Browse Topic: Advanced manufacturing
With the continuous improvement of performance requirements for aviation equipment, the importance and complexity of hydraulic systems as the core carrier of flight control are becoming increasingly prominent. The cleanliness of aircraft hydraulic pipelines directly affects the reliability and flight safety of hydraulic systems, and it is necessary to use specialized cleaning and testing equipment during design and manufacturing to achieve efficient cleaning. The design of traditional cleaning equipment relies on experience-driven development, with mechanical, hydraulic, and electrical systems developed independently. There are problems such as unclear requirement definitions, low efficiency of interdisciplinary collaboration, and lagging validation, making it difficult to achieve the goal of forward design. Therefore, this study introduces Model-based Systems Engineering (MBSE) method in the development process of pipeline cleaning test equipment, proposes a modeling process based on RFLP (Requirements-Function-Logical-Physical), and uses SysML system modeling language to construct a top down design model system for aircraft hydraulic pipeline cleaning equipment. Through requirement analysis modeling, functional behavior definition, and system architecture design, the significant advantages of MBSE method in the development of complex aviation test equipment have been verified, effectively improving the bold design capability and top down design efficiency. MBSE method can not only improve the design efficiency of equipment, but also promote the intelligent and efficient operation of equipment, which has important significance for the development of intelligent manufacturing and electromechanical integration technology.
This research develops a multi-arc cooperative additive fabrication to address the technical challenges of low forming efficiency and insufficient precision in the production of complex components using conventional single-arc additive manufacturing systems. With the core objective of achieving efficient and high-quality production of large high-performance metal parts, the equipment employs a modular architecture, incorporating four core modules: additive fabrication modular, 3D measurement module, subtractive machining module, and central control module. It creatively designs a multi-arc cooperative additive fabrication head assembly characterized by “two contours + one filling” arc layout, enabling synchronized operation of two contour single-wire arcs and an independently developed single-power three-wire oscillating filling arc. This system builds a multi-robot collaborative motion system based on the master-slave control strategy. It realizes time synchronization and trajectory synchronization of additive, measurement, and subtractive robots through the KUKA.RoboTeam software package. Meanwhile, it integrates a laser arc constraint device, a molten pool monitoring system, and a digital process parameter monitoring module. An integrated manufacturing capability of “additive - measurement - subtraction” is formed to enhance the forming efficiency and accuracy of components. Experimental verification shows that the forming efficiency of this equipment reaches 1800 cm^3/h, which is more than three times higher than that of traditional single-arc equipment. The surface roughness of the components is optimized to 41.50 μm, and the forming size error is controlled within ±0.5 μm. It can be adapted to the one-time forming of components with a width of 30 to 150 mm. It provides reliable technical support for the high-performance manufacturing of large metal components.
A machine-learning strategy has generated a new class of ultra-high strength and ductility steel for 3D printing that costs less, resists rust, and requires only a fraction of the usual processing time.
3D printing could change how we build parts for jet engines and power plants, but the process leaves microscopic holes that cause the materials to shatter. Published in International Journal of Extreme Manufacturing, Professor Fangyong Niu’s team in Dalian University of Technology have fixed the problem by doing something unconventional: They added a microwave.
Topology optimization (TO), while powerful for generating high-performance structural layouts, often yields designs with enclosed voids that hinder manufacturability in powder-based additive manufacturing (AM). To address this, this paper proposes an Adaptive Virtual Temperature Field (AVTF) method that enforces the connectivity constraint. The approach integrates a projection-based density filtering and flood fill algorithm to detect enclosed voids, combined with an adaptive penalty scheme that autonomously adjusts the virtual temperature penalty factor to eliminate disconnected regions. AVTF operates via a low-cost geometric feedback mechanism. Numerical examples demonstrate that the method effectively eliminates enclosed voids with only a marginal increase in compliance while significantly reducing the maximum virtual temperature. The resulting designs exhibit fully connected material layouts, ensuring powder removability. The method provides a practical and robust pathway toward AM-ready topology optimization, bridging the gap between structural performance and manufacturability.
