Combinatorial structural-analytical models for the prediction of the mechanical behaviour of isotropic porous pure metals. (1st April 2021)

• Record Type: Journal Article
• Authors: Bolzoni, L.; Carson, J.K.; Yang, F.
• Is Part Of: Acta materialia. Volume 207(2021)
• Publication Date: 2021-04-01
• Subjects: Porous materials -- Mechanical behaviour -- Structural-analytical model -- Modelling -- Sintering; Materials -- Periodicals; Materials science -- Periodicals; Materials -- Mechanical properties -- Periodicals; Metallurgy -- Periodicals; Chemistry, Inorganic -- Periodicals; 620.112
• DOI: 10.1016/j.actamat.2021.116664
• Languages: English
• ISSNs: 1359-6454

Abstract

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. Graphical abstract: Image, graphical abstract