Browse Topic: Forming
To accurately assess the navigation safety status of LNG vessels in port waters and balance safety control with waterway capacity efficiency, this study constructs a 3D dynamic safety domain model for port LNG vessels, integrating human–ship–environment multi-factors. The model introduces the Weibull function to quantify the impact of drivers’ knowledge, skills, and physiological-psychological states on safety boundaries, combines a ship motion mathematical model to establish a 2D safety domain boundary equation, and incorporates hull subsidence to build a vertical dimension, forming a complete 3D model. Longitudinally, the safety distance is calculated using the car-following braking theory, while laterally, boundaries are determined by controlling the ratio of inter-vessel interference force to navigation resistance. Through static scenario analysis and dynamic simulation verification, results show that the safety domain scale is dominated by ship speed and environmental conditions, and its shape tends to shrink as the driver’s state improves, making it more suitable for actual port scenarios than traditional models. Verified with a specific LNG hub port as a case, the safety distance calculated by the model is significantly reduced compared with current specifications, while the delay impact rate and average delay time on other vessels are decreased. The research results establish a quantifiable framework for dynamic safety assessment, providing maritime administrations and on-board pilots with a scientifically-grounded tool to determine real-time safe navigation boundaries in complex port environments, balancing safety control with operational efficiency.
Impacts of laser shock peening (LSP) on the evolution characteristics of microstructure in commercially pure α-phase titanium (α-Ti) are explored by molecular dynamics (MD) simulations of high strain-rate compression. The EAM potential (Zhou potential) is selected for its ability to capture the evolution of microstructures. Considering the LSP-induced peak plasma pressure, the strain rate during the simulated shock compression process is set at 10^9 s-1 to replicate the LSP process. The stress-strain curve of the α-Ti under high strain-rate compression is obtained. The maximum equivalent stress reaches 3.6 GPa, consistent with the theoretically calculated value. The simulation results reveal that mechanical twins (MTs) are activated at a strain of 3%. The number of mechanical twins increases and eventually stabilizes, forming a network structure throughout the grains. In the meantime, numerous partial dislocations are generated adjacent to the grain boundaries. The dislocation density also increases with strain and dislocation reactions occur. Moreover, grain refinement is identified. The grain size is refined from the initial ~ 8 nm to ~ 4 nm in the polycrystalline α-Ti. Twinning, together with dislocation-mediated plasticity, drives the refinement of grain size. Gradients of twin density, dislocation density, and grain size density are induced by LSP on the surface of α-Ti. This study comprehensively investigates how LSP influences the evolution of microstructures by MD simulations. It develops an innovative numerical strategy that offers a foundation for elucidating the underlying mechanisms of LSP.
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.
NASA Marshall Space Flight Center has developed a new small-scale metal extrusion tool, called a conventional friction stir extrusion (C-FSE) machine that may be attached or added-on to a conventional friction stir welding (C-FSW) system. The C-FSE machine uses the heat generation and plastic deformation processes underpinning C-FSW to perform metal extrusion instead of metal joining.
This research investigates the fabrication and evaluation of Delrin (polyoxymethylene, POM) composites reinforcing 5-20 wt.% chopped ramie fiber (RF). The polymer composites were fabricated via the injection moulding technique. Glass transition temperature (Tg), thermal conductivity, Vicat softening temperature (VST), heat deflection temperature (HDT), melt flow index (MFI), and coefficient of linear thermal expansion (CLTE) were the various thermal characteristics of the sustainable composites that were systematically evaluated as per the ASTM standards. The addition of RF drastically altered the Delrin matrix's performance. Among the formulations, the composite with 15 wt.% RF had the best combination of properties: higher VST and HDT values, which provide greater dimensional stability at high temperatures; lower CLTE, resulting in less thermal expansion; comparatively better thermal conductivity; and improved heat dissipation. Eventually, there was a moderate drop in the MFI, indicating more rigid polymer chains that restrict the flowability of the composite, thereby increasing its heat-withstanding capabilities. DSC analysis revealed a slight upward shift in Tg and increased crystallinity, suggesting restricted polymer chain mobility and enhanced load transfer at 20 wt.% RF loadings, agglomeration effects, and weaker interfacial bonding with the matrix led to deterioration in properties. Aircraft cabin components like interior panels, ducting supports, and lightweight non-structural fittings requires dimensional stability, thermal resistance, and mechanical reliability under fluctuating flight conditions.
