Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/45438

TitlePredictive models for physical and mechanical properties of 316L stainless steel produced by selective laser melting
Author(s)Miranda, Maria Georgina Macedo
Faria, Susana
Bartolomeu, F.
Pinto, E.
Madeira, S.
Mateus, A.
Carreira, P.
Alves, N.
Silva, F. S.
Carvalho, Óscar Samuel Novais
KeywordsSelective laser melting
316L stainless steel
Predictive models
Density
Mechanical properties
Issue date7-Mar-2016
PublisherElsevier
JournalMaterials Science and Engineering: A
Abstract(s)Selective Laser Melting (SLM) processing parameters are known to greatly influence 316L stainless steel final properties. A simple energy density calculation is insufficient for explaining mechanical and physical properties as well as microstructural characteristics, which are known to significantly influence these parts performance. In fact, parts produced by using different combinations of processing parameters, even presenting similar energy density, can display different properties. Thus, it is necessary to assess their influence as isolated parameters but also their interactions. This work presents a study on the influence of several SLM processing parameters (laser power, scanning speed and scanning spacing) on density, hardness and shear strength of 316L stainless steel. The influence of these processing parameters on the abovementioned properties is assessed by using statistical analysis. In order to find the significant main factors and their interactions, analysis of variance (ANOVA) is used. Furthermore, in order to assess the effect of the part building orientation, two different building strategies were tested. The influence of these processing parameters on shear strength, hardness and density were assessed for the two building strategies, thus resulting six different models that can be used as predictive design tools. The microstructures experimentally obtained were analyzed, discussed and correlated with the obtained models.
TypeArticle
URIhttp://hdl.handle.net/1822/45438
DOI10.1016/j.msea.2016.01.028
ISSN0921-5093
Publisher versionhttp://www.sciencedirect.com/science/article/pii/S0921509316300272
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CMAT - Artigos em revistas com arbitragem / Papers in peer review journals

Files in This Item:
File Description SizeFormat 
Predictivemodels_2016.pdf
  Restricted access
1,64 MBAdobe PDFView/Open

This item is licensed under a Creative Commons License Creative Commons

Partilhe no FacebookPartilhe no TwitterPartilhe no DeliciousPartilhe no LinkedInPartilhe no DiggAdicionar ao Google BookmarksPartilhe no MySpacePartilhe no Orkut
Exporte no formato BibTex mendeley Exporte no formato Endnote Adicione ao seu ORCID