Directed energy deposition (DED) enables the efficient fabrication and repair of large metal components. However, repeated reuse of the powder generates degraded particles, which must be removed by sieving. This study used scanning electron microscopy and laser diffraction particle size analysis to characterize the sieve-rejected Inconel Alloy 939 powder collected sequentially after each of four DED processing cycles (R1–R4). The rejected powder consistently contained elongated, irregular, and satellite-attached particles, and its particle size increased progressively with each reuse cycle. The mean particle size increased by 16.8%, from 51.1 μm for R1 to 59.7 μm for R4. The D₁₀, D₅₀, and D₉₀ values also increased, with D90, for example, rising from 71.5 to 82.2 μm. These results suggest that sieving may serve as an important quality control step for maintaining powder quality when the powder is repeatedly reused in DED.

Material extrusion (MEX) is widely used in additive manufacturing due to its simplicity and compatibility with various thermoplastics. While adhesive coating is commonly applied to enhance the watertightness of MEX-fabricated (MEXed) structures, its impact on mechanical properties remains unclear. This study investigated the effect of cyanoacrylate adhesive dip coating on the mechanical behavior of MEXed polylactic acid (PLA) specimens. Tensile specimens were fabricated in both longitudinal and transverse orientations and coated with two commercial cyanoacrylate adhesives, Loctite 401 and Alaska 4001. The coating process was repeated 2, 4, and 6 times, followed by cross-sectional observations and tensile testing. The results indicated that the cross-sectional area increased with an increasing number of coating repetitions, regardless of adhesive type or infill pattern. Tensile strength generally decreased after coating, with reductions of 10.6–14.7% after six coating cycles. In contrast, the elongation at break increased after coating treatment. For Loctite 401, the maximum increases were approximately 35.1% and 124.1% for the longitudinal and transverse specimens, respectively, while Alaska 4001 resulted in maximum increases of approximately 29.7% and 48.3%, respectively. These findings suggest that adhesive dip coating significantly alters the mechanical behavior of MEXed PLA structures and requires optimization to balance strength and ductility.

As interest in personalized biomechanical interventions grows, customized insoles have garnered attention for their ability to improve plantar pressure distribution and foot comfort. While previous studies primarily focused on pressure redistribution, systematic quantitative evaluations across different foot types remain limited. Notably, objective assessment methods that link plantar pressure-based structural metrics with subjective comfort are still insufficient. In this study, customized insoles were fabricated for 32 adults using plantar pressure-based foot shape acquisition and additive manufacturing, and their biomechanical effects were evaluated. The arch index (AI) and subjective comfort were measured before and after one month of use. AI was calculated from static plantar pressure data, and comfort was assessed across multiple foot regions using a visual analogue scale. Statistical analysis revealed significant improvements in comfort at the arch, ankle, and rearfoot regions (p < 0.05). The mean AI deviation from the normal reference value decreased by approximately 30.6%, indicating a shift toward a more normalized arch pattern. Overall, this study demonstrates that customized insoles can enhance plantar contact characteristics and perceived comfort across different foot types. These findings provide quantitative evidence for engineering evaluation and establish a foundation for the objective assessment of personalized insole performance.

Cooperative 3D printing (C3DP) with multiple robotic manipulators can reduce build time through parallel deposition, but it requires layer partitioning that accounts for collision clearance, workload balance, G-code toolpath compatibility, and interlayer boundary alignment. This study presents a Voronoi- and graph-based layer partitioning framework for C3DP. STL geometry and G-code were integrated into layer-aligned data, and a 65 mm collision clearance was defined from the measured end-effector collision radius as the minimum separation preventing collisions between robots approaching non-adjacent Voronoi cells. Each layer was divided into Voronoi cells so that non-adjacent cells could be treated as collision-free regions. Cell adjacency and toolpath-based processing time were modeled as a weighted graph, and adjacent cells were clustered into workload-balanced task regions. Interlayer seed offsets staggered the partition boundaries, and graph coloring identified regions that could be printed simultaneously.The framework was evaluated by workload-balance simulations and printing experiments. Balance deteriorated when clusters were excessive relative to graph nodes. In experiments, the end-effector separation always exceeded the 65 mm clearance. Partitioned printing reduced the layer printing time from 70.063 to 63.57 min, a 9.3% reduction, and the second layer covered the preceding partition boundary, confirming the staggered-boundary implementation.

