Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/45438
Título: | Predictive models for physical and mechanical properties of 316L stainless steel produced by selective laser melting |
Autor(es): | 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 |
Palavras-chave: | Selective laser melting 316L stainless steel Predictive models Density Mechanical properties |
Data: | 7-Mar-2016 |
Editora: | Elsevier 1 |
Revista: | Materials Science and Engineering: A |
Resumo(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. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/45438 |
DOI: | 10.1016/j.msea.2016.01.028 |
ISSN: | 0921-5093 |
Versão da editora: | http://www.sciencedirect.com/science/article/pii/S0921509316300272 |
Arbitragem científica: | yes |
Acesso: | Acesso restrito UMinho |
Aparece nas coleções: | CMAT - Artigos em revistas com arbitragem / Papers in peer review journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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Predictivemodels_2016.pdf Acesso restrito! | 1,64 MB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons