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.
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.
Parallel robots exhibit superior precision to serial robots. They operate with reduced power consumption due to load distribution among individual motors. However, symmetrical parallel robots employing a 1T2R structure encounter challenges with parasitic movements at the end-effector, leading to control complexities and application limitations. This study aimed to downsize the robot while ensuring its operational range by employing origami techniques. Addressing the inherent weakness of origami’s stiffness, various methods of material stacking and designed joints with diverse materials and thicknesses were proposed to meet specific angle requirements for each component. The developed control model was validated through simulations and experiments, effectively minimizing parasitic movements by verifying the robot"s motion.
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.
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Multifunctional gradations of TPMS architected heat exchanger for enhancements in flow and heat exchange performances Seo-Hyeon Oh, Jeong Eun Kim, Chan Hui Jang, Jungwoo Kim, Chang Yong Park, Keun Park Scientific Reports.2025;[Epub] CrossRef
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.
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Heated Syringe Extrusion for Soft Gripper Fabrication in Additive Manufacturing Kwang Yeol Yu, Woo Jin Jeong, In Hwan Lee International Journal of Precision Engineering and Manufacturing-Smart Technology.2025; 3(1): 59. CrossRef
Multi-material additive manufacturing process design of sensor embedded soft gripper Kwang Yeol Yu, Hochan Kim, In Hwan Lee Sensors and Actuators A: Physical.2025; 386: 116322. CrossRef
Application of Image Recognition Technology in Nozzle Cleaning for Material Extrusion Additive Manufacturing Processes Ho-Chan Kim, Yong-Hwan Bae, Hae-Yong Yun, In-Hwan Lee Journal of the Korean Society of Manufacturing Process Engineers.2024; 23(11): 20. CrossRef
Construction of a Pneumatic Control System for Soft Gripper Seongyeon Kim, Kiseong Kim, Jongho Shin, Jungho Cho Journal of the Korean Society of Manufacturing Process Engineers.2024; 23(6): 30. CrossRef
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.
This research developed a CAM S/W, which generates an adaptive 5-axis tool path, to optimize the quality of Direct Energy Deposition (DED) 3D printing. After reconstructing part shapes and generating printing paths in each shape, the path simulation including automatic collision detection was implemented. Productivity and printing quality were improved through equipment improvement and process optimization. In addition, high-quality parts with desirable physical and mechanical properties were produced by generating an adaptive 5-axis path specialized in the printing process that reflects various physical phenomena and monitoring results. Finally, the performance of CAM S/W was verified through the production of prototypes for industrial components.
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.
In this paper, when the finishing process is performed on the additive by FDM type, the optimal parameter set of the additive-finishing design parameters to improve the surface quality and the verification of the finishing effect are described. Additive design parameters such as nozzle diameter and layer height and finishing design parameters such as depth of cut and feed rate have a significant influence on the printing time and surface roughness of the sculpture. So, we define the major additive-finishing design parameters expected to affect the results. So, we define the major additive-finishing design variables that expected to affect the experimental results. And to confirm how much they affect the results with the minimum number of experiments, the sensitivity analysis of the design parameters was performed through the level average analysis of the Taguchi method. As a result, compared to the surface roughness and additive time when only high-quality sculpture was performed, and it was confirmed that the printing time improved up to 70% and the surface roughness improved up to 87% for the additive-finishing sculpture performed with the optimal combination of design parameters.
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Advancements in polymer nanocomposite manufacturing: revolutionizing medical breakthroughs via additive manufacturing Sadaf Bashir Khan, Shenggui Chen, Xiaohong Sun Polymer Bulletin.2024; 81(11): 9465. CrossRef
Optimal Joint Path Planning of a New Virtual-Linkage-Based Redundant Finishing Stage for Additive-Finishing Integrated Manufacturing Jiwon Yu, Haneul Jeon, Hyungjin Jeong, Donghun Lee Mathematics.2023; 11(24): 4995. CrossRef
Additive manufacturing requires a relatively long time to fabricate complex three-dimensional (3D) structures or parts with more than one material. For additive manufacturing processes, production time and precision vary depending on the fabrication conditions. In this study, we developed a food additive manufacturing process of the material extrusion method type using a dual nozzle. In addition, we observed the change in the cross-sectional shape of the discharged food line according to each fabrication condition. By using a dual nozzle, the structure was fabricated under conditions of relatively high precision for the outer wall and relatively low precision for the infill, thereby shortening the production time. Through this process, it can be expected that the production time will be shortened in the food field, while the appearance will be of good quality.
Research on the application of additively manufactured polymer (AMP) to the conventional manufacturing process is underway. In this study, an additively manufactured die-set (AMDS) was used and applied to the warm forming of the magnesium alloy. Heat transfer and coupled temperature-displacement analysis were conducted in the V-Bending and UBending processes to study the applicability of the AMDS to the warm-forming process of the magnesium alloy sheet (AZ31B). A heat transfer experiment was conducted to determine the thermal contact conductance between the AZ31B material and two types of die-set, the metal and AMP. V-Bending and U-Bending experiments were conducted at 373 and 423 K; reduction in temperature between metal die-set and the additively manufactured polymer die-set were compared. The springback after the bending process with different initial temperatures and die materials was investigated. The simulation model showed good agreement. The springback of AZ31B was more decreased with the additively manufactured polymer die-set than with the metal die-set. The stress of the additively manufactured polymer die-set in the bending process was very small. It was confirmed that in the AZ31B material, the additively manufactured polymer die set helps increase the formability and decrease springback by keeping the temperature of AZ31B better.
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