This study outlines a structural design process for a cylindrical superelastic shape memory alloy (SMA) ligation clip. Although polymer-based clips are widely used, they face challenges related to long-term stability and limited radiopacity, highlighting the necessity for metal clips. By systematically modifying two key design variables—the hole offset ratio and the cut-off ratio—the proposed clip effectively reduces excessive stress concentration and enhances superelastic behavior. Finite element analyses indicate that the stress deviation in the two cross-sectional deformation regions decreased by 83.9%, and the martensitic transformation remained confined to a small area, demonstrating robust strain recovery within the superelastic range. In conclusion, the improved SMA clip successfully withstood internal pressures exceeding 15 psi without leakage, showcasing its superior ligation performance and potential for durable, reliable use in minimally invasive surgical procedures.
Materials such as titanium alloys, nickel alloys, and stainless steels are difficult to machine due to low thermal conductivity, work hardening, and built-up edge formation, which accelerate tool wear. Frequent tool changes are required, often relying on operator experience, leading to inefficient tool use. While modern machine tools include intelligent tool replacement systems, many legacy machines remain in service, creating a need for practical alternatives. This study proposes a method to autonomously determine tool replacement timing by monitoring machining process signals in real time, enabling automatic tool changes even on conventional machines. Tool wear is evaluated using current and vibration sensors, with the replacement threshold estimated from the maximum current observed in an initial user-defined interval. When real-time signals exceed this threshold, the system updates controller variables to trigger tool changes. Results show vibration data are more sensitive to wear, whereas current data provide greater stability. These findings indicate that a hybrid strategy combining both sensors can enhance accuracy and reliability of tool change decisions, improving machining efficiency for difficult-to-cut materials.
In this study, we developed a composite anode support composed of La-doped SrTiO3 (LST) and Gd-doped CeO2 (GDC) using a tape casting process for solid oxide fuel cells (SOFCs). By adjusting the pore former content in the slurry, we constructed a bilayered structure consisting of a porous anode support layer (ASL) and a dense anode functional layer (AFL) with the same material composition. The number of tape-cast sheets was controlled to tailor the overall thickness, and lamination followed by co-sintering at 1250oC resulted in a mechanically robust bilayer. We characterized the microstructural evolution concerning sintering temperature and pore former content using SEM, while XRD confirmed the phase stability of LST and GDC. The measured electrical conductivity at 750oC ensured sufficient electron transport. To enhance interfacial adhesion and suppress secondary phase formation, we introduced a GDC buffer layer and a pre-sintering treatment prior to electrolyte deposition. A full cell with a YSZ electrolyte and LSCF cathode achieved a stable open circuit voltage of approximately 0.7 V and demonstrated continuous operation at 750oC. These findings highlight the suitability of LST-GDC composite anodes as thermochemically stable supports, potentially enabling direct hydrocarbon utilization in intermediate-temperature SOFCs.
This study details the development of a semi-active suspension wheel module for small mobile robots and assesses its dynamic characteristics under various driving conditions through simulation. The wheel module features a low-degree-of-freedom mechanical design and includes a semi-active damper to improve adaptability to different environments. To validate the simulation model, a prototype robot equipped with the wheel module was created, and obstacle-crossing experiments were conducted to measure vertical acceleration responses. The model was then refined based on these experimental results. By employing design of experiments and optimization techniques, the effective range of damping coefficients was estimated. Additionally, simulations were carried out at different speeds, payloads, and obstacle heights to identify optimal damping values and examine their trends. The results indicate that the proposed module significantly enhances driving stability and can serve as a foundation for future control strategies in robotic mobility systems.
This study presents a self-wearable smart personal protective respirator featuring a color-signaling triage system designed to facilitate rapid assessment during large-scale physical disasters. The device enables individuals to wear the respirator, allowing responders to quickly identify critically ill patients through real-time biometric signal acquisition and intuitive LED-based visualization. Clinical triage criteria, developed with input from emergency medicine experts, informed a severity classification algorithm based on heart rate, respiratory rate, body temperature, and posture. To implement this system, an ergonomic head-type respirator prototype was created, integrated with a compact sensor module that includes a photoplethysmography (PPG) sensor, a barometric pressure and temperature sensor, and a combined accelerometer and gyroscope sensor. Additionally, custom sensors were developed: a respiration sensor utilizing nickel oxide nanoparticles patterned by laser, and an ECG sensor made by spraying silver nanoparticles onto a flexible polyimide film and then laser-patterning it into a serpentine shape. The system effectively detects vital signs and visualizes severity levels using color signals. Although field deployment was not part of this study, the prototype demonstrated potential to reduce triage time and enhance disaster response efficiency. Further validation in real-world settings is recommended.
