Study on hot deformation behavior and recrystallization mechanism of an Al-6.3Zn-2.5Mg-2.6Cu-0.11Zr alloy based on machine learning

The data from thermal simulation plays a significant role in understanding the deformation behavior and deformation mechanisms of materials, and provides guidance for the design of hot deformation processes. In this paper, thermal simulation experiments were performed on an Al-6.3Zn-2.5Mg-2.6Cu-0.11Zr alloy, and the microstructure after hot deformation were characterized by Electron Backscatter Diffraction (EBSD) technique. The thermal simulation data and microstructural characteristics were analyzed and studied using machine learning technique: the Backpropagation Artificial Neural Network (BP-ANN) method, correlation analysis and K-means clustering analysis. A model for predicting not only flow stress but also dislocation density was established using the BP-ANN method, which exhibits good prediction accuracy. The R-2 and AARE values are 90.19 % and 6.87 % for the prediction of dislocation density, and are 98.67 % and 6.96 % for the prediction of flow stress. Subsequently, correlation analysis was performed to investigate the relationship between energy dissipation efficiency eta and deformation parameters as well as microstructural characteristics, revealing that energy dissipation rate is correlated positively with the content of dynamical recrystallization (DRX) and high angle grain boundary (coefficients of 0.90 and 0.82) and negatively with the dislocation density and the ZenerHollomon parameter (coefficients of -0.98 and -0.97). Additionally, K-means clustering analysis was applied to study the relationship between the energy dissipation efficiency and softening mechanisms, it was found that the softening mechanisms changed with the variation of eta value, dynamic recovery (DV), geometric DRX (GDRX) and continuous DRX (CDRX) dominated when eta>0.38; DV, CDRX and discontinuous DRX(DDRX) dominated when 0.28<eta<0.38; DV dominated when eta<0.28. The results show that machine learning techniques are useful tools in mining thermal simulation data, and more information can be obtained than traditional data mining methods.