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Efficient optimization methodology for laser powder bed fusion parameters to manufacture dense and mechanically sound parts validated on AlSi12 alloy

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  • Additional Information
    • Contributors:
      UCL - SST/IMMC/IMAP - Materials and process engineering
    • Publication Information:
      Elsevier BV
    • Publication Date:
      2021
    • Collection:
      DIAL@UCL (Université catholique de Louvain)
    • Abstract:
      Themain drawback of laser powder bed fusion (L-PBF), commonly called selective lasermelting (SLM) is the high porosity which may lead to an early failure of the parts. To minimize it, the L-PBF parameters need to be optimized focusing on the laser power, scanning speed and hatching space. However, no standard guideline exists. In this study, an efficient and cost-effective methodology is developed and validated on AlSi12. This innovative methodology brings together single scan tracks (SST),macroscopic properties analysis and design of experiments (DOE). It requires three batches of SST, cubes and tensile samples. The DOE significantly decreases the manufacturing and characterization costs. 9 SST are sufficient to identify a process window that is 85% similar to the one obtained froma full factorial designwith 105 SST. This process windowreliably leads to high densities and better mechanical properties in comparison to the state-of-the-art properties reported in the literature for L-PBF AlSi12. In conclusion, a methodology using only 9 SST, 18 cubes and 12 tensile tests has been validated on AlSi12. It is further envisioned to optimize the L-PBF parameters of any existing or new alloy leading potentially towards better mechanical properties than the state-of-the-art in the literature.
    • Relation:
      info:eu-repo/grantAgreement/European Research Council (ERC)/ALUFIX/Grant agreement n°716678; info:eu-repo/grantAgreement/European Funds for Regional Developments (FEDER)//; info:eu-repo/grantAgreement/Walloon Region//n°101628-722943; boreal:240767; http://hdl.handle.net/2078.1/240767
    • Accession Number:
      10.1016/j.matdes.2020.109433
    • Online Access:
      http://hdl.handle.net/2078.1/240767
      https://doi.org/10.1016/j.matdes.2020.109433
    • Rights:
      info:eu-repo/semantics/openAccess
    • Accession Number:
      edsbas.F3816663