R > 10), displacement loss due to bending of the input link is identified as the primary cause of deviation. Reinforcing the input link's thickness and incorporating a pocket structure reduces the deviation to within 12.4% while minimizing resonance frequency degradation. Conversely, in the low-amplification region (R < 7), the main issue is dynamic performance deterioration caused by oversized intermediate links. Implementing a hexagonal mass-reduction design enhances the first resonance frequency by up to 28% without compromising the amplification ratio. These findings establish differentiated design guidelines based on the target amplification ratio, enabling simultaneous improvements in precision and dynamic performance for bridge-type displacement amplification mechanisms in positioning systems.

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.

Cable chains are essential for guiding and protecting cables in repetitive linear-motion equipment. However, during highspeed operations, inertial effects and structural deformation can lead to position overshoot beyond the intended stroke, resulting in off-path motion and increased stress concentrations in links and joints. This study assesses the structural stability of a U-shaped cable carrier under conditions of position overshoot and suggests an optimized geometry. To analyze this, a nonlinear finite element model is employed, constraining one end of the carrier while applying a prescribed overshoot displacement to the other end. Structural stability is measured using a stability index, which is defined as the maximum reaction force at the point of yielding, when the equivalent (von Mises) stress reaches the material's yield stress. A sensitivity analysis identifies the key geometric design variables, and response surface methodology is applied to find an optimal shape that maximizes the reaction force at yielding. The proposed simulation-driven workflow offers practical design guidance for enhancing the stability of cable carriers during non-ideal overshoot events.

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.