Abstract

Nodular cast iron is a Fe-C-Si metallic alloy whose microstructure consists of a matrix, composed in general of ferrite and pearlite, with graphite nodules embedded in it. Owing to its microscopical heterogeneity, the material response is governed by the properties, morphology and typology of the phases involved. This work reports on the evaluation of the elastic properties, i.e., Youngs modulus and Poissons ratio, of an equivalent homogeneous material that characterizes the macroscopic response of a nodular cast iron. Asymptotic homogenization is used to this end. This approach is applied to both 3D and 2D multiparticle cells simulated via the finite element method. Two different physically and geometrically-based criteria are considered to estimate the representative volume element (RVE), where the size of the RVE is found to be sensitive to the chosen criterion. The main microstructural features are obtained from a computational simulation of the solidification process of the material. The numerical predictions computed for the 3D and 2D cases are compared and discussed in terms of the resulting elastic properties. It is observed that the models employing 3D multiparticle cells require lower RVE sizes than the corresponding 2D models, where the latter present a stiffer elastic response.

Abstract

The development and optimization of Micro electro-mechanical systems (MEMS) devices, due to their small size scale, require testing and precise characterization. As an example, over etch, which is the deviation between the designed masks and the effective dimensions of the suspended parts, strongly influences the performances of MEMS; therefore, to predict the correct functioning of the device its actual value must be carefully identified. In this work, we propose an efficient, time-saving tool to identify fabrication imperfections in MEMS devices. In particular, we replace the complex geometry of a MEMS mechanical filter with an equivalent homogeneous medium, whose linear-elastic effective properties are evaluated employing twoscale asymptotic homogenization and we identify the over etch by minimizing the relative error between experimental data and corresponding predictions obtained for different combinations of over etch.