The semiconductor industry is experiencing significant growth in production scale and investment, driven by rising demand for generative AI, high-performance computing (HPC), high-bandwidth memory (HBM), and high-performance/high-density chips. As a result, precision inspection and yield management at the wafer stage have become critical challenges. Probe cards, essential for verifying a chip's electrical performance, play a vital role in yield management. However, during repetitive inspection processes, probe cards absorb heat from the underlying chuck, leading to probe tip-pad alignment errors that degrade contact reliability and measurement accuracy. This situation necessitates a quantitative evaluation system based on thermo-structural coupled analysis. Additionally, the modeling process for multiple interposers and interposer housings, along with the preprocessing of contact conditions, adds complexity due to the increasing number of contact surfaces. This complexity can result in convergence issues and reduced accuracy. To address these challenges, this study employs Ansys Parametric Design Language (APDL) to enhance interposer and housing modeling, as well as contact problem resolution. It introduces a novel meshing method that positions nodes at target coordinates using point clouds, providing an effective analysis approach applicable to large, high-density probe cards and thermo-structural problems involving numerous contacts.

Methane thermal decomposition is a promising technology for producing CO₂-free hydrogen. This study experimentally examines how temperature (1,000–1,400^oC) and residence time affect methane decomposition in a ceramic tubular reactor. The results show that both the methane conversion rate and hydrogen yield increased with temperature, reaching approximately 95% and 45%, respectively, at 1,400^oC. At lower temperatures (1,000–1,200^oC), residence time had a significant impact, while at higher temperatures (1,300–1,400^oC), temperature became the predominant factor. Additionally, the formation of C₂ hydrocarbons, particularly acetylene (C₂H₂), increased as residence time decreased, negatively affecting both methane conversion and hydrogen yield. Analysis of the solid carbon by-products identified two distinct forms: amorphous, spherical carbon black particles and a semi-graphitic, crystalline carbon film. These findings provide essential data for optimizing the conditions of methane thermal decomposition.

Wearable sensors are susceptible to degradation from physical wear, moisture, and desiccation, which can result in signal attenuation and unreliable data. This pilot study, conducted in a controlled single-participant setting, introduces a framework to quantify and characterize sensor degradation while restoring corrupted electromyography (EMG) signals. Four types of sensors—polyethylene terephthalate film, parylene film, 3M bioelectrode pads, and microneedle patches—were affixed to the left forearm in a three-electrode EMG configuration. Impedance at 100 Hz was monitored as an indicator of sensor aging, while a one-dimensional convolutional autoencoder was employed to reconstruct degraded EMG signals using a loss function that incorporated both time-domain and frequency-domain error terms. The reconstruction loss showed a correlation with impedance changes, providing a practical metric for assessing sensor health. These findings highlight the feasibility of real-time signal recovery and its potential to extend the lifespan of sensors.

Alcohol acts as a central nervous system depressant and is classified as a psychoactive drug that impairs cognitive alertness and motor coordination. Driving after alcohol consumption slows reaction time in emergency situations and increases the risk of collisions. Although various technologies have been developed to measure alcohol concentration, many suffer from limitations such as sensitivity to environmental factors (e.g., temperature and humidity), hygiene concerns, and the need for periodic calibration. This study proposes an optical method for measuring alcohol concentration using near-infrared (NIR) spectroscopy. Statistical analyses were conducted across multiple wavelength regions to identify wavelengths with significant correlations to alcohol concentration. As a preliminary step, single alcohol solution samples were prepared using distilled water and ethanol. The optical properties of the samples were analyzed in the NIR wavelength range, and statistical indicators including the coefficient of determination (R²), p-value, and coefficient of variation (CV) were evaluated. The results identified specific wavelengths with statistical significance, and the application of mathematical modeling at these wavelengths enabled accurate estimation of alcohol concentration. This approach demonstrates the potential for non-invasive alcohol concentration measurement while minimizing hygiene and infection-related concerns for users.

