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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/7500

    Título
    Parameter Optimisation in Selective Laser Melting on C300 Steel
    Autor
    Cuesta Segura, Isidoro IvánAutoridad UBU Orcid
    Díaz Portugal, AndrésAutoridad UBU Orcid
    Rojo, M. A.
    Peral, Luis Borja
    Martínez, J.
    Alegre Calderón, Jesús ManuelAutoridad UBU Orcid
    Publicado en
    Applied sciences. 2022, V. 12, n. 19, 9786
    Editorial
    MDPI
    Fecha de publicación
    2022-09
    DOI
    10.3390/app12199786
    Résumé
    Additive manufacturing (AM) of metallic materials is increasingly being adopted in numerous sectors, such as biomedicine, aerospace or automotive industries, due to its versatility in the creation of complex geometries and the minimisation of material waste when compared to traditional subtractive methods. In order to ensure a reliable operation of these parts, however, an in-depth study of the effect of additive manufacturing on mechanical properties, including tensile, fatigue and fracture resistance, is necessary. Among the vast number of methods and materials, this project is focused in one of the most promising techniques for the industry: Selective Laser Melting (SLM) for the production of a tools steel, in particular C300 steel components for the automotive sector. The main objective of this paper is to optimise some of the key parameters in the printing process, such as laser power, laser speed and hatch spacing. These variables are essential to obtain parts with good resistance. To that purpose, tensile tests were performed in 3D printed specimens, and then elastoplastic properties were extracted, organised and analysed through a design of experiments for the subsequent output fitting using the response surface methodology.
    Palabras clave
    Response surface
    Additive manufacturing
    Selective laser melting
    C300 maraging steel
    Materia
    Ingeniería civil
    Civil engineering
    Materiales
    Materials
    URI
    http://hdl.handle.net/10259/7500
    Versión del editor
    https://doi.org/10.3390/app12199786
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    Cuesta-as_2022.pdf
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    24.22Mo
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