Additive manufacturing (AM) processes facilitate the production of components with high geometrical complexity, presenting substantial opportunities for innovation in demanding sectors such as aerospace and biomedical engineering. A significant challenge impeding their broader application is the characteristic surface roughness of as-fabricated parts, which results from the layer-wise construction and the presence of partially melted powder particles. While electrochemical polishing (EP) represents a viable post-processing technique for achieving a smooth surface finish, a comprehensive understanding of how the non-equilibrium microstructures characteristic of AM materials interact with the EP process remains incomplete. This investigation centers on the electrochemical polishing behavior of Ti-6Al-4V alloy fabricated by direct energy deposition (DED), utilizing a sodium chloride-ethylene glycol electrolyte. The findings reveal that the material's distinct engenders anisotropic anodic dissolution. This behavior is attributed to the differential electrochemical potentials among the constituent phases and their crystallographic orientations, which consequently narrows the operational process window for effective, uniform polishing. This preferential dissolution of certain phases results in the formation of a subtle, micro-scale topographical variation that mirrors the orientation of the original columnar grain structure. Notwithstanding this microstructural influence, the EP treatment proved highly successful in refining the surface finish, substantially decreasing the average surface roughness from 0.350 μm to 0.042 μm. Concurrently, the treatment led to a significant enhancement in the alloy's corrosion resistance, attributed to an oxide layer. These findings underscore the critical necessity of accounting for microstructural characteristics when developing optimized electrochemical polishing protocols for additively manufactured components.
Blended metal powders offer a compelling alternative to pre-alloyed powders in metal additive manufacturing by providing access to a wider range of alloy compositions and avoiding the high costs in producing pre-alloyed powders. In this work, a new and crack-free Ti-5AlMnScZrMgSiFe alloy (in wt.%) was manufactured by laser powder bed fusion (L-PBF) from mixed powders to investigate the microstructures, mechanical performance of printed parts. Ti-5 AlMnScZrMgSiFe alloy contains both alpha (α) and alpha prime (α′) phases. Further microstructural characterizations show that the L-PBF Ti-5 AlMnScZrMgSiFe contain dense dislocations and twins formed in additive manufacturing process. The as-printed Ti-5 AlMnScZrMgSiFe alloy exhibits a tensile fracture strength of ~950 MPa with a fracture elongation of ~12.5%. The eye-catching properties are attributed to the dense dislocations, nano-twins and solid-solution strengthening.
This paper takes a 3D Printer proposed by the project team in the early stage as the research object, constructs a digital twin entity including 3D models and data models in order to develop a digital twin interactive software. By activating the real-time correlation between 3D models and data models, valuable data exchange can be achieved between the digital twin entity and the physical entity, and valuable data can be used to drive both to refresh their operating status.
In this paper, we focus on satellite production lines and design and implement a digital twin simulation and verification system for them. This is to improve manual documentation efficiency and provide sufficient process controllability in the small satellites’ batch production and assembly testing. We built a layered architecture. This allows the system to dynamically interact with AIT data management systems, structured process systems, and equipment data by fusing multi-source data. We also develop functional modules that combine lightweight 3D model visualization, dynamic simulation engines, and hybrid scheduling optimization algorithms. These modules can perform twin simulation, execute processes, intelligently schedule production, manage work reporting, conduct intelligent analysis, trigger anomaly alarms, and perform system management. We also dynamically simulate complex workflows like satellite transfer and automated assembly. These workflows are then verified using 3D virtual scene modeling and physical engines. We use time-series analysis to improve scheduling accuracy and multidimensional dynamic monitoring and hierarchical response to enhance production stability. In practice, the system can provide visualized control over the full process of satellite production. This greatly improves assembly efficiency and process controllability. It can also be an extensible digital way for aerospace manufacturing. The use of hierarchical architecture design and multimodal data fusion can be further applied in the complex equipment intelligent manufacturing.
Humanoid robots have long been the focus of science fiction, but today they are making their way into industrial environments thanks to the simultaneous maturing and convergence of multiple systems. Technology advances have driven the development of humanoid robots that have a wide range of movement and can perform demanding jobs around the clock without tiring. While currently representing a small share of all industrial robot deployments, the humanoid robot market is projected to grow rapidly over the next few years. In fact, estimates suggest the market could reach over $4 billion by 2030. This growth is being driven by factors such as labor shortages, falling costs, and the need for more flexible automation.
