Directed energy deposition (DED) enables the efficient fabrication and repair of large metal components. However, repeated reuse of the powder generates degraded particles, which must be removed by sieving. This study used scanning electron microscopy and laser diffraction particle size analysis to characterize the sieve-rejected Inconel Alloy 939 powder collected sequentially after each of four DED processing cycles (R1–R4). The rejected powder consistently contained elongated, irregular, and satellite-attached particles, and its particle size increased progressively with each reuse cycle. The mean particle size increased by 16.8%, from 51.1 μm for R1 to 59.7 μm for R4. The D₁₀, D₅₀, and D₉₀ values also increased, with D90, for example, rising from 71.5 to 82.2 μm. These results suggest that sieving may serve as an important quality control step for maintaining powder quality when the powder is repeatedly reused in DED.

The growing complexity of steel manufacturing, loss of veteran operators, and demand for carbon-neutral operation make tacit operational knowledge increasingly difficult to sustain. Standalone large language models (LLMs) cannot adequately address terminology mismatch, schema fragmentation, undocumented tacit knowledge, and non-traceable hallucinations in on-site data. This study proposes a domain knowledge graph-based conversational operation assistant integrating a steel-process ontology with expert–LLM dialogue. An eight-class ontology is introduced, featuring the UnfilledSlot class to explicitly represent missing knowledge. A five-phase methodology constructs an initial graph, automatically fills available slots, prioritizes remaining deficiencies, and generates targeted multiple-choice questions for experts. The completed graph supports NL2SQL, analysis, explainable AI, and response generation, while operational logs reveal new knowledge gaps. Applied to a steel continuous casting process with 8,441 slots, automatic filling achieved 62% (5,233 slots), while 600 bundled expert questions resolved 2,702 additional slots, reducing the unfilled ratio from 38% to 6%. Comparisons with a standalone LLM, retrieval-augmented generation, and an ablation without UnfilledSlot, together with expert evaluations across three graph-completeness levels, confirm that explicit deficiency handling improves response reproducibility and traceability.

Omnidirectional gimbal systems use multiple actuators to achieve the desired line-of-sight (LOS) motion through thrust allocation. However, actuator degradation caused by thermal effects or aging alters the force constants, leading to model mismatch and diminished torque realization performance. Conventional pseudoinverse-based thrust allocation assumes constant actuator characteristics and, therefore, cannot effectively compensate for these variations. This paper proposes an adaptive thrust allocation algorithm based on Recursive Least Squares (RLS) for omnidirectional gimbal systems. The proposed method estimates the force constant of each actuator in real-time using measured torque and actuator current data. These estimated force constants are incorporated into the thrust allocation process to address actuator degradation. Additionally, a weighted thrust allocation strategy redistributes current demand to alleviate the burden on degraded actuators while maintaining the required torque. The proposed algorithm was validated through MATLAB/Simulink simulations under conditions of asymmetric actuator degradation. Compared to the conventional pseudoinverse-based method, the proposed algorithm reduced the torque realization RMSE by 99% and the LOS tracking RMSE by 24.5%. Furthermore, the weighted thrust allocation decreased the RMS current of the most degraded actuator by 35.8%, all while preserving torque realization and LOS tracking performance, demonstrating the effectiveness of the proposed method.

Military electronic equipment is often subjected to harsh operational environments characterized by mechanical shocks, vibrations, and rapid temperature fluctuations. Therefore, reliably protecting internal components is crucial. To ensure system reliability, potting materials must provide both mechanical cushioning and environmental stability. While polydimethylsiloxane (PDMS) is a promising option due to its excellent flexibility, chemical stability, and environmental resistance, its low thermal conductivity limits its effectiveness in thermal management. In this study, we aimed to address these limitations by fabricating aluminum nitride (AlN)/PDMS composite potting materials. We evaluated their thermal and mechanical properties based on varying filler loadings. PDMS specimens with different AlN contents (0, 5, 10, and 20 wt%) were prepared, followed by systematic compressive tests and thermal conductivity measurements. The results indicated that both compressive strength and thermal conductivity improved consistently with increased AlN filler loading, achieving up to a 46.7% enhancement in thermal conductivity while reasonably maintaining the inherent elasticity of the elastomer. These findings suggest that AlN/PDMS composites can effectively serve as protective materials that meet the thermal management and mechanical cushioning requirements of defense electronics, providing a foundational guideline for designing military-grade potting materials.

