Neural-network modeling of hot-compression test curves for calendering gasket materials Original Research Article

In this paper, we report on the use of neural networks (NNs) to estimate hot-compression test (HCT) curves for calendering gasket materials on the basis of their formulas. The NNs were used to demonstrate their potential during optimizing formulas for new calendering gasket materials. In the past, and to a large extent even now, the optimization of a new calendering gasket material was based on a process of trial and error, which takes a long time and is expensive because of the need for repeated experimental tests. And even after the completion of all this testing the final formula of the gasket material need not necessarily be the optimum one. We have shown that it is possible, with the assistance of a NN that was trained with appropriate data from just a small number of HCT curves, to satisfactorily investigate the valid ranges of the input data. On the basis of this investigation some valuable information was obtained that will make it easier to develop new calendering gasket materials. Using NNs, the speed of convergence to the final formula of the calendering gasket material can be much faster, because there is no need carry out many experimental HCT tests.