The increasing pressure to decarbonize manufacturing systems is pushing industry beyond conventional lightweighting strategies toward material and process paradigms, capable of delivering functional performance with radically lower environmental impact. In this context, polymer-based composite Additive Manufacturing (AM) offers an underexplored yet highly promising pathway for sustainable production of load-bearing components. This study presents a preliminary comparative cradle-to-gate Life Cycle Assessment (LCA) of a Formula SAE brake pedal, assessing the environmental transition from conventional sheet metal fabrication and finishing operations of Aluminum 7075-T6 to additive manufacturing solutions, with specific focus on Carbon-Fiber-Reinforced Polymer (CFRP) composites. Two topology-optimized designs, respectively for Powder Bed Fusion (PBF) in AlSi10Mg and Material Extrusion (MEX) in Polyethylene Terephthalate Glycol with Carbon Fiber (PETG-CF) are compared to conventional fabrication aluminum benchmark. The analysis is integrated in the product and process design following ISO 14040/14044 standards and is implemented using the Environmental Footprint 3.0 methodology within the 3DEXPERIENCE platform. Results outline that Material Extrusion (MEX) composite manufacturing achieves the lowest environmental impact across all evaluated categories. Compared to conventional manufacturing, the PETG-CF solution enables an approximate 50% reduction in Global Warming Potential and an almost complete elimination of mineral depletion. Unlike metal additive manufacturing, which remains constrained by high process energy demand, MEX benefits from low processing temperatures, minimal auxiliary systems, and highly efficient material deposition. Crucially, these sustainability gains are achieved while maintaining functional performance through design-driven topology optimization. AM composite solutions, by merging advanced material science with additive flexibility, may lead to design approaches which cease to be ‘potential’ enablers of sustainable manufacturing for the Industry 5.0 transition.
Qualification of new aerospace alloys requires extensive mechanical testing to capture anisotropy and ensure reliable performance under complex loading conditions. This process is costly and time-consuming, particularly with emerging manufacturing routes such as additive manufacturing. Advanced yield surface prediction offers a route to reduce test campaigns by linking microstructural features to macroscopic constitutive models. In this work, Digimat is employed as a multi-scale material modeling platform to generate yield surfaces of polycrystalline metals using computational homogenization. Representative volume elements (RVEs) are constructed from experimental texture and grain morphology data, and their response under multiaxial loading is simulated using a crystal plasticity framework. The computed yield loci are then fitted with phenomenological functions (e.g. Yld2000-2D), enabling calibration of anisotropic yield models from virtual testing. As a case study, an AA6016-T4 sheet with strong cube texture is modeled and validated against experimental data, including yield stresses and Lankford coefficients in multiple directions. The predictive capability of the approach is further assessed through a cup drawing simulation in Simufact, where earing behavior is accurately reproduced. These results demonstrate that digital yield surface prediction can capture anisotropic plasticity and provide reliable input to forming simulations while significantly reducing experimental requirements. This capability lays the foundation for more efficient alloy qualification, with direct impact on fatigue and damage tolerance modeling in aerospace applications.
Additive manufacturing, or 3D printing, is a new way of making metal parts and other types of materials by building them layer by layer. This research project, by researchers at General Electric, the Edison Welding Institute, and Oak Ridge National Laboratory (ORNL), printed an alloy composed of Inconel 718 and René 41 at two ends with a compositional graded region in the middle. The study evaluated the stress and composition variations of the alloy. To do so, the researchers conducted neutron experiments at the Spallation Neutron Source (SNS) and the High Flux Isotope Reactor (HFIR) at ORNL, both Department of Energy Office of Science user facilities. Neutrons are ideally suited to study internal stresses in materials because they can penetrate dense metals.
