Crush performance of additively manufactured maraging steel microlattice reinforced plates
Source abstract
Abstract
Abstract Additive metal manufacturing techniques, in particular laser-based powder bed fusion methods, are revolutionary in their capabilities to fabricating new classes of lightweight and complex materials called metallic microlattices. In this paper, a microlattice structure was designed for energy absorption purposes and further additively manufactured using maraging steel (Maraging300) powder through Laser-powder bed fusion (L-PBF) technique. In addition, several cylindrical bars and cubes in horizontal and vertical directions were manufactured to perform uniaxial tensile and compression tests on bulk L-PBF Maraging300. The manufactured microlattices were characterized using different electron microscopy techniques including scanning electron microscopy and micro-CT analysis to ensure that the desired structural integrity was achieved. In addition, the microlattice was then crushed using a universal mechanical testing machine to evaluate its performance experimentally under quasi-static uniaxial compressive loading conditions. Along with the crush, a nonlinear finite element model with predictive capabilities was then developed using commercial package (LS-DYNA) for axial crush of the microlattice to compare its expected performance. Two finite element models are developed using the as-built geometry from the printed lattice, and the as-designed geometry. The effect of model parameters is discussed and very good agreement between the experimental results and the finite element prediction was observed. Finally statistical analysis of the model and failure analysis of the crushed lattice is presented.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Additive Manufacturing Materials and Processes · Additive Manufacturing and 3D Printing Technologies · Cellular and Composite Structures
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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