Material extrusion (MEX) is widely used in additive manufacturing due to its simplicity and compatibility with various thermoplastics. While adhesive coating is commonly applied to enhance the watertightness of MEX-fabricated (MEXed) structures, its impact on mechanical properties remains unclear. This study investigated the effect of cyanoacrylate adhesive dip coating on the mechanical behavior of MEXed polylactic acid (PLA) specimens. Tensile specimens were fabricated in both longitudinal and transverse orientations and coated with two commercial cyanoacrylate adhesives, Loctite 401 and Alaska 4001. The coating process was repeated 2, 4, and 6 times, followed by cross-sectional observations and tensile testing. The results indicated that the cross-sectional area increased with an increasing number of coating repetitions, regardless of adhesive type or infill pattern. Tensile strength generally decreased after coating, with reductions of 10.6–14.7% after six coating cycles. In contrast, the elongation at break increased after coating treatment. For Loctite 401, the maximum increases were approximately 35.1% and 124.1% for the longitudinal and transverse specimens, respectively, while Alaska 4001 resulted in maximum increases of approximately 29.7% and 48.3%, respectively. These findings suggest that adhesive dip coating significantly alters the mechanical behavior of MEXed PLA structures and requires optimization to balance strength and ductility.

As interest in personalized biomechanical interventions grows, customized insoles have garnered attention for their ability to improve plantar pressure distribution and foot comfort. While previous studies primarily focused on pressure redistribution, systematic quantitative evaluations across different foot types remain limited. Notably, objective assessment methods that link plantar pressure-based structural metrics with subjective comfort are still insufficient. In this study, customized insoles were fabricated for 32 adults using plantar pressure-based foot shape acquisition and additive manufacturing, and their biomechanical effects were evaluated. The arch index (AI) and subjective comfort were measured before and after one month of use. AI was calculated from static plantar pressure data, and comfort was assessed across multiple foot regions using a visual analogue scale. Statistical analysis revealed significant improvements in comfort at the arch, ankle, and rearfoot regions (p < 0.05). The mean AI deviation from the normal reference value decreased by approximately 30.6%, indicating a shift toward a more normalized arch pattern. Overall, this study demonstrates that customized insoles can enhance plantar contact characteristics and perceived comfort across different foot types. These findings provide quantitative evidence for engineering evaluation and establish a foundation for the objective assessment of personalized insole performance.

Integrating metals and plastics is essential for lightweight structures and efficient assembly in industries such as automotive and IT device manufacturing. This study investigated how laser micro-patterning enhances the interfacial bonding strength between these dissimilar materials in metal insert injection molding. Galvanized high-strength steel and polyphenylene sulfide (PPS) reinforced with 40% glass fiber were used as the metal and plastic materials, respectively. Four micro-patterns—circular, square, diamond, and hexagonal—were machined by laser on the bonding area at the end of the steel sheet. The effect of pattern spacing on bonding strength was evaluated by tensile shear tests on injection-molded specimens. The combined effect of laser processing and plasma surface treatment was also examined. For the square pattern, wider pattern spacing increased the bonding strength 2.2-fold, and for the hexagonal pattern the increase was 1.4- fold. Combining laser micro-patterning with plasma treatment enhanced the bonding strength by factors of 1.4 to 2.3. The effect of plasma treatment was greater for the square and diamond patterns than for the circular geometry.

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.

Product development based on metal 3D printing has been expanding steadily in the manufacturing industry. Powder bed fusion (PBF) is currently the most widely used metal 3D printing method, as it produces parts of complex shape with high precision. Applying PBF to the cutting tool industry could shorten process time relative to conventional sintering routes and reduce the material waste generated during machining. It also allows internal lattice structures and fluid channels to be designed within the tool, facilitating light-weighting and improved cutting performance. In this study, nTop software was first used to design the internal lattice structure of an end mill tool for additive manufacturing and to perform the corresponding simulation analysis. Three configurations were compared, in which the tool lattice was defined either by thickness dimension or by thickness range, and the reinforced results confirmed that none of the designs posed problems for actual additive manufacturing within the software. On this basis, tool bodies were fabricated on PBF metal 3D printing equipment, and their feasibility for tool applications was evaluated.

Strain wave gears are widely used in applications that require high reduction ratios, compact designs, and high positioning accuracy. However, increasing torque capacity remains a key challenge, particularly due to the high stresses at the flex spline tooth root. This study presents the development and investigation of a new strain wave gear variant. The tooth profile is defined using a quadruple-arc geometry with tangential transitions. A two-dimensional tooth engagement analysis evaluates the meshing behavior under flex spline deflection and identifies potential collisions during the design phase. Consequently, an FE simulation was conducted to investigate stress distribution. In the final step, the developed strain wave gear was assessed for overall performance and load capacity. Performance evaluations, including hysteresis loss, showed that the developed strain wave gear performed equally well or better than a leading strain wave gear manufacturer across seven key performance metrics. In terms of load capacity, the maximum torque at 50% failure probability (T50%) reached 106.7 Nm, which is 30% higher than the development target of 82 Nm, thereby confirming the validity of the tooth-profile design.

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.

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.

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.