Prediction of hot deformation behavior in Ni-based alloy considering the effect of initial microstructure

Progress in Natural Science: Materials International

Zhonghua Zhang¹  Changfeng Chen²  Lei Wang³  Feng Liu³  Qiang Zuo³ 

[1] Baoshan Iron & Steel Co., Ltd., Shanghai 201900, China;Department of Materials Science and Engineering, China University of Petroleum, Beijing 102249, China;State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi׳an, Shaanxi 710072, China;

关键词: Initial microstructure;    Ni-based alloy;    Hot deformation behavior;    Constitutive equations;    Artificial neural network;   

DOI  :  10.1016/j.pnsc.2015.01.007

来源: DOAJ

【 摘 要 】

High temperature heat treatments were conducted for as-cast N08028 alloy to obtain various microstructures with different amounts of σ-phase, and then hot compression tests were carried out using Gleeble-3500 thermo-mechanical simulator in deformation temperature range from 1100 to 1200 °C and strain rate range from 0.01 to 1 s−1. For the same initial microstructure, the flow stress was observed to increase with increasing the strain rate and decreasing the deformation temperature, while for the same deformation condition, the flow stress was found to increase with increasing the amount of σ-phase in the initial microstructure. Moreover, dynamic recrystallization was found to be the main dynamic soften mechanism. On this basis, Arrhenius-type constitutive equations and artificial neural network (ANN) model with back-propagation learning algorithm were established to predict hot deformation behavior of the alloy. Furthermore, the parameters of constitutive equations were found to be dependent on the initial microstructure, which was also as one of the inputs for the ANN model. Suitability of the two models was evaluated by comparing the accuracy, correlation coefficient and average absolute relative error, of the prediction. It is concluded that the ANN model is more accurately than the constitutive equations.

【 授权许可】

Unknown