Hair-like surfaces in nature consist of high-aspect-ratio fibers with diameters below 100μm, falling to several tens of micrometers in softer hairs. These fine fiber arrays govern tactile softness, flexibility, surface texture, and mechanical response. Conventional fiber-spinning methods produce fine fibers effectively but offer limited control over the position, direction, and patterned arrangement of individual fibers. Here we propose a fused deposition modeling (FDM)-based strategy that combines melt extrusion with geometric drawing to fabricate PLA hair-like fibers. PLA melted fully at the processing temperature of 250oC, well below the thermal degradation onset near 330oC. DSC analysis showed that faster cooling suppressed thermodynamic crystallization, indicating that the final fiber structure is governed by drawing history and rapid cooling rather than by increased crystallinity. As the printing speed increased, the fiber diameter decreased nonlinearly, following D ≈ 106.3 v^-0.45, in excellent agreement with the D  v^-0.5 scaling predicted by the continuity equation. Tensile strength and modulus increased with printing speed, whereas elongation and toughness decreased, indicating drawing-induced molecular orientation. These results demonstrate that FDM can serve as a programmable platform for fabricating biomimetic hair-like fiber arrays with predictable diameter and mechanical properties.

This study investigated the ultrasonic fatigue behavior of ABS and of a high-strength photopolymer resin (Rigid Black) fabricated by digital light processing (DLP) additive manufacturing. The dynamic elastic modulus of both materials was measured so that specimens could be designed to satisfy the 20 kHz resonance condition. ABS specimens were CNC-machined, whereas Rigid Black specimens were DLP-printed and post-cured. Thermal effects were minimized by compressed-air cooling with a 0.3 s/3 s duty cycle. S-N curves showed that fatigue life increased as the stress amplitude decreased for both materials. ABS exhibited higher fatigue strength and a more consistent life distribution, which is attributed to its homogeneous microstructure. Rigid Black showed lower fatigue strength with greater scatter, reflecting the anisotropy and interfacial inhomogeneity introduced by layer-by-layer fabrication. Fractographic analysis revealed that ABS underwent mixed-mode ductile-fatigue fracture through crazing, whereas Rigid Black failed in a brittle manner, with directional crack propagation driven by process-induced defects. These results confirm the feasibility of ultrasonic fatigue evaluation for DLP-printed polymer components and provide a basis for assessing the durability of additively manufactured parts.

3D printing is emerging as a promising solution for the automotive industry, as it offers economic advantages in small-batch, high-variety production and mitigates climate impact by eliminating mold fabrication. This study compares the carbonemission reduction potential and economic feasibility of fused deposition modeling (FDM)—the most widely used polymer 3D printing process—with those of conventional injection molding at the actual component level. The analysis shows that the environmental burden of mold manufacturing in injection molding is substantial, confirming the advantage of FDM in low-volume production. Specifically, for production volumes below 645 units, FDM performs better in reducing carbon emissions. These findings indicate that FDM can serve as a sustainable alternative for low-volume manufacturing in automotive applications.

This study examines the porosity behavior during the directed energy deposition (DED) of dissimilar metals S45C and H13. We analyzed the effects of deposition parameters, including laser power, feed rate, and powder characteristics, on pore formation, taking into account the unique properties of these metals. Our findings indicate that laser power is the primary factor influencing porosity. At a low power of 200 W, insufficient energy input, along with differences in thermal conductivity and chemical composition between S45C and H13, led to incomplete melting and lack-of-fusion, resulting in high porosity. As the laser power increased to 400-600 W, the melt pool stabilized, enhancing interfacial bonding and significantly reducing porosity. However, at an excessive power of 800 W, rapid melting and solidification of the powder caused gas entrapment and pore formation, which increased porosity, particularly due to the differing thermal conductivities of S45C and H13. Therefore, our results suggest that maintaining an adequate laser power of 400-600 W is essential for achieving a stable melt pool and minimizing porosity in the DED process for dissimilar S45C and H13 metals.

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.

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.