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    Modeling and characterization of mechanical properties in laser powder bed fusion additive manufactured Inconel 718

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    Author
    Moorthy, Senthamilaruvi
    Advisor
    Stebner, Aaron P.
    Date issued
    2018
    Keywords
    additive manufacturing
    machine learning
    selective laser melting
    Inconel 718
    3D printing
    mechanical properties
    
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    URI
    https://hdl.handle.net/11124/172164
    Abstract
    Laser powder bed fusion (L-PBF) is a promising additive manufacturing process capable of manufacturing near net shaped components directly from digital computer-aided-design (CAD) data. Lack of consistency in mechanical properties of L-PBF printed parts prevent widespread adaptation of this technique in industry. To understand the factors that cause variability and inconsistency in mechanical properties two plates each of tensile and compression samples were L-PBF printed in various orientations using Inconel 718 alloy. One plate each of compression and tensile samples was subjected to solution annealing and double aging heat treatment. Tensile, compressive and hardness properties were measured in as built and heat-treated condition. Compressive properties were also characterized in the machined condition to understand the influence of all post-processing activities on mechanical properties. Porosity of compression samples was characterized with X-ray micro computed tomography to understand the influence of porosity on mechanical properties. Apart from build orientation, factors such as shape, thickness and laser scanning path were found to cause variation in mechanical properties. Anisotropy in mechanical properties that developed because of build orientation and laser scanning path was retained after heat treatment. Hardness increased by 58% after heat treatment. More than 50% of porosity by volume was found to be removed after machining compression samples from 2mm nominal diameter down to 1.5mm diameter. The samples also showed higher yield and Young's modulus after machining. An attempt has been made to explain the observed variability in mechanical properties across orientation and part position on the build plate using machine learning algorithms. Although the regression approach could not explain the variability, the classification technique seems to be a plausible approach. Orientation and position could not completely explain the variability in mechanical properties. This suggests that more variables are involved in determining the final mechanical properties of L-PBF printed parts.
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