Optimization of the processing parameters during internal oxidation of Cu-Al alloy powders using an artificial neural network

Internal oxidation is a commercial method for producing oxide dispersion strengthened copper (ODS Cu). In this paper, the dilute Cu-Al alloy powders containing 0.26 wt% of Al have been internally oxidized at temperatures (T) from 700 to 1000 degrees C, for holding times (t) up to 10 h. The alumina particle size has been observed and determined by electron microscopy using the two-stage preshadowed carbon replica method. By the use of backpropagation network, the non-linear relationship between internal oxidation process parameters (T, t) and alumina particle size has been established on the base of dealing with the experimental data. The results show that the well-trained backpropagation neural network can predict the alumina, particle size during internal oxidation precisely and the prediction values have sufficiently mined the basic domain knowledge of internal oxidation process. Therefore, a new way, of optimizing process parameters has been provided by the authors. (c) 2004 Elsevier Ltd. All rights reserved.