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.
Machina Labs recently closed its latest round of financing with $124 million, enough to develop a facility featuring up to 50 of its RoboCraftsman cells capable of producing thousands of complex structural assemblies for aerospace and defense customers - a list that already includes Lockheed Martin and the U.S. Air Force, among others. Founded in 2019, Machina Labs is a California-based company that seeks to reinvent metal manufacturing with a robot that uses artificial intelligence (AI) to rapidly form and assemble complex military grade structures directly from digital design files. RoboCraftsman is the company's manufacturing robot that leverages its proprietary “RoboForming” process to integrate multiple manufacturing processes - including metal forming, trimming, scanning, and heat treating - into a single containerized machine.
David Martin, CBMM Asia Bernardo Barile, CBMM Europe BV Caio Pisano, CBMM Europe BV Automotive high strength steels have specific microstructure-dependent forming characteristics. Global formability is generally associated with high uniform strain values which imply good drawability and stretch forming properties driven by pronounced work hardening. Local formability on the other hand is often measured by various fracture strain values—generally higher in single phase steels. In this respect, the so-called ‘local/global formability map’ concept has been established not only to provide a comprehensive methodology to characterize existing automotive steels but also to enable improvement strategies toward more balanced forming characteristics. Niobium (Nb) microalloying is a powerful tool to achieve both property improvement in general and property balance in particular. More than two decades of research has demonstrated that Nb-induced microstructural optimization is applicable to HSLA steels, AHSS (DP, CP, TRIP, TWIP) and PHS, and it has been realized in commercial production of such steels. This contribution details the underlying metallurgical and processing effects of Nb microalloying in automotive high-strength steels and highlights achieved global and local formability improvements. Respective optimization vectors are demonstrated through intrinsic formability mapping, where the possibilities and limitations are indicated.
Tire noise reduction is important for improving ride comfort, especially in electric vehicle due to lack of engine noise and majority of the noise generated in-cabin is from tire-road interaction. Therefore, the tire tread pattern contribution is one of the important criteria for NVH performance apart from other structurally generated noise and vibration. In this work a GUI-based pitch sequence optimization tool is developed to support tire design engineers in generating acoustically optimized tread sequences. The tool operates in two modes: without constraints, where the pitch sequence is optimized freely to reduce tonal noise levels; and with constraints, where specific design rules are applied to preserve pattern consistency and manufacturability. The key point to be considered in this pitch sequence is that it should be reducing the tonal sound and equally spread i.e., the same pitch cannot be concentrated on one side which may lead to non-uniformity. So, the restriction is that the highest and lowest pitch types cannot occur adjacent to one another. This design rule helps in reducing undesirable pattern non-uniformity and improves both acoustic and structural performance. This tool helps in faster design iteration and integration with downstream development processes. This tool is also validated in current OE projects showing promising improvements in tire noise behavior while maintaining realistic design feasibility.
Aluminum alloy wheels have become the preferred choice over steel wheels due to their lightweight nature, enhanced aesthetics, and contribution to improved fuel efficiency. Traditionally, these wheels are manufactured using methods such as Gravity Die Casting (GDC) [1] or Low Pressure Die Casting (LPDC) [2]. As vehicle dynamics engineers continue to increase tire sizes to optimize handling performance, the corresponding increase in wheel rim size and weight poses a challenge for maintaining low unsprung mass, which is critical for ride quality. To address this, weight reduction has become a priority. Flow forming [3,4], an advanced wheel rim production technique, which offers a solution for reducing rim weight. This process employs high-pressure rollers to shape a metal disc into a wheel, specifically deforming the rim section while leaving the spoke and hub regions unaffected. By decreasing rim thickness, flow forming not only enhances strength and durability but also reduces overall wheel weight. This study investigates and compares the mechanical properties of conventional GDC and LPDC cast alloy wheels with flow-formed counterparts, focusing on the rim region. Results reveal that the flow-forming process facilitates a 30% thickness reduction in the rim section. Furthermore, it leads to a slight increase in yield and tensile strength while significantly improving elongation in parallel to the flow-forming direction. The study also examines microstructural changes, including the deformation behavior of silicon dendrites [5].
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