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      Combinatorial structural-analytical models for the prediction of the mechanical behaviour of isotropic porous pure metals

      Bolzoni, Leandro; Carson, James K.; Yang, Fei
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      Combinatorial structural-analytical models for the prediction of the mechanical behaviour of isotropic porous pure metals.pdf
      Published version, 2.286Mb
      DOI
       10.1016/j.actamat.2021.116664
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      Bolzoni, L., Carson, J. K., & Yang, F. (2021). Combinatorial structural-analytical models for the prediction of the mechanical behaviour of isotropic porous pure metals. Acta Materialia, 207. https://doi.org/10.1016/j.actamat.2021.116664
      Permanent Research Commons link: https://hdl.handle.net/10289/14119
      Abstract
      This work provides insight on the prediction of the mechanical behaviour of isotropic porous pure metals using empirical and structural-analytical models and proposes two new combinatorial structural-analytical models for the estimation of the mechanical properties. Porous metals such as foams are advanced engineering materials and therefore the prediction of their properties for their optimisation is beneficial. Nevertheless, the estimation of their mechanical behaviour generally relies on semi-empirical models, which are limited to specific materials (i.e. type of metal + type of internal structure + individual property) and for which empirical constants need to be determined. Among the available structural-analytical models, which were developed to estimate mathematically equivalent thermophysical properties, the Symmetric and Interconnected Skeleton Structural (SISS) model gives the best prediction over a broad range of volume fraction of pores (i.e. 0.4–1.0) but always significantly overestimates the elongation to failure. This study presents the derivation of new combinatorial structural-analytical models that are able to rapidly and accurately predict the Young modulus plus ultimate tensile strength and the elongation to failure, respectively, across the entire range of volume fraction of pores. These models have physical bases, are not time- and computing-intensive (thus rapid and low cost), and have reasonable accuracy for materials whose microstructure is uncertain.
      Date
      2021
      Type
      Journal Article
      Publisher
      Elsevier
      Rights
      © 2021 Acta Materialia Inc. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
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      • Science and Engineering Papers [3122]
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