Soft robot systems demonstrate exceptional load-bearing capacity and spatial compliance during operation, with transformative potential in disaster response scenarios requiring adaptive morphology and hazardous material manipulation. By integrating the complementary advantages of soft robotics and particle jamming mechanisms, this study proposes a real-time variable-stiffness soft actuator, while systematically investigating its mathematical modeling framework and stiffness modulation principles. A deformation model for the variable stiffness soft actuator is established, followed by static analysis of the variable-stiffness members using particle jamming theory, with theoretical investigation of their stress distributions. Subsequently, a variable-stiffness driver was fabricated via additive manufacturing (3D printing), resulting in a flexible mechanical digit capable of stiffness tuning, A soft mechanical hand grasping test platform was built, and grasping experiments of objects of different shapes and sizes were conducted. Experimental validation confirms the influence of actuator dimensions, particle characteristics, and granule size distribution on both stress states and bending angles at the soft robotic digit’s distal segment. The obtained results establish theoretical foundations and advance variable-stiffness soft robotics research and associated stiffness regulation methodologies.
QuesTek is advancing a suite of emerging alloy technologies to address modern rotorcraft engineering challenges. Current initiatives prioritize the optimization of "print-to-use" materials, such as 17-4PH and other specialized steels designed to minimize or eliminate post-processing requirements in additive manufacturing. These innovations represent a strategic shift toward materials that are not only high-performing but are also specifically tailored for next-generation manufacturing workflows. The catalyst for these advancements is QuesTek’s mastery of Integrated Computational Materials Engineering (ICME). These core capabilities are now deployed through QuesTek's ICMD® software platform, which empowers engineering teams with predictive simulation tools that eliminate the bottlenecks of traditional trial-and-error methodologies. By integrating these physics-based models into a centralized digital environment, QuesTek enables the rotorcraft industry to design, test, and implement advanced materials with unprecedented speed, reduced costs, and increased technical confidence.
This paper investigates the feasibility of using flax fiber-reinforced composites in combination with additively manufactured polymer cores for helicopter rotor blades. A new rotor blade with flax composite spar and skin laminates and a 3D-printed ASA Aero core was designed to be geometrically equivalent to an existing carbon fiber/foam reference blade of the MERIT rotor test rig and manufactured using identical tooling. Material characterization included compression testing of the printed core at ambient and elevated temperatures, single-lap shear adhesion testing with epoxy laminates, and hygroscopic conditioning of core and laminate specimens. Structural testing comprised static beam bending, experimental modal analysis with axial pre-loading to approximate centrifugal stiffening, and sustained-load creep and recovery testing of the flax blade. The results show that the 3D-printed core provides sufficient compressive stiffness at curing temperature and adhesion to epoxy laminates, enabling its use as an internal consolidation tool during blade manufacturing. Compared to the carbon reference blade, the flax/3D blade exhibits reduced flapwise and lead–lag bending stiffness, altered modal behavior, and pronounced viscoelastic effects, including creep, incomplete recovery, and strong hygroscopic swelling. Component-level hygroscopic tests reveal that moisture-induced mass and thickness changes can generate sufficient internal stresses to locally initiate structural damage. Overall, the study identifies key limitations and design considerations for applying flax fiber composites in primary rotor blade structures.
Unmanned aerial vehicle (UAV) primary structures require high specific strength and stiffness, traditionally necessitating expensive carbon fiber composites. This study evaluates simulation-driven, additively manufactured polymer alternatives fabricated from PLA and computationally optimized via macroscopic Topology Optimization (TO), mesoscopic Variable-Thickness Lattices (VTL), and uniform Triply Periodic Minimal Surfaces (TPMS). Evaluations were conducted under a superimposed, multi-axial flight envelope. Physical testing demonstrated that VTL architectures maximized the Structural Efficiency Index (SEI) by pushing mass to the extreme geometric fibers and increasing global flexural rigidity. In contrast, mass-constrained TO yielded misleading specific strength due to volumetric starvation and elevated compliance, while the uniform TPMS baseline exhibited favorable specific stiffness but lacked targeted root robustness, resulting in reduced specific strength. Off-axis testing further showed that Diamond lattices dominated vertical bending and inverted impulse loading, whereas Octet and Kelvin geometries more efficiently resolved transverse shear. Experimental data identified a performance-optimized efficiency asymptote in the 73-79 g VTL specimens, which achieved a 19-24% mass reduction relative to a 97 g carbon fiber baseline. To assess assembled-vehicle relevance, the selected fully 3D-printed replacement arms were installed on the baseline quadcopter and subjected to nine dynamic ground tests comprising staircase and cyclic propulsive loading under freestream conditions of 0, 10, and 20 knots. The optimized arms completed the full test matrix without fracture, mount failure, screw loosening, visible yielding, or permanent deformation, demonstrating structural viability in a realistic multi-part UAV assembly without carbon fiber reinforcement.
