Application of novel physical picture based on artificial neural networks to predict microstructure evolution of Al-Zn-Mg-Cu alloy during solid solution process
(1. School of Materials Science and Engineering, Central South University, Changsha 410083, China;
2. Key Laboratory of Nonferrous Metal Materials Science and Engineering, Ministry of Education,
Central South University, Changsha 410083, China;
3. Beijing Institute of Aeronautical Materials, Aviation Industry Corporation of China, Beijing 100095, China)
2. Key Laboratory of Nonferrous Metal Materials Science and Engineering, Ministry of Education,
Central South University, Changsha 410083, China;
3. Beijing Institute of Aeronautical Materials, Aviation Industry Corporation of China, Beijing 100095, China)
Abstract: The effects of the solid solution conditions on the microstructure and tensile properties of Al-Zn-Mg-Cu aluminum alloy were investigated by in-situ resistivity measurement, optical microscopy (OM), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and tensile test. A radial basis function artificial neural network (RBF-ANN) model was developed for the analysis and prediction of the electrical resistivity of the tested alloy during the solid solution process. The results show that the model is capable of predicting the electrical resistivity with remarkable success. The correlation coefficient between the predicted results and experimental data is 0.9958 and the relative error is 0.33%. The predicted data were adopted to construct a novel physical picture which was defined as “solution resistivity map”. As revealed by the map, the optimum domain for the solid solution of the tested alloy is in the temperature range of 465-475 °C and solution time range of 50-60 min. In this domain, the solution of second particles and the recrystallization phenomenon will reach equilibrium.
Key words: aluminum alloy; solution treatment; electrical resistivity; artificial neural network; microstructure evolution