Thermo-mechanical analyses of heterogeneous materials with a strongly anisotropic phase: the case of cast iron. (15th June 2015)

• Record Type: Journal Article
• Authors: Pina, J.C.; Kouznetsova, V.G.; Geers, M.G.D.
• Is Part Of: International journal of solids and structures. Volume 63(2015)
• Page Start: 153
• Page End: 166
• Publication Date: 2015-06-15
• Subjects: Thermo-mechanical analysis -- Heterogeneous materials -- Anisotropic constituents -- Lamellar cast iron -- Mechanical analysis -- Heat transfer analysis -- Coupled thermo-mechanical analysis; Mechanics, Applied -- Periodicals; Structural analysis (Engineering) -- Periodicals; Elastic solids -- Periodicals; Mécanique appliquée -- Périodiques; Constructions, Théorie des -- Périodiques; Solides élastiques -- Périodiques; Elastic solids; Mechanics, Applied; Structural analysis (Engineering); Periodicals; 624.18
• DOI: 10.1016/j.ijsolstr.2015.02.048
• Languages: English
• ISSNs: 0020-7683

Abstract

Abstract: This work presents a systematic study of thermo-mechanical behaviour of macroscopically isotropic heterogeneous materials with anisotropic constituents based on microstructural modelling. As an example, lamellar cast iron is considered, whose microstructure is composed of spatially interconnected anisotropic graphite particles embedded in ferrite/pearlite matrix. The complex three-dimensional microstructure of lamellar cast iron is represented here by an idealised unit cell model which captures in a simplified manner the main morphological features of the material. The thermal, mechanical and thermo-mechanical response of the unit cell incorporating the highly anisotropic phase is analysed by comparing the results for the equivalent unit cell with the isotropic constituents and considering both fully fixed and loose interface conditions. The major conclusion drawn from these analyses is that the anisotropy of microstructural phases plays crucial role in determining both the effective as well as local response of the material. The simplifying isotropy assumption leads to significantly different predictions at both scales.