This study assessed the accuracy and reliability of a 2D image-based deep learning algorithm for posture analysis by comparing it with a 3D motion capture system. Twenty healthy adult males participated, and nine balance parameters were measured using both methods: body tilt (ML/AP), shoulder tilt, pelvis tilt (ML/AP), knee tilt, left/right varus/valgus, and forward head posture. We evaluated agreement and reliability using root mean square error (RMSE), mean absolute error (MAE), Pearson correlation coefficients, and intraclass correlation coefficients (ICC). Most parameters exhibited RMSE and MAE within 3°, while forward head posture, pelvis tilt (AP), and varus/valgus had errors below 10°. High correlations were found for shoulder tilt (r = 0.886) and forward head posture (r = 0.681), whereas knee tilt and left varus/valgus showed lower correlations due to methodological differences. Both methods demonstrated high repeatability (3D: ICC > 0.90, 2D: ICC > 0.80), with moderate-to-high agreement between methods (ICC ≥ 0.5 for most parameters). Shoulder tilt (ICC = 0.919) and forward head posture (ICC = 0.799) showed particularly high agreement. These findings indicate that 2D image-based posture analysis can provide accurate and reliable assessments comparable to 3D motion capture, presenting a more accessible and cost-effective alternative for posture evaluation in clinical and research contexts.

Multi-Agent Path Finding (MAPF) is an algorithm designed to identify collision-free paths for multiple agents, commonly used in fields like robotics and drone navigation. Conflict-Based Search with Continuous Time (CCBS) is particularly beneficial for real-world applications due to its capability to find paths in continuous time; however, it often experiences lengthy computation times. Although techniques such as prioritizing conflicts (PC), disjoint splitting (DS), and high-level heuristics have been implemented to reduce these times, challenges remain. To address these issues, this paper introduces methods to improve space utilization by calculating agent congestion. By optimizing space usage, we can identify paths that avoid potential collisions, even when those paths share the same cost. We propose enhancements to high-level heuristics, conflict prioritization, and low-level heuristics, as well as a method for calculating congestion in continuous time. These improvements lead to a reduction in agent collisions and a decrease in high-level expansions, resulting in a 30% increase in computational success rates compared to the existing CCBS. Incorporating space utilization into the search process significantly enhances MAPF performance.

The field of tissue engineering requires versatile scaffold fabrication technologies capable of inducing cell proliferation and differentiation to promote functional tissue regeneration. Traditional fabrication methods face inherent trade-offs among production speed, resolution, and cost, which hinder their ability to replicate the intricate hierarchical structures of biological tissues. To address these challenges, we developed a mask projection photolithography system with variable optical magnification. This system allows for precise control of the microscale feature size in the final product using a single mask, by adjusting the optical magnification with interchangeable objective lenses and a relay lens. With this system, we successfully fabricated porous scaffolds with reproducible pore sizes ranging from 25 to 100 μm, exposing a Poly (ethylene glycol) diacrylate (PEGDA, Mn = 700) hydrogel precursor solution through a honeycomb-patterned mask for durations of just 3 to 10 seconds. The mask projection system presented in this study offers a powerful and efficient platform for creating the microstructures essential for various advanced biomedical applications, including tissue engineering, drug delivery, and organoid-on-a-chip, thanks to its unique combination of speed, precision, and cost-effectiveness.

The practical application of Raman spectroscopy is often constrained by its low signal sensitivity, particularly for low-concentration liquid samples. This study introduces a straightforward platform that enhances Raman signals by physically concentrating analytes, providing an alternative to complex substrate fabrication and chemical treatments. We employed a femtosecond pulse laser to create functional micro-grid patterns on a silicon (Si) substrate. This laser process induces localized ablation and simultaneous oxidation, resulting in three-dimensional, hydrophilic microstructures of nonstoichiometric silicon oxide (SiO_2-x). These grid structures effectively confine aqueous sample droplets through a pinning effect, functioning as a microwell array that traps and concentrates suspended polystyrene (PS) particles. This physical concentration mechanism achieved a notable signal enhancement, with a maximum factor of 5.2 for PS particles, without the need for sample dehydration. This work presents a simple, cost-effective, and highly reproducible alternative to conventional SERS for analyzing low-concentration liquid samples, demonstrating strong potential for integration into microfluidic systems.

The design of the extrusion die significantly affects both the extrusion process and the quality of multi-lumen tubes. Traditional design methods that rely on trial and error tend to increase manufacturing time and costs while diminishing product quality. This study utilizes inverse extrusion simulation and optimization to design the extrusion die without the need for trial and error. The inverse extrusion simulation generates the die profile necessary to achieve the desired extrudate shape. Subsequently, direct extrusion simulations are conducted to predict the extrudate profile based on the derived die. The optimal volumetric flow rates of air within the lumens are also identified to ensure the extrudate meets the target profile. The results from the direct extrusion simulation, combined with optimization, confirm that the designed extrusion die can successfully produce the target profile. Using the derived die, the multi-lumen tube with the desired specifications is successfully extruded. This design and manufacturing approach enhances both the quality and productivity of multi-lumen tubes.

