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 nonadjacent 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.

Demand for high-performance bonded magnets with complex geometries is growing in electric motors, sensors, and energy devices. In lithography-based composite manufacturing (LCM), the magnetic properties of anisotropic NdFeB composites depend strongly on particle alignment during curing. This study compares the magnetic characteristics of 70 wt% NdFeB composites fabricated under magnetic-field-assisted and non-aligned conditions. An in situ alignment module was integrated into the manufacturing platform to induce directional particle orientation. The magnetic flux distribution was analyzed in ANSYS Maxwell, and particle alignment behavior was simulated by discrete element method (DEM) modeling in ANSYS Rocky. Vibrating sample magnetometry confirmed that magnetic-field-assisted processing enhances the anisotropic magnetic response, in agreement with the simulation predictions.

3D printing is emerging as a promising solution for the automotive industry, as it offers economic advantages in smallbatch, 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.

As AI transformation expands in manufacturing, intelligent technologies are increasingly applied to CNC machine tools and machining processes. In multi-product, small-batch production environments, frequent product changes require flexible and autonomous process planning. This study proposes a standard data integration-based intelligent process planning system that automatically performs the entire process from 3D model input to NC code generation. To enable intelligent process planning, data across all stages—from feature recognition to machining execution—must be integrated into a unified flow and connected with AI-based decision-making. The proposed system uses an ISO 14649-based XML schema to sequentially link data generated by each module, ensuring standardized information flow. Based on this framework, rulebased feature recognition, constraint-based process planning, and machine learning-based cutting condition optimization are implemented. A prototype system was developed to validate the approach, automatically generating NC code for industrial parts and performing actual CNC machining. Experimental results confirmed the feasibility and validity of the proposed system. This study demonstrates that standardized data integration combined with AI technologies can enable autonomous, flexible, and efficient process planning for advanced manufacturing environments.

Recent manufacturing environments demand greater flexibility due to the increasing need for high-mix, low-volume production. While mobile and collaborative robots have made it easier to relocate equipment and change layouts, reconfiguring manufacturing cells remains challenging. Successful reconfiguration relies not only on physical layout changes but also on a deep understanding of the original design intent, operational constraints, and the empirical knowledge gained during operation. Unfortunately, this knowledge is often implicit and may depend on engineers or operators who are no longer available. To tackle this issue, this study introduces a framework for manufacturing cell reconfiguration based on the Asset Administration Shell (AAS). This framework integrates static engineering information with the operational knowledge acquired throughout construction and operation. It organizes asset specifications, operational states, manufacturing skills, and related documents into a unified structure, enabling reconfiguration decisions to reflect both system configurations and proven operating conditions. Furthermore, it connects work execution results with operational knowledge, document versions, and raw data references to enhance traceability and reproducibility post-reconfiguration. This proposed approach aims to reduce the complexity and cost of cell reconfiguration and relocation while enhancing operational flexibility, consistency, and scalability.

Manufacturing systems are increasingly required to operate in high-mix, low-volume production environments, where process flexibility is crucial. One effective way to achieve this flexibility is through the use of multiple processing alternatives (MPA), allowing a product to be produced using different process plans or component structures. In MPA environments, scheduling decisions must address both the selection of processing alternatives for each product and the execution order of the resulting production tasks. Additionally, processing times often vary due to machine conditions and process variability, further complicating scheduling. This study introduces a dual-network-based deep reinforcement learning method for scheduling in manufacturing systems with multiple processing alternatives. The framework utilizes two Q-networks to learn both the selection of processing alternatives and the dispatching rules. Computational experiments demonstrate that the proposed method effectively reduces both the average makespan and its variability compared to a genetic algorithm-based approach, particularly as the problem size increases, showcasing its effectiveness in the face of processing time uncertainty.

This paper examines the role of generative AI and large language models (LLMs) in advancing intelligent manufacturing as we transition from Industry 4.0 to Industry 5.0. We begin by analyzing the current limitations of rule-based and manufacturing data systems in facilitating flexible, human-centric production. Next, we categorize LLM utilization strategies into three methodological axes: fine-tuning domain-specific models, employing general-purpose models through prompt engineering, and utilizing retrieval-augmented generation (RAG), which includes multimodal RAG that integrates sensor and text data. For each strategy, we present representative case studies across key application areas such as asset management, maintenance intelligence, quality control, process optimization, and knowledge- and document-centric support systems. Concurrently, we explore how information modeling and ontology-based knowledge graphs can be integrated with LLMs to enhance structured manufacturing semantics, improve source traceability, and minimize hallucinations. Finally, we summarize the advantages and limitations of each approach and propose future research directions for human-centric manufacturing, including the development of trustworthy LLM pipelines, standardized data schemas, and closer integration between digital twins and LLM-based decision support systems.

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.

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.