This paper presents a method for the real-time detection of pipeline leaks using flexible Acoustic Emission (AE) sensors. The signals gathered from the AE sensor are transformed into RGB images through the application of Mel-spectrogram and color coding. These converted images serve as input for a Convolutional Neural Network (CNN) based on ResNet18. With this approach, both the presence and intensity of leaks in a pipeline can be identified using the AE sensor. The effectiveness of the proposed method was validated through data collected from a testbed featuring a galvanized pipe.

Fretting corrosion results from microscopic abrasion of connector contacts and is influenced by environmental conditions in automotive applications. This study designed and fabricated test equipment capable of evaluating fretting corrosion characteristics at low temperatures. A temperature–humidity environmental chamber was used, and a compact test jig box was created to fit inside it. The specimen was positioned outside the box and fully exposed to low temperatures, while the driving components were enclosed inside the box. To ensure their reliable operation, warm air was supplied using vortex tubes, maintaining the internal box temperature above 0oC even when chamber conditions reached −40℃. A hemispherical-tip jig was also produced to enable consistent specimen preparation. Experiments conducted at −40℃ used a constant current–resistance method to measure output signals. The system successfully captured accurate and stable resistance changes corresponding to displacement cycles. These findings indicate that the developed equipment provides stable low-temperature operation and reliable measurement performance. Therefore, the system is expected to support fretting corrosion characterization across a wide range of environments, including low-temperature, high-temperature, and temperature-cycling conditions.

This study evaluates the structural design and safety of the CanSat in launch environments. The CanSat serves as an educational replica satellite, allowing users to experience the design and operation of small satellites. To ensure stable operation during launch, the structural analysis and design must consider external forces, including vibration and acceleration loads. We determined the material properties for the structure and conducted modal and random vibration analyses, comparing the results with launch environment data from NASA, ECSS, Falcon 9, and Soyuz-2. Additionally, we performed an acceleration load analysis using actual data from CanSat launches during competitions. The modal analysis indicated that the first natural frequency was 65.34 Hz, which exceeds the required threshold. The random vibration and acceleration load analyses further confirmed the structural safety of the design. While the data from NASA and ECSS were conservatively set, reflecting higher vibration intensities, the Falcon 9 and Soyuz-2 launch vehicles provided relatively lower vibration environments due to differences in their designs. Overall, the results demonstrate that the CanSat's structural integrity is maintained under the conditions analyzed for Falcon 9 and Soyuz-2.

Ceiling inspections present challenges due to limited accessibility and structural constraints. To ease the burden on security personnel, who would otherwise need to manually disassemble, inspect, and restore ceiling components, this study proposes a robotic system for detecting hazardous objects within ceiling environments. The proposed system features several key innovations: a hollow-structured track mechanism designed to reduce vibrations from jolting while traversing structural beams and to improve localization accuracy. We optimized the robot’s mass distribution and required drive torque through dynamic simulations to ensure stable mobility in confined ceiling spaces. For effective hazardous object detection, we developed a YOLOv8-Seg-based background learning algorithm that suppresses ceiling-structure patterns, allowing for the identification of unknown objects without prior class-specific training. Additionally, we introduced a frame-based filtering algorithm to enhance detection reliability by reducing false positives caused by motion blur during movement. The system's effectiveness was validated through experiments conducted in a ceiling-structured testbed, demonstrating its capability for accurate hazardous object detection under realistic operating conditions.

In machining operations, dynamometers are typically used to directly measure the forces acting on cutting tools. However, their high cost and complex setup restrict their use to laboratory environments, making them unsuitable for real-time monitoring in general production settings. To overcome this limitation, this study proposes an autoencoder-based learning model for estimating cutting forces using only spindle vibration signals acquired during milling. The model features a deep neural network (DNN) that takes processed spindle vibration signals as input and predicts latent features derived from cutting force signals through an autoencoder. These predicted latent features are then fed into a pretrained decoder to reconstruct the corresponding cutting force signals. To enhance the model's accuracy and robustness, the raw vibration signals sampled at 20 kHz were filtered with a bandpass filter that spans the effective frequency range of 20–2500 Hz, effectively removing irrelevant noise. For validation, an accelerometer was mounted on the spindle head of a milling machine, and vibration data were collected during cutting. The estimated cutting forces were compared to ground truth measurements obtained from a dynamometer. The model achieved a Pearson correlation coefficient of 0.943, demonstrating that reliable cutting force estimation is achievable using only low-cost vibration sensors.