Military shelters contain various electronic devices that generate significant heat during operation due to their high power output. This heat buildup can degrade the performance of the equipment and shorten its operational lifespan. In high-temperature environments, overheating can lead to serious malfunctions in communication systems or information management platforms, jeopardizing the efficiency and reliability of military operations. Conversely, in low-temperature or high-humidity conditions, condensation may form inside the shelter, increasing the risk of physical damage to electronic components. Such damage can significantly compromise the reliability and durability of the equipment, raising the likelihood of system failure. This study proposes using various environmental control systems, including heating, ventilation, and air conditioning (HVAC) units and air ducts, to mitigate the adverse effects of temperature and humidity fluctuations within military shelters. To achieve this, thermal analysis models were utilized to evaluate and verify the performance of these systems. The analysis specifically examined the heat output of individual devices to determine if the proposed control systems could effectively maintain optimal operating temperatures within the shelter. The results of this study aim to provide a valuable foundation for designing environmental control systems that ensure thermal stability in military shelters.

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 study examines the porosity behavior during the directed energy deposition (DED) of dissimilar metals S45C and H13. We analyzed the effects of deposition parameters, including laser power, feed rate, and powder characteristics, on pore formation, taking into account the unique properties of these metals. Our findings indicate that laser power is the primary factor influencing porosity. At a low power of 200 W, insufficient energy input, along with differences in thermal conductivity and chemical composition between S45C and H13, led to incomplete melting and lack-of-fusion, resulting in high porosity. As the laser power increased to 400-600 W, the melt pool stabilized, enhancing interfacial bonding and significantly reducing porosity. However, at an excessive power of 800 W, rapid melting and solidification of the powder caused gas entrapment and pore formation, which increased porosity, particularly due to the differing thermal conductivities of S45C and H13. Therefore, our results suggest that maintaining an adequate laser power of 400-600 W is essential for achieving a stable melt pool and minimizing porosity in the DED process for dissimilar S45C and H13 metals.

Cable chains are essential in the semiconductor industry for preventing the twisting or sagging of moving cables. They can be broadly categorized into two types based on their fastening methods, with rivet-based assembly being the most common. An alternative method utilizes integral locking features without rivets, which simplifies manufacturing and reduces production costs. However, integral cable chains are more susceptible to breakage during assembly, limiting their use in various industrial environments.This study introduces a structural design approach aimed at minimizing localized stress during assembly while ensuring the cable chain meets the required retention force. Design variables were selected from the modifiable features of the integral cable chain. Through sensitivity analysis, we identified key variables that significantly influence the retention force, which allowed us to reduce the number of design iterations. By employing finite element analysis and response surface methodology, we derived an optimal shape that achieved the target pull-out force and resulted in a 9.7% reduction in assembly stress compared to the original design.

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.

The Split Hopkinson Pressure Bar (SHPB) experiment is commonly employed to assess the dynamic mechanical properties of materials under high strain-rate conditions (10²-10⁴ s-¹) through the propagation of elastic stress waves via pressure bars. The precision and dependability of SHPB measurements are heavily influenced by the alignment of the specimen with the bars. Misalignment can lead to flexural vibrations, causing waveform distortion and undermining the assumption of one-dimensional stress waves. While previous research has explored the impact of misalignment on waveform characteristics, pinpointing the specific sources of distortion from measured signals remains a challenge. This study introduces a machine learning-based classification method that extracts features from distorted SHPB waveforms to identify the type of misalignment. Incident wave signals under various misalignment scenarios were simulated using the commercial finite element software LS-DYNA, and the extracted features were utilized to create a training dataset. Several machine learning models, including XGBoost, were trained and evaluated, with XGBoost yielding the highest accuracy and F1-score. The trained model was then applied to experimentally measured distorted waveforms to validate its effectiveness. This proposed approach facilitates the automated diagnosis of distortion sources in SHPB data, reducing the need for manual interpretation and improving analysis efficiency.