Army researchers recently developed a 3D-printable, easy-to-assemble drone designed to enhance intelligence, surveillance and reconnaissance capabilities. Army Research Laboratory, Adelphi, MD Researchers at the U.S. Army Combat Capabilities Development Command, or DEVCOM, Army Research Laboratory (ARL) harnessed bottom-up Soldier innovation to develop an experimental 3D-printed small unmanned aerial system, or drone, that was demonstrated at the inaugural U.S. Army Best Drone Warfighter Competition in Huntsville, Alabama. Known as the Soldier Portable Autonomous Reconnaissance Transitioning Aircraft, or SPARTA, the drone was developed at DEVCOM ARL in collaboration with Soldiers. By incorporating Soldier feedback early in the design process and leveraging ARL's world-class research facilities, researchers developed a 3D-printable, easy-to-assemble drone designed to enhance intelligence, surveillance and reconnaissance capabilities. ARL is actively working to partner the technology with industry to get into the hands of the warfighter.
Researchers at the U.S. Army Combat Capabilities Development Command, or DEVCOM, Army Research Laboratory (ARL) harnessed bottom-up Soldier innovation to develop an experimental 3D-printed small unmanned aerial system, or drone, that was demonstrated at the inaugural U.S. Army Best Drone Warfighter Competition in Huntsville, Alabama.
Researchers at Lawrence Livermore National Laboratory (LLNL) have optimized and 3D-printed helix structures as optical materials for Terahertz (THz) frequencies, a potential way to address a technology gap for next-generation telecommunications, non-destructive evaluation, chemical/biological sensing and more.
Battery modules consist of battery cells electrically joined at the terminals by conductive busbars. Laser welds are the most consistent and controllable process to create these connections on a large scale due to their control over power, laser width, speed, wobble, and overlap, and their quality is critical to battery pack performance. Tuning these parameters for an application typically requires weld trials to reach desired weld width, penetration, and strength without overheating the battery cell and weakening the dielectric insulators around the terminals. Poorly welded cells in a module can result in increased electrical resistance, causing greater joule heating and accelerated cell aging, and poorly welded modules can lead to uneven aging and unpredictable performance. To better understand the laser welding process, a modelling approach was developed to predict weld properties to reduce production time, costs, and potential cell damage. The 3D finite element model was calibrated using test data gathered using 1 mm thick aluminum busbars being welded onto 25 mm aluminum terminals with varying laser parameters. A volumetric gaussian heat source was used to characterize the modelled laser. Melting and vaporization in the weld were captured without explicitly modelling them by adjusting the model’s material properties to improve computational efficiency. Each simulation’s predicted melt pool cross section was compared to that of each corresponding weld trial. This modeling approach led to the development of a parametric tool that could quickly predict laser melt pool width and depth which can be used to accelerate laser weld process development.
A newly developed tool could enable more control over how energetic materials function throughout manufacturing processes. Purdue University, West Lafayette, IN Much like baking the perfect cake involve s following a list of ingredients and instructions, manufacturing energetic materials - explosives, pyrotechnics and propellants - requires precise formulations, conditions and procedures to ensure they are safe and perform as intended. Because any small tweaks or environmental changes can dramatically alter how energetic materials function, Purdue University engineer Monique McClain is developing state-of-the-art tools and methods to control these materials' behavior throughout the manufacturing process and down to the particle level.
Leonardo DRS has opened a new naval power and propulsion manufacturing and testing facility in Charleston, South Carolina, expanding its role in delivering next generation electric propulsion, integrated power systems, and high energy payload support for U.S. Navy surface and undersea platforms. The 140,000 square foot site consolidates advanced manufacturing, final assembly, and high fidelity testing for electric power conversion and propulsion systems, while also supporting naval steam turbine design, production, and subsystem integration for programs including the Columbia class ballistic missile submarine. A representative for Leonardo's Naval Power Systems business unit provided emailed statements with details about the type of advanced manufacturing the company will deploy at the new facility.
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