Propulsion motors are vital components in marine propulsion systems and industrial machinery, where high torque and operational reliability are paramount. During operation, high-power propulsion motors generate considerable heat, which can adversely affect efficiency, durability, and stability. Therefore, an effective thermal management system is necessary to maintain optimal performance and ensure long-term reliability. Cooling technologies, such as water jackets, are commonly employed to regulate temperature distribution, prevent localized overheating, and preserve insulation integrity under high-power conditions. This paper examines the cooling performance of water jackets for high-power propulsion motors through numerical analysis. We evaluated the effects of three different cooling pipe locations and varying coolant flow rates on thermal balance and cooling efficiency. Additionally, we analyzed temperature variations in the windings and key heat-generating components to determine if a specific cooling flow rate and pipe configuration can effectively keep the winding insulation (Class H) within its 180oC limit. The findings of this study highlight the significance of optimized cooling system design and contribute to the development of efficient thermal management technologies, ultimately enhancing motor reliability, operational stability, and energy efficiency.
Dry adhesives inspired by gecko footpads have garnered considerable attention due to their unique features, including strong yet reversible adhesion, self-cleaning properties, and repeatable use. However, scaling these microstructured adhesives from laboratory fabrication to continuous, high-throughput manufacturing poses significant challenges. In this study, we introduce a stepwise thermal patterning system designed for the scalable production of gecko-inspired dry adhesives on flexible substrates. This automated system combines sequential processes such as plate-to-plate micro-molding, rapid thermal curing, demolding, and roll-up of the patterned film. By raising the curing temperature to approximately 180oC and employing an efficient stepwise imprinting method, we achieve fabrication speeds of up to 150 mm/min without compromising pattern accuracy. The system successfully replicates micropillar structures with a diameter of 15 μm and height of 15 μm, featuring 20 μm mushroom-shaped tips on flexible substrates. The resulting dry adhesives demonstrate stable pull-off strengths of 20-23 N/cm² and retain over 83.5% of their initial adhesion after 100,000 attachment–detachment cycles. These findings highlight the potential of our platform for reliable, high-throughput manufacturing of bio-inspired adhesives, paving the way for various industrial applications such as robotic manipulators, pick-and-place electronic assembly, and wearable devices that require repeated, residue-free attachment.
In this paper, we propose a novel method for controlling the anisotropic sliding behavior of droplets using multiscale hierarchical structures. First, we employed a silicon wafer mold containing micro-pillars and directional micro-line structures to induce the directional sliding of droplets. Additionally, we fabricated micro-cone patterns and integrated them into the structures to precisely control droplet movement. These two structures were replicated in polymer and subsequently fused into a single multiscale hierarchical mold through a partial curing process. The completed multiscale hierarchical surface was then replicated with PDMS to create anisotropy that governs the direction of droplet movement. We experimentally confirmed that the degree of sliding is influenced by the cone pattern. Our proposed structural design demonstrates that anisotropic wettability control is achievable even on surfaces made from a single material, indicating potential applications in various fields such as microfluidics, sensors, and functional surfaces.
Intrinsically stretchable electronics enable seamless integration with dynamic biological tissues and curved surfaces, making them vital for next-generation wearables, biointerfaces, and intelligent robotics. Yet, precise, high-resolution patterning of stretchable electrodes and circuits remains challenging, limiting practical applications. Traditional lithography offers excellent resolution but is hindered by thermal and chemical incompatibilities with soft substrates. Consequently, alternative approaches such as soft lithography, laser-based patterning, printing methods, and electrospray deposition have gained importance. Soft lithography provides an economical, low-temperature option suitable for delicate materials like liquid metals. Laser-based techniques deliver high resolution and design flexibility but require careful parameter tuning for specific substrates. Mask-free printing methods, including direct ink writing and inkjet printing, enable versatile patterning of complex geometries, while electrospray deposition supports precise, non-contact patterning on stretchable surfaces. Collectively, these techniques advance the fabrication of robust stretchable displays, wireless antennas, and bioelectronic interfaces for accurate physiological monitoring. Despite progress, challenges persist, particularly in achieving large-area uniformity, multilayer stability, and sustainable processing. Addressing these issues demands interdisciplinary collaboration across materials science, fluid dynamics, interfacial engineering, and digital manufacturing. This review highlights recent progress and remaining hurdles, offering guidance for future research in stretchable electronics.
This study explores the use of laser ablation technology for creating on-demand shadow masks, which are essential in the fabrication of thin film transistor (TFT) devices. Traditional methods for producing shadow masks often encounter significant challenges, such as high costs, lengthy production times, and difficulties in achieving fine, high-resolution patterns. To address these issues, this study introduces a method for manufacturing shadow masks using fiber laser-based laser ablation. Key laser parameters, including frequency and power, were optimized throughout the research. Systematic experimentation revealed that a frequency of 20 kHz and a power output of 14 W enabled the precise and uniform creation of patterns with a 50 μm channel spacing. When these custom shadow masks were employed in the TFT fabrication process, the resulting devices exhibited stable and reliable electrical performance. The findings suggest that laser ablation-based on-demand shadow mask technology offers a cost-effective and flexible solution for producing large-area, high-resolution TFTs. Additionally, this approach significantly reduces the prototyping cycle, making it ideal for rapid development and iterative testing in research and development environments.
