Accurate prediction of cutting forces in milling is crucial for intelligent machining. However, traditional methods of collecting discrete data are often time-consuming and can lead to overfitting due to data sparsity. This study introduces a continuously variable data collection strategy designed to efficiently gather training data for machine learning models. By linearly varying the feed rate and radial depth of cut along a single tool path, we obtained continuous data that reflect the variations in machining conditions. Additionally, we applied trigonometric feature transformation to the tool rotation angle to maintain its periodic characteristics. The collected data were used to train Artificial Neural Network (ANN), Support Vector Regression (SVR), and Gaussian Process Regression (GPR) models. The results demonstrate that models trained with continuous data exhibit improved generalization performance under unseen conditions compared to those trained with discrete data. Furthermore, comparable predictive accuracy was achieved using a subset of the continuous dataset. These findings suggest that the proposed approach enhances data efficiency in model training and has the potential to reduce the experimental effort required in developing machining monitoring systems.

This study investigates the ability of mechanical polishing to enhance the surface properties of sculpting clay ceramics without the use of glaze. To accomplish this, a ceramic tile machining system capable of efficient grinding and polishing was developed and utilized. Through experiments conducted under various conditions, key processing parameters— including tool rotational speed, applied pressure, and feed rate—were determined experimentally, resulting in significantly improved surface quality of the ceramic specimens. Analyses of glossiness, surface roughness, and contact angle confirmed notable enhancements in surface characteristics after polishing. These results demonstrate that a mechanically polished surface can achieve quality comparable to, or even surpassing, that obtained through traditional glazing processes, suggesting the potential to replace conventional glaze coatings with a mechanical polishing approach.

The damping ratio is a crucial modal property that influences the sound duration and acoustic quality of traditional Korean bells. This study presents a high-precision measurement methodology utilizing non-contact acoustic resonance excitation and lightweight piezoelectric patches, specifically designed to address the limitations of conventional contact-based methods. Traditional accelerometers introduce significant errors due to mass loading and cable stiffness; in contrast, the proposed method employs a lightweight piezoelectric patch combined with selective acoustic resonance to minimize parasitic damping. Specimens with varying tin and silicon content were fabricated and tested. Verification results indicate that the coefficient of variation for the measured damping ratio decreased from 24% (observed with the conventional method) to less than 10%, demonstrating improved repeatability and precision. Notably, the damping ratio reaches a minimum at approximately 13 wt% tin, providing scientific validation for the potential optimized chemical composition of the Divine Bell of King Seongdeok. Furthermore, the damping ratio was observed to decrease as the resonance frequency increased from 110 to 245 Hz. This research establishes a technical foundation for accurate acoustic simulation and the modern restoration of traditional metallic musical instruments.

Large optical mirrors for space applications require accurate surface evaluation between successive corrective machining steps. Without proper compensation for self-weight, gravitational deformation can affect the measured surface figure and thereby reduce the reliability of the machining process. In this study, an air-cylinder-based gravity compensation device was developed to maintain the target support loads for large optical mirrors. A proportional–integral–derivative (PID) controller with load-cell feedback was implemented, and the initial gains obtained using the Ziegler–Nichols method were retuned on the basis of the measured system response. Single-axis experiments showed that the retuned controller met the specified performance requirements for overshoot, settling time, and steady-state error, even when applied to air cylinders with different spring reaction forces. The validated control method was then applied, via a sequential multi-channel control scheme, to a five-axis gravity compensation system to assess its applicability to multi-point gravity compensation. The experimental results confirmed stable convergence to the target support loads and effective load regulation at steady state, demonstrating that the proposed system is suitable for the multi-point gravity compensation of large optical mirrors.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.