Software Products for Modelling and Simulation in Materials Science

Models and software products have been developed for modelling, simulation and prediction of different correlations in materials science, including 1. the correlation between processing parameters and properties in titanium alloys and ?-titanium aluminides; 2. time–temperature–transformation (TTT) diagrams for titanium alloys; 3. corrosion resistance of titanium alloys; 4. surface hardness and microhardness profile of nitrocarburised layers; 5. fatigue stress life (S–N) diagrams for Ti–6Al–4V alloys. The programs are based on trained artificial neural networks. For each particular case appropriate combination of inputs and outputs is chosen. Very good performances of the models are achieved. Graphical user interfaces (GUI) are created for easy use of the models. In addition interactive text versions are developed. The models designed are combined and integrated in software package that is built up on a modular fashion. The software products are available in versions for different platforms including Windows 95/98/2000/NT, UNIX and Apple Macintosh. Description of the software products is given, to demonstrate that they are convenient and powerful tools for practical applications in solving various problems in materials science. Examples for optimisation of the alloy compositions, processing parameters and working conditions are illustrated. An option for use of the software in materials selection procedure is described.

An efficient neural network approach for nanoscale FinFET modelling and circuit simulation

The present paper demonstrates the suitability of artificial neural network (ANN) for modelling of a FinFET in nano-circuit simulation. The FinFET used in this work is designed using careful engineering of source-drain extension, which simultaneously improves maximum frequency of oscillation f(max) because of lower gate to drain capacitance, and intrinsic gain A(V0) = g(m)/g(ds), due to lower output conductance g(ds). The framework for the ANN-based FinFET model is a common source equivalent circuit, where the dependence of intrinsic capacitances, resistances and dc drain current I-d on drain-source V-ds and gate-source V-gs is derived by a simple two-layered neural network architecture. All extrinsic components of the FinFET model are treated as bias independent. The model was implemented in a circuit simulator and verified by its ability to generate accurate response to excitations not used during training. The model was used to design a low-noise amplifier. At low power (J(ds) similar to 10 mu A/mu m) improvement was observed in both third-order-intercept IIP3 (similar to 10 dBm) and intrinsic gain A(V0) (similar to 20 dB), compared to a comparable bulk MOSFET with similar effective channel length. This is attributed to higher ratio of first-order to third-order derivative of I-d with respect to gate voltage and lower g(ds), in FinFET compared to bulk MOSFET. Copyright (C) 2009 John Wiley & Sons, Ltd.

Modelling and visualisation of material flow in friction stir spot welding

Abstract The material flow in friction stir spot welding of aluminium to both aluminium and steel has been investigated, using pinless tools in a lap joint geometry. The flow behaviour was revealed experimentally using dissimilar Al alloys of similar strength. The effect on the material flow of tool surface features, welding conditions (rotation speed, plunge depth, dwell time), and the surface state of the steel sheet (un-coated or galvanized) have been systematically studied. A novel kinematic flow model is presented, which successfully predicts the observed layering of the dissimilar Al alloys under a range of conditions. The model and the experimental observations provide a consistent interpretation of the stick-slip conditions at the tool-workpiece interface, addressing an elusive and long-standing issue in the modelling of heat generation in friction stir processing.