All-solid-state batteries (ASSBs) utilizing non-flammable inorganic electrolytes are gaining significant attention due to safety concerns associated with conventional lithium-ion batteries. Among various oxide electrolytes, lithium lanthanum titanate (LLTO) demonstrates high ionic conductivity at room temperature but is prone to lithium loss at elevated sintering temperatures. In this study, we employed electrostatic spray deposition (ESD) at 250℃, followed by flash light sintering within milliseconds using a xenon lamp. This approach enabled the production of dense and highly crystalline LLTO thin films with minimal lithium evaporation. Scanning electron microscopy (SEM) analysis confirmed reduced porosity at 650V, while X-ray photoelectron spectroscopy (XPS) revealed stable lithium content. Additionally, X-ray diffraction (XRD) indicated the formation of a cubic perovskite structure that is beneficial for ionic transport. This rapid and scalable process shows promise for producing high-quality LLTO electrolytes, thereby enhancing the safety and performance of next-generation ASSBs.

As advanced materials with high hardness, strength, and heat resistance are increasingly applied in fields such as aerospace, semiconductors, biomedical engineering, and mold manufacturing, the demand for high-precision machining technologies is growing. Micro electrical discharge machining (Micro-EDM) has gained attention as a non-contact process that locally melts and vaporizes conductive materials using electrical sparks, allowing for the fabrication of intricate 3D microstructures with high precision. This study analyzes the impact of capacitance in RC-type discharge circuits on the machining characteristics of single discharge craters using aluminum, brass, copper, STS304, and WC-Co. Additionally, we compare the overlapping behavior and morphological evolution of multiple discharge craters across these materials. We investigated the diameter and depth of single discharge craters, as well as the geometrical characteristics of overlapped craters. The results demonstrate the influence of discharge energy and material properties on discharge crater geometry, providing a quantitative basis for analyzing surface morphology in the Micro-EDM process.

This study introduces a straightforward and cost-effective method to enhance the positional accuracy of a 6-axis serial robot using a double ball-bar (DBB). Kinematic errors, a primary source of inaccuracies in offline programming, are estimated and calibrated through circular tests. The kinematics of the robot are modeled using the Denavit-Hartenberg (D-H) convention, and a mathematical relationship between radial deviation and kinematic errors is established. To avoid singularities, identifiable parameters are selected using singular value decomposition. The method involves three steps: measuring the tool center point (TCP) with the DBB, estimating key kinematic parameters, and verifying the calibration results. Redundant or less significant parameters are excluded to concentrate on the most impactful ones. During the process, the robot is commanded to trace a circular path while radial deviations are recorded. This data is then utilized to estimate and adjust the kinematic model. After recalculating and executing the circular path with the calibrated model, a notable reduction in deviation is achieved. This proposed approach requires no additional equipment and provides a quick, affordable solution for improving the accuracy of industrial robots while lowering maintenance costs.

Balloon catheters are a key technology in medical devices, essential for minimally invasive procedures. This study quantitatively analyzes how the orientation characteristics of polymer tubes, influenced by extrusion conditions, affect the mechanical properties and compliance of the final balloon—where compliance refers to the change in diameter under external pressure. Nylon 12 tubes, with a target outer diameter of 1.2 mm and an inner diameter of 1.0 mm, were extruded under six different orientation conditions by varying the screw flow rate and puller speed. The tubes were processed under identical forming conditions, allowing for a consistent evaluation of their mechanical properties. As orientation increased, elongation decreased while yield strength increased, and these trends continued in the balloon, significantly influencing compliance. To quantitatively measure orientation, we introduced the dimensionless Deborah number. We established a curve-fitted experimental model that links extrusion conditions, polymer tube properties, and balloon compliance. This model allows for the prediction of balloon performance based on extrusion-stage parameters, providing a practical framework for process optimization. Overall, this study offers an effective quantitative indicator for forecasting balloon catheter performance based on extrusion conditions and supports the systematic design of medical balloon products.

