In this study, we comparatively analyzed the convective heat transfer performance of single-wall and double-wall Gyroid TPMS (Triply Periodic Minimal Surface) structures. Using computational fluid dynamics (CFD), we evaluated the average convective heat transfer coefficients under constant surface temperature conditions for both constant velocity and constant pressure flow. Although both structures maintained the same fluid volume, the double-wall configuration increased the surface area by approximately 1.8 to 1.9 times, resulting in enhanced heat transfer performance. Under constant velocity conditions, the double-wall structure exhibited an average convective heat transfer coefficient that was 1.3 to 1.4 times higher than that of the single-wall structure. Under constant pressure conditions, we observed an increase of 1.06 to 1.1 times. Despite the double-wall structure leading to greater pressure losses due to increased shear stress from the formation of microchannels, it still maintained improved heat transfer performance even with reduced mass flow rates under constant pressure conditions. These findings provide fundamental data for designing TPMS-based cooling systems and optimizing additive manufacturing processes.

Silicon is a key material in advanced technologies due to its thermal stability, appropriate bandgap, and wide applicability for advanced devices. Si microstructures offer enhanced surface area, thus improving performances for energy storage and biosensing applications. However, conventional top-down fabrication methods are complex, costly, and environmentally unfriendly as they rely on cleanroom facilities and toxic chemicals. This study proposed a simplified, eco-friendly bottom-up laser-based process to fabricate silicon microstructures. By controlling laser parameters during the interaction with silicon nanoparticles, diverse Si structures can be fabricated by Si nanoparticle coating and laser irradiation.

Facility Layout Problem (FLP) aims to optimize arrangement of facilities to enhance productivity and minimize costs. Traditional methods face challenges in dealing with the complexity and non-linearity of modern manufacturing environments. This study introduced an approach combining Reinforcement Learning (RL) and simulation to optimize manufacturing line layouts. Deep Q-Network (DQN) learns to reduce unused space, improve path efficiency, and maximize space utilization by optimizing facility placement and material flow. Simulations were used to validate layouts and evaluate performance based on production output, path length, and bending frequency. This RL-based method offers a more adaptable and efficient solution for FLP than traditional techniques, addressing both physical and operational optimization.

Holonic Manufacturing Systems (HMSs) are regarded as a foundation of cyber-physical production systems as they enable computers to conduct intelligent process planning, scheduling, and control by endowing manufacturing components with autonomy and collaboration. In an HMS, autonomy is realized by specifying holons that represent virtual agents of manufacturing components, while collaboration is facilitated through a communication mechanism that enables data exchange and decision making throughout a holarchy of holons without human intervention. This study presents the development of a virtualized holon model and a predictive process planning procedure using the Asset Administration Shell (AAS), i.e., a standardized model that can identify digital representation of manufacturing components to ensure interoperability. Specifically, an AAS-based information model was proposed to define operator, machine, product, and order holons. In addition, a predictive process planning procedure based on the Contract Net Protocol was developed to automatically allocate tasks while predicting task execution times. This study can contribute to the designing of an AAS- domain specific information model for HMS to increase interoperability in the holon holarchy, exhibiting the feasibility of AAS applications in predictive process planning on HMS.

This study aims to optimize the process conditions for high-density polyethylene (HDPE) additive manufacturing through a systematic analysis of key variables, including material selection, layer height, feed rate, melting temperature, and bed temperature. By exercising precise control over these variables, optimal conditions were established, which included a melting temperature of 240oC, a welding speed of 150 cm/min, and a material throughput of 5.66 kg/h. Furthermore, the process was refined by implementing a zig-zag layering method, which significantly improved the stability, bonding strength, and overall mechanical properties of the final HDPE products. The effects of these optimized process conditions were assessed through a series of mechanical tests, such as tensile tests, impact tests, and heat deflection temperature (HDT) tests. As a result, the defined process conditions yielded excellent mechanical performance, achieving a tensile strength of 21.15 MPa, an impact strength of 320 J/m, and an HDT of 93oC. Overall, this study illustrates the enhancement of HDPE additive manufacturing quality through the optimization of process conditions. The strategic implementation of these optimized variables, along with advanced extrusion module design, demonstrates the potential for producing high-quality and cost-effective HDPE products, thereby underscoring their enhanced marketability and performance potential.