The purpose of this study is to evaluate the deformation behavior of 3D printed specimens using the small punch tensile test method. Traditional tensile tests for assessing mechanical properties require a significant amount of material to produce uniaxial tensile specimens. In contrast, the small punch test method only requires 10 x 10 x 0.5 mm (width x length x thickness) thin plate specimens, providing a substantial economic advantage in specimen sampling and production. This method is particularly beneficial when it is impossible to produce specimens of the same size as uniaxial specimens, as it allows tensile testing with just the minimum sample required. In this study, we utilized fused deposition modeling 3D printing and considered various 3D printing parameters, such as layer height and volume fraction, while manufacturing the specimens. We then compared and analyzed the effects of these variables on tensile strength as measured by the small punch tensile test. Furthermore, we focused on investigating the applicability of this method to the deformation behavior of 3D printed specimens. We also examined the impact of laminating conditions, including layer height, printing speed, and laminating direction, on the failure modes observed after the small punch tensile test.
This study presents a dual-impeller air-cooled heat exchanger aimed at improving thermal management in electro-optical tracking systems operating under high power density. Two geometric modifications were introduced to enhance flow characteristics and heat transfer performance: the curvature of the center plate and the integration of a pin-fin structure at the outlet. Through numerical simulation, the improved model demonstrated more efficient internal flow compared to the original model, achieved through enhanced inflow characteristics and reduced flow separation. The pin-fin structures induced localized turbulence and recirculation zones, contributing to an increased thermal exchange surface area and longer effective heat transfer time. Consequently, the outlet temperature of the internal system decreased by an average of 1.4°C across various rotational speeds, resulting in a 5.9% increase in heat exchanger efficiency compared to the original model. Overall, this study shows that structural enhancements in heat exchanger design can significantly improve the cooling performance of high-power electronic systems, suggesting practical applicability for advanced thermal management solutions.
Detecting and analyzing defects in components or systems is crucial for maintaining high-quality standards in modern manufacturing and quality control. Recently, imaging-based defect detection methods have gained popularity across various engineering fields, highlighting their growing importance. Additionally, the integration of Artificial Intelligence (AI) to improve accuracy and efficiency is rapidly advancing. This paper presents a system that uses imaging to detect holes in CV joint boots, as these holes significantly affect the overall performance and durability of the system. Moreover, it introduces a method for enhancing detection performance by applying AI techniques. Validation tests on actual CV joint boots confirmed that the proposed method improves detection performance.
Improving the interfacial stability between cathode active material (CAM) and solid electrolyte (SE) is essential for enhancing the performance and durability of all-solid-state batteries (ASSBs). One promising method to achieve this is through surface coating with a chemically stable ion conductor, which helps suppress interfacial side reactions and improve long-term cycling stability. In this study, we deposited a uniform LiNbO3 (LNO) protective layer on NCA using particle atomic layer deposition (Particle ALD). This technique utilizes a self-limiting growth mechanism to ensure precise thickness control. We characterized the structural and chemical properties of the coated CAM with X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and scanning electron microscopy-energy dispersive spectroscopy (SEM-EDS), confirming the successful formation of a uniform LNO layer. Electrochemical evaluations revealed that LNO@NCA exhibited significantly improved capacity retention, maintaining 68.1% after 50 cycles at a 1C rate, compared to just 56.5% for the uncoated sample. This enhancement is attributed to the LNO layer's effectiveness in mitigating electrochemical side reactions. These findings demonstrate that Particle ALD-derived LNO coatings are an effective strategy for stabilizing CAM|SE interfaces and extending the cycle life of high-energy ASSBs.
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder marked by the progressive degeneration of motor neurons and muscle atrophy. Despite extensive clinical research, effective treatments remain scarce due to the complexity of the disease's mechanisms and the inadequacy of current preclinical models. Recent advancements in microphysiological systems (MPS) present promising alternatives to traditional animal models for studying ALS pathogenesis and evaluating potential therapies. This review outlines the latest developments in ALS MPS, including co-culture membrane-based systems, microfluidic compartmentalization, microarray platforms, and modular assembly approaches. We also discuss key studies that replicate ALS-specific pathologies, such as TDP-43 aggregation, neuromuscular dysfunction, and alterations in astroglial mitochondria. Additionally, we identify significant challenges that need to be addressed for more physiologically relevant ALS modeling: replicating neural fluid flow, incorporating immune responses, reconstructing the extracellular matrix, and mimicking the pathological microenvironment. Finally, we emphasize the potential of ALS MPS as valuable tools for preclinical screening, mechanistic studies, and personalized medicine applications.
First
Prev
