This study proposed a conditional generative adversarial network (cGAN) model for predicting steel plate deformation based on heating line positions in a line heating process. A database was constructed by performing finite element analysis (FEA) to establish relationships between heating line positions and deformation shapes. Deformation shapes were converted into color map images. Heating line positions were used as conditional labels for training and validating the proposed model.
During the training process, generator and discriminator loss values, along with MSE and R² metrics, converged stably, demonstrating that generated images closely resembled the actual data. Validation results showed that predicted deformation magnitudes had an average relative error of approximately 3% and a maximum error of less than 7%. These findings confirm that the proposed model can effectively predict steel plate deformation shapes based on heating line positions in the line heating process, making it a reliable predictive tool for this application.

With rapid growth of the global electric vehicle market, interest in the development of secondary batteries such as lithium batteries is also increasing. Core functional parts of secondary batteries are known to determine the performance of these batteries. Micro cracks, scratches, and markings that may occur during the manufacturing process must be checked in advance. As part of developing an automated inspection system based on machine vision, this study optimized the design of a linear feeder exposed to an environment with a specific operating frequency continuously to transfer parts at a constant supply speed. Resonance can occur when the natural frequency and the operating frequency of the linear feeder are within a similar range. It can negatively affect stable supply and the process of finding good or defective products during subsequent vision tests. In this study, vibration characteristics of the linear feeder were analyzed using mode analysis, frequency response analysis, and finite element analysis. An optimal design plan was derived based on this. After evaluating effects on vibration characteristics for structures in which vibrations or periodic loads such as mass and rails were continuously applied, the shape of the optimal linear feeder was presented using RSM.

This study analyzed acoustic emission (AE) signals generated during ultrasonic machining of SiC cathodes and evaluated classification performances of various machine learning models. AE data were collected in both waveform and hit formats, enabling signal characterization through statistical analysis and frequency domain examination. Various machine learning models, including XGBoost, KNN, Logistic Regression, SVM, and MLP, were applied to classify machining states. Results showed that XGBoost achieved the highest classification accuracy across all sensor positions, particularly at the upper part of the worktable with an accuracy of 98.35%. Additional experiments confirmed the consistency of these findings, highlighting the influence of sensor placement on classification performance. This study demonstrates the feasibility of monitoring AE-based machining state using machine learning and emphasizes the importance of sensor placement and signal analysis in improving classification accuracy. Future research should incorporate defect data and deep learning approaches to further enhance classification performance and process monitoring capabilities.

Pinhole-free ionic conductors are critical to achieve optimal performance in thin film-solid oxide fuel cells (TF-SOFCs). However, nanoscale defects, especially pinholes, can induce current leakage and contribute to cell failure by creating electrical short circuits. This study introduced a novel methodology for detecting pinholes in yttria-stabilized zirconia (YSZ) thin-film solid oxide electrolytes. The approach utilized selective adsorption of silver (Ag) nanoparticles generated via a spark discharge generator (SDG). Analytical techniques, including focused ion beam (FIB), scanning electron microscopy (SEM), and transmission electron microscopy (TEM), were employed to investigate interactions between Ag nanoparticles and nanoscale defects. Results showed that nanoparticle-based diagnostic methods were efficacious for defect characterization, offering a solution for enhancing the quality of thin-film electrolytes.

In this study, we fabricated and investigated the polymer-based cylindrical flow sensor for two-dimensional (2D) detection. The flow sensor was the drag force type flowmeter which was fabricated with ecoflex. It had CNT/PDMS as the piezoresistive material and a cylindrical shape to measure the 2D flow. It also had impact resistance and ease of fabrication due to its polymer-based sensor. At first, two piezoresistive parts were applied to evaluate detection properties. Forces from various direction were applied. Results showed its potential as a sensing device. Following this, the final flow sensor was fabricated with four piezoresistive parts and its sensitivity was measured in the air flow from 0 to 30 m/s. Resistance changes were measured while rotating the sensor. Outputs showed a form of sine waves. Data were repeatedly collected under various conditions. The direction and air flow rate were then determined. To check physical impact resistance, a sudden high air flow rate with 100m/s was applied to the sensor and a stable output was obtained. These results suggest that such ecoflex-based cylindrical flow sensor can be used as a 2D flow rate sensor.