Additive manufacturing, a key enabler of Industry 4.0, is revolutionizing the automatic landscape in manufacturing. The primary challenge in manufacturing innovation centers on the implementation of smart factories characterized by unmanned production facilities and automated management systems. To overcome this challenge, the adoption of 3D printing technologies, which offer significant advantages in standardizing production processes, is crucial. However, a major obstacle in complete automation of additive manufacturing is an inadequate placement of support structures at critical locations, which remains the leading cause of print failures. This study proposed a novel algorithm for accurate detection of island regions known to be critical areas requiring support structures. The algorithm can compare loops on two consecutive layers derived from STL files. In contrast to conventional GPU-based image comparison methods, our proposed CPU-based algorithm enables high-precision detection independent of image resolution. Experimental results demonstrated the algorithm's efficacy in enhancing the reliability of 3D printing processes and optimizing automated workflows. This research contributes to the advancement of smart manufacturing by addressing a critical challenge in the automation of additive manufacturing processes.

This paper shows results of research on transparent electrode manufacturing processes using thermal imprinting and IPL technique. By using an IPL process instead of the existing heat sintering process, the sheet resistance value was reduced to about 1/ 10. Additionally, sintering time could be reduced from 1 hour to 1 ms. As a result of measuring the transmittance to determine the excellence of the transparent electrode produced in this way, it was confirmed that it had a high transmittance of 94.4% compared to the substrate with a very high bending stability compared to the existing ITO transparent electrode. These results show that the transparent electrode manufacturing method proposed in this study is very useful.

Predicting elastic modulus of a porous structure is essential for applications in aerospace, biomedical, and structural engineering. Traditional methods often struggle to capture complex relationships between material properties, design variables, and mechanical behavior. This study employed artificial neural networks (ANNs) to predict the elastic modulus of a porous structure based on various material and design parameters. An ANN model was trained on a dataset generated via finite element analysis (FEA) simulations, covering diverse combinations of material properties and design variables (e.g., porosity, structure types). The model demonstrated high accuracy in predicting the elastic modulus on a separate test dataset. Key findings included identification of significant design variables influencing the elastic modulus and the ANN model"s ability to generalize predictions to new data. This approach showcases that ANN is a powerful tool for designing and optimizing porous structures, providing reliable mechanical property predictions without extensive experimental testing or complex simulations. The proposed method can enhance design efficiency and pave the way for developing advanced materials with tailored mechanical properties. Future research will extend the model to predict other mechanical properties and incorporate experimental validation to verify ANN predictions.

Additive manufacturing (AM) technology, also known as 3D printing, is a highly promising technology that can drive innovation in various industrial areas, including the nuclear industry. Although the nuclear industry is traditionally conservative when it comes to adopting new technologies, it is crucial that AM technology is eventually applied for a variety of reasons. To overcome the barriers that currently hinder the adoption of AM in the nuclear industry, it is essential to ensure the reliability of AM products. One key factor is ensuring that AM products have mechanical properties equivalent to those of traditionally manufactured products. This paper presents the results of mechanical property tests conducted on additive manufactured specimens of stainless steel 316 L after heat treatment. We performed tensile tests, hardness tests, and microstructure analysis on specimens produced using two types of metal AM technologies: powder bed fusion (PBF) and directed energy deposition (DED). The results of the tests indicate that certain weaknesses, such as anisotropy and brittleness, in AM products can be improved through three types of heat treatments. In particular, AM products produced using the PBF method and subjected to heat treatments show potential for application in the nuclear industry in terms of materials.

Recent advancements in additive manufacturing (AM) have made it possible to create compact heat exchangers (HXs) with complex geometries. This study introduces a new approach that uses Triply Periodic Minimal Surface (TPMS)-based designs for HXs. Mathematical filtering techniques are incorporated to optimize the local morphology changes. The goal of the proposed mathematical filtering method is to improve the flow characteristics and heat exchange capability of TPMS HXs by modifying the structure’s morphology at the inlet and outlet regions. This modification facilitates flow selection and reduces pressure drop. The HX design includes cylindrical flow domains at the inlet and outlet regions. Three different HX designs with varying inlet/outlet domains (through-hole, half-hole, and taper-hole) were fabricated using polymer AM and DLP 3D printing. These designs were then tested for pressure drop. Among the three designs, the taper-hole configuration showed the best flow characteristics, with a 50% reduction in pressure drop compared to previous studies. The taper-hole design was then replicated using metal AM technology, resulting in a 70-125% improvement in heat exchange capacity compared to previous studies.

