Training effect on damping capacity in Fe–20 mass% Mn binary alloy

Two-dimensional finite element analysis together with stream function and neural network models are employed to determine thermo-mechanical behavior during hot strip rolling of AA5083. An appropriate velocity field and stream function is first determined using the rule of volume constancy and upper bound theorem and then temperature field within the metal is predicted by means of a two-dimensional conduction–convection model. In order to consider the effect of flow stress and its dependence on temperature, strain and strain rate, a neural network model is also employed in the analysis. Based on the performed tensile tests, two different neural network models are constructed one for smooth yielding and the other one for the serrated flow. Then, the ANN models are coupled with the thermo-mechanical analysis. In the next step, by combination of the predicted temperature, strain and strain rates fields and the experimental data achieved from the tensile tests, the occurence of dynamic strain ageing during hot rolling is predicted. The model predictions are then compared with the experimental data and good agreement is observed between the two sets of results that shows the validity of the proposed model.     

Super TEX-SIM: Interactive textile design and simulation

This paper presents an interactive tool for designing and simulating textile images. It comprises a yarn editor, a textile editor and a texture mapper. These subsystems share a colour editor that can modify the colours of yarn and textile images interactively. We describe how yarn and textile images are generated, and how they are mapped to a target picture read from an image scanner. The user interface in the tool has many useful functions so that users can design textile images without knowledge of computer graphics and programming languages. Several examples of texture mapping are included.