Characterization of grain growth behaviors by BP-ANN and Sellars models for nickle-base superalloy and their comparisons
(1. School of Material Science and Engineering, Chongqing University, Chongqing 400044, China;
2. School of Civil Engineering, Chongqing University, Chongqing 400045, China)
2. School of Civil Engineering, Chongqing University, Chongqing 400045, China)
Abstract: In order to deeply understand the grain growth behaviors of Ni80A superalloy, a series of grain growth experiments were conducted at holding temperatures ranging from 1223 to 1423 K and holding time ranging from 0 to 3600 s. A back-propagation artificial neural network (BP-ANN) model and a Sellars model were solved based on the experimental data. The prediction and generalization capabilities of these two models were evaluated and compared on the basis of four statistical indicators. The results show that the solved BP-ANN model has better performance as it has higher correlation coefficient (r), lower average absolute relative error (AARE), lower absolute values of mean value (μ) and standard deviation (ω). Eventually, a response surface of average grain size to holding temperature and holding time is constructed based on the data expanded by the solved BP-ANN model, and the grain growth behaviors are described.
Key words: grain growth model; BP artificial neural network; Sellars model; average grain size