This paper deals with the current technology status and technology development direction on shape shifting drone. A shape shifting drone is defined as a drone for which its shape and/or function of its platform in flight can be changed by shape shifting technology in order to fulfill a variety of missions effectively in harsh mission environment. A shape shifting drone can be classified as a rotary-wing based, a fixed-wing based, or a biomimetic based shape shifting drone. This work describes technology trends of domestic and foreign countries. It identifies core technologies and development direction. This work will be useful for planning research and development programs on required technology for the development of shape shifting drone in the future.

The propulsion system of a projectile is very important for the aerospace industry. To perform space exploration mission, controlling position and posture of the projectile in the terminal stage is very important. In this study, a new lateral thrust system is proposed to control the position and posture of the projectile at the terminal stage. Based on nozzles in a lateral thruster, a high-speed projectile can instantly change its position and posture. After changing its position and posture, reverse thrust is generated to control unnecessary movements for stabilizing. Based on various tests, the operation and performance of the nozzle opening device (NOD) of the separation mechanism were validated. As a result, excellent reproducibility was confirmed with standard deviation of 0.057 ms for the time from the end of igniter operation to the start of NOD separation. The internal pressure of the chamber and NOD separation time were inversely proportional to each other with a linear relation. The internal pressure of the chamber and flight speed of NOD were also proportional to each other. The flight speed of NOD was 37.53 m/s at the maximum expected operation pressure (β), 30.26 m/s at 0.5 β, and 17.05 m/s at 0 psi.

Due to their structural properties, nanopatterns are actively used in various fields. In the semiconductor industry, the importance of analyzing the uniformity of nanopatterns is becoming increasingly important. New analysis methods are needed. The elliptical Fourier descriptor (EFD) method can quantify the shape information into frequency components by Fourier transforming contours. In this study, shape analysis of nanopatterns was performed using EFD. Nanopatterns with a period of about 400 nm were formed using laser interference lithography. EFD coefficients were then compared. Results of the analysis showed that the variation between coefficients of poorly shaped patterns was larger than that of normal patterns, confirming the possibility of quantitative comparison. However, further research is needed to establish a clear correlation between coefficient changes and quality changes. In the absence of a standard for geometrical changes in nanopatterns, it is expected that EFD can be applied as a methodology to provide new quantitative indicator.

Hydrogen gas sensors are essential for industrial safety, environmental monitoring, and the energy sector. As hydrogen infrastructure expands and hydrogen fuel cell vehicles become more widespread, precise detection of hydrogen, which has a wide explosive range, has become increasingly critical. To ensure accurate detection of hydrogen in real-world conditions, sensor technologies must offer high sensitivity, stability, and reproducibility, along with cost-effectiveness, fast response time, and compact design. This study introduces a hydrogen gas sensor based on pressure analysis principles. This sensor was developed to quantitatively evaluate hydrogen uptake, diffusion behavior, solubility, and release characteristics in polymers under high-pressure conditions. Experimental results demonstrated the sensor’s excellent performance, with a stability of 0.2%, a resolution of 0.12 wt·ppm, and a measurement range of 0.12 to 1500 wt·ppm, all within 1 second. Furthermore, the sensor's sensitivity, resolution, and detection range could be tuned to suit different operational environments. Uncertainty analysis showed an expanded uncertainty of 8.8%, confirming the system’s capability for real-time hydrogen detection and characterization. This sensor technology is well-suited for applications in hydrogen refueling stations and fuel cell systems, contributing to the advancement of a safe hydrogen society.