In this research, a self-reinforced composite material was manufactured using a single polyethylene material, and this self-reinforced composite material has excellent recyclability and is environmentally friendly compared to composite materials composed of other types of material, such as glass fiber reinforced composites (GFRP) and carbon fiber reinforced composites (CFRP). In this research, the manufactured self-reinforced composite material consists of an outer layer and an inner layer. To manufacture the outer layer, low density polyethylene (LDPE) films were laminated on high density polyethylene (HDPE) fabrics and knitted fabrics, and composite materials were prepared at various temperatures using hot stamping. A 3D printing process was utilized to manufacture the inner layer. After designing a structure with a cross-sectional shape of a triangle, circle, or hexagon, the inner layer structure was manufactured by 3D printing high-density polyethylene material. As an adhesive film for bonding the outer layer and the inner layer, a polyethylene-based self-reinforced composite material was prepared using a low-density polyethylene material. Input data for simulation of self-reinforced composite materials were obtained through tensile property analysis using a universal testing machine (UTM, Shimadzu, Kyoto, Japan), and the physical property values derived as output data and actual experimental values were obtained. As a result of the comparison, the error rate between simulation data and experimental data was 5.4% when the shape of the inner layer of self-reinforced composite material was a hexagon, 3.6% when it was a circle, and 7.8% when a triangular shape showed the highest value. Simulation in a virtual space can reduce the time and cost required for actual research and can be important data for producing high-quality products.

Recently, the automobile industry has demanded weight reduction, so research on materials is being actively conducted. Among this research, carbon fiber-reinforced composite materials are being studied a lot in the automobile industry due to their excellent mechanical properties, chemical resistance, and heat resistance. However, carbon fiber-reinforced composite materials have disadvantages, in that they are not free from color selection, and have weak interfacial bonding strength. In this study, a colored epoxy resin was prepared by mixing epoxy—which is a thermosetting resin according to the pigment concentration (0.1, 0.3, 0.5, 1.0 wt%)—and curing shrinkage. Thermal expansion characteristics were analyzed and the concentration of 0.5 wt% pigment showed the lowest shrinkage and thermal expansion characteristics. In addition, to measure the interfacial shear strength (IFSS) of the carbon fiber and the colored epoxy resin, the IFSS was obtained by performing a microdroplet debonding test, and the strength of the pigment concentration of 0.5 wt% was reduced to a relatively low level. Through these experiments, it was determined that an epoxy resin in which 0.5 wt% pigment is mixed is the optimal condition. Finally, using the composite material modeling software (Digimat 2020.0), the representative volume element (RVE) of the meso-scale was set, and interfacial properties of carbon fibers and colored epoxy resins were analyzed by interworking with general-purpose finite element analysis software (Abaqus CAE).

High-viscosity aramid copolymer solutions are widely used in fiber manufacturing and advanced composite applications, but their elevated viscosity poses significant challenges for mixing and agitation processes. This study employs computational fluid dynamics (CFD) simulations to enhance the mixing performance of such systems. Flow behavior around the impeller was analyzed within a cylindrical stirred tank while varying the number of baffles (0, 2, 4, and 6) and comparing two different impeller designs (A and B). Simulation results showed that installing a sufficient number of baffles—particularly four—effectively suppressed swirling flows commonly observed in high-viscosity fluids, thereby significantly improving mixing efficiency. Additionally, impeller geometry played a critical role in performance: the axial-flow impeller promoted faster homogenization and broader circulation compared with the radial-flow design. Through this CFD-based analysis, this study elucidates the key mechanisms governing mixing in high-viscosity fluids and provides practical design and operational guidelines for industrial stirred tank systems. These findings complement existing empirical guidelines focused on low-viscosity fluids and contribute to improving the efficiency and reliability of high-viscosity polymer processing.

In this research, we attempted to develop paints that can be applied to various fields such as high-rise building structures and electric vehicle batteries. To minimize damage to life and property in the event of a fire, we attempted to manufacture a highly elastic paint material that can block flames and control smoke spread, and that has additional sound insulation and waterproofing functions. A high-elasticity paint was manufactured by mixing a flame-retardant polyurethane dispersion (PUD) with an acrylic emulsion binder and adding different mass fractions of expandable graphite (EG). The thermal, physical, and morphological properties of the prepared mixed paint were analyzed. The thermal properties of the mixed paint were analyzed and intended to be used as input data (heat transfer coefficient, specific heat capacity) for fire simulation. Output data were used to predict how much the temperature would change depending on the time of fire occurrence. The reason for conducting simulations on the fire stability of paint materials is that the fire stability of paints can be predicted without conducting fire tests. Two hours after the fire broke out, the thermal temperature distribution was analyzed. The temperature distribution was compared with and without mixed paint. Two hours after a fire broke out in a virtual space, it was found that when the mixed paint was applied, the surrounding temperature of the penetration area was lower than when the mixed paint was not applied. Development costs for developing excellent paints can be reduced. Since fire safety can be predicted without actually conducting tests, the time required for product development can be reduced. We are confident that this is a very groundbreaking technology because it allows fire safety simulations for developed products to be conducted in a virtual space by creating an environment similar to actual fire test standards.

Measuring torsional movement is crucial for tracking human kinematics in applications such as virtual reality, biomechanics, rehabilitation, and soft robotic joints. However, continuous, non-invasive torsion measurement remains challenging due to multi-axial deformations, where existing methods rely heavily on empirical data fitting or computationally expensive deep learning models requiring vast training datasets. To address these limitations, this paper proposes a training-free, highly precise torsion estimation framework that converts soft resistive strain sensor data into forearm rotation angles using closed-form kinematic-geometric modeling. Two geometric representations—the Cylinder Model and Truncated Cone Model—are established to analytically map non-linear tensile strain to torsion angles. Additionally, an online calibration algorithm is integrated to mitigate cumulative measurement drift caused by viscoelastic hysteresis. Under static conditions, the proposed models achieved a high coefficient of determination (R² = 0.9888) against reference IMU data. In dynamic trials, the hysteresis compensation algorithm maintained robust tracking performance (R² = 0.8033), achieving an angular root-mean-square error (RMSE) of 4.12° and a cycle peak prominence relative error of 0.42% for the Truncated Cone Model. In this single-subject proof-of-concept feasibility study (n = 1), the proposed framework demonstrated the feasibility of continuous torsional motion tracking through a training-free geometric approach, providing a preliminary foundation for future evaluation in wearable systems and soft robotics.