We present an extrusion-based dispensing system designed for the planar patterning of tungsten ink through direct ink writing. This system achieves uniform ink deposition by precisely controlling the dispensing pressure and the motion of the substrate along predefined writing paths. To assess the impact of pressure on pattern geometry, we fabricated line patterns under various pressure conditions and analyzed their widths and thicknesses. To gain further control over pattern width, we employed an adjacent line overlapping strategy, where several lines, each approximately 200 μm wide, were written with partial overlap. We quantitatively verified the relationship between the number of adjacent lines and the resulting pattern width. This method was also adapted to create planar patterns with complex geometries, including variable widths, curved paths, and discontinuous features. The resulting patterns demonstrated uniform quality and precision. These findings confirm that our proposed system provides a versatile solution for fabricating planar conductive patterns with intricate geometries, suitable for applications in printed electronics and interconnects.

The study examined the flow characteristics within an LNG cargo pump, specifically focusing on how variations in the geometry of the inducer and inducer casing affect pump performance. LNG cargo pumps are essential for transferring LNG from carriers' storage tanks to onshore facilities. The inducer significantly influences the pump's suction performance, making it crucial for efficient LNG transfer. Given that the inducer often operates under challenging conditions, computational fluid dynamics (CFD) analysis was conducted on various geometric configurations. The analyses assessed velocity, pressure, efficiency, head, and pressure loss coefficient. Among the configurations studied, Case 4 exhibited the lowest efficiency and head, although the differences compared to other cases were minimal. Notably, Case 4 demonstrated more uniform pressure distributions and stable velocity profiles. Additionally, its pressure loss coefficients were 34.9% and 10.9% lower than those of Case 1 and Case 2, respectively, indicating enhanced flow stability and reduced energy loss. Overall, within the design parameters of this study, Case 4 emerged as the most optimized configuration for stable LNG transport.

This study examines the charge reduction characteristics of charged particles using a neutralizer to prevent accidents from electrostatic discharge and enhance process efficiency. The research measures the number of charges, elimination efficiency, and penetration rate under various voltage polarity conditions with a DC-type bipolar electrostatic eliminator. The results indicate that electrostatic neutralization is most effective under negative high voltage (-HV) conditions, while the mesh penetration rate increases and charge accumulation occurs under positive high voltage (+HV) conditions. Furthermore, partial charge neutralization is observed under both positive and negative high voltage (±HV) conditions due to the sequential emission of positive and negative ions. This study quantifies the mitigation of electrostatic charge using a neutralizer, offering insights for optimizing filtration systems and improving process stability. Future research will refine electrostatic control mechanisms by considering additional parameters such as particle size, material properties, and flow conditions.

Laser-induced graphene (LIG) fabrication technology, introduced by the James Tour group at Rice University in 2014, has been extensively explored for various applications. These applications include physical sensors such as bending, temperature, and touch sensors; chemical sensors like gas and pH sensors; and energy storage devices, particularly micro-supercapacitors (MSCs). Additionally, theoretical studies utilizing molecular dynamics (MD) simulations have been conducted to investigate the LIG formation mechanism. However, the carbonization and graphitization of organic materials are complex and spatially non-uniform, making complete mechanistic interpretation difficult. Most existing research has primarily focused on chemical and materials science aspects, with practical process optimization using commercial laser systems largely limited to simple variations in laser power and scan speed. There is a lack of systematic studies addressing broader laser-parameter modulation. In this study, we systematically varied laser parameters—including power, scanning speed, pulse width, repetition rate, line spacing, and defocusing—and comprehensively evaluated the resulting electrical, physical, and chemical properties of LIG formed on wood substrates. The results provide insights into how graphene quality varies with laser processing conditions and demonstrate a versatile approach for controlling performance through laser modulation.