Sintering temperature influence on grains function distribution by neural network application

Artificial neural networks application in science and techonology begun during 20th century. This biophysical and biomimetic phenomena is based on extensive research which have led to understanding how neural as a living organism nerve system basic element processes signals by a simple algorithm. The input signals are massively parallel processed, and the output presents the superposition of all parallel processed signals. Artificial neural networks which are based on these principles are useful for solving various problems as pattern recognition, clustering, functional optimization. This research analyzed thermophysical parameters at samples based on Murata powders and consolidated by sintering process. Among different physical properties we applied out neural network approach on grain sizes distribution as a function of sintering temperature, T, (from 1190-1370?C). In this paper, we continue to apply neural networks to prognose structural and thermophysical parameters. For consolidation sintering process is very important to prognose and design many parameters but especially thermal like temperature, to avoid long and even wrong experiments which are wasting the time and materials and energy as well. By this artificial neural networks method we indeed provide the most efficient procedure in projecting the mentioned parameters and provide successful ceramics samples production. This is very helpful in prediction and designing the micro-structure parameters important for advance microelectronic further miniaturization development. This is a quite original novelty for real micro-structure projecting especially on the phenomena within the thin films coating around the grains what opens new prospective in advance fractal microelectronics.