During its early development stages, 3D printing was primarily used for rapid prototyping, whereas it is currently employed to fabricate products in various fields, including aerospace, automobile production, dentistry, architecture, and food. The photopolymerization of the polymer used for 3D printing is precise and provides excellent surface roughness but has lower mechanical strength than traditional manufacturing methods. In this study, Multi-walled Carbon Nanotubes (MWCNTs) were blended with urethane acrylate-based resin as a filler. Mechanical strength enhancement was confirmed using a DLP 3D printer. The stabilities of MWCNT dispersions in resin were verified, and viscosity and curing depth measurements were conducted to establish 3D printing parameters. Tensile and flexural strengths were higher for an MWCNT length of 50 μm than one of 100 μm, and maximum values were obtained at an MWCNT content of 0.1 phr. Under optimal conditions, tensile and flexural strengths increased by 2.1 and 1.8-fold, respectively.

Soft robots, known for their flexible and gentle movements, have gained prominence in precision tasks and handling delicate objects. Most soft grippers developed thus far have relied on molding processes using high-elasticity rubber, which requires additional molds to produce new shapes, limiting design flexibility. To address this constraint, we present a novel approach of fabricating pneumatic soft grippers using thermoplastic polyurethanes (TPU) through the Fused Filament Fabrication (FFF) technique. The FFF technique enables the creation of various gripper shapes without the need for additional molds, allowing for enhanced design freedom. The soft grippers were designed to respond to applied air pressure, enabling controlled bending actions. To evaluate their performance, we conducted quantitative measurements of the gripper’s shape deformation under different air pressure conditions. Moreover, force measurements were performed during gripper operation by varying the applied air pressure and adjusting the mounting angle. The results of this study provide valuable insights into the design and control of soft grippers fabricated using TPU and the FFF process. This approach offers promising opportunities for employing soft robots in various fields and paves the way for further advancements in robotics technology.

In this study, we present the fabrication of dual-morphing vascular stents using an additive-lathe printing method and two different shape-memory polymers. Traditional additive manufacturing techniques confront significant challenges in producing vascular stents with complex, hollow, mesh-like structures due to limitations such as a flat printing bed and the placement of supports. To overcome these obstacles, we employed a lathe-type additive manufacturing system with a rotatable base substrate, enabling precise fabrication of cylindrical-shaped stents. To achieve shape transformability, we used shapememory polymers as the stent materials, offering the advantage of minimally invasive surgery. Two distinct shape-memory polymers, with different transition temperatures (35 and 55oC), were printed using the additive-lathe method. The printed stents consisted of two distinct parts that underwent dual-stage morphological changes at the different temperatures. By manipulating the printing paths, the dual-morphing properties of the stents could be adjusted in both longitudinal and circumferential directions. This innovative approach could be a solution to several limitations associated with the application of stents in diseased vascular tissues with complex shapes, facilitating minimal invasion during surgical procedures.

In this study, the design for additive manufacturing of shoe molds with complex and precise patterns was performed to achieve rapid prototyping. Low alloy steels such as AISI4340 and SAE1524 were selected to make shoe molds to apply to the conventional chemical etching process. A lattice-oriented design and optimization of toolpath was tested to reduce the processing time. A reduction of 60% in processing time and pattern precision of 0.3 ㎜ was been achieved. Moreover, to improve the reliability of pattern formation, single-layer image analysis with computer vision and machine learning was developed and non-destructive analysis by X-ray CT was been performed. It was found that the quality of shoe molds can be decreased with a single defective layer.

Directed energy deposition (DED) additive manufacturing technology enhances the functionality of existing or damaged parts by adding metallic materials to the surfaces. Blown-powder DED technology utilizes a focused, high-energy source to fuse the part’s surface with the supplied metal powder. Maintaining a constant stand-off distance (SOD), the distance between the deposition head and the workpiece, is a key factor in ensuring deposition quality, as variations in SOD will change the powder focus position and the laser spot size on the surface. Therefore, traditional additive manufacturing systems require CAD or pre-scanned surface data. In this study, we proposed auto-surface tracking technology. No workpiece CAD data or pre-scanned surface data are required, and in-situ measurement and feedback control can automatically consider the deposition height differences that cause a change in SOD when depositing the next layer. The accuracy of the SOD measurements and feedback control error was verified using a step height sample. The mean SOD measurement error was 4.7 ㎛ with a standard deviation of 42 ㎛ (reference SOD, 14 ㎜). The feasibility of the autosurface tracking technology was confirmed through the additive manufacturing processes of the gear and an actual blanking mold applied in the defense and industrial fields.