Research on hot deformation behavior of a novel P/M superalloy based on Arrhenius and artificial neural network model
WANG Minxi1,2，LIU Jiantao1,2,3，ZHANG Yiwen1,2,3
（1. High Temperature Material Institute，Central Iron and Steel Research Institute，Beijing 100081，China；
2. Beijing CISRI-GAONAMaterials & Technology Co. Ltd.，Beijing 100081，China；3. Beijing Key Laboratory
of Advanced High Temperature Materials，Beijing 100081，China）
Abstract：Nickel-based superalloy strengthened by γ′ phase (L12 structure) has excellent high temperature
strength, great plasticity and outstanding damage tolerance. These properties make it irreplaceable in many areas,
such as aerospace industry and nuclear power industry. Meanwhile, a series of problems such as segregation, inhomogeneity
and poor hot working performance coming from traditional casting/forging superalloy have been solved
by powder metallurgy process. Nowadays, powder superalloy has become the first choice of key hot end components
such as turbine disc of advanced aero-engine and powder superalloy has become an important symbol of engine
advancement. To obtain desired mechanical properties, superalloy is usually fabricated by extrusion, hot forging
and other processes. Therefore, it is of great importance to study the hot deformation behavior of superalloy.
To study the hot deformation behavior of superalloy, constitutive model is applied to describe the relationship between
flow stress and deformation parameters. Among many constitutive models, Arrhenius-type constitutive model
is widely and commonly used in describing deformation behavior of nickel-based superalloy. Through the regression
analysis, the relationship between flow stress and strain rate, deformation temperature is established and deformation parameters can be optimized to acquire superalloy with homogeneous microstructure and excellent mechanical
properties. However, because of the complex none-linear characteristics of deformation parameters on flow
stress, it is difficult to predict the flow behavior precisely. Thus, tools to describe this relationship more accurately
are urgently needed. Deep learning tools, such as artificial neural network (ANN), is the promising way to solve
this problem. ANN has the ability of self-learning, self-adaptation, strong nonlinear function approximation and
fault tolerance. Meanwhile it does not rely on mathematical models and deformation mechanisms. Through the adjustment
of the internal connections between a large number of nodes, ANN can achieve the purpose of information
processing and predict more accurate than other constitutive models. The effect of deformation temperature and
strain rate on hot deformation behavior of the PM superalloy was investigated. The change in flow stress during hot
deformation actually is the competition between work hardening (dislocation accumulation, dislocation interaction,
etc.) and softening mechanism (DRV, nucleation, grain growth, etc.). The AARE and R of typical Arrhenius-type
model is as large as 23.36% and 0.965 8. After the modification in strain, the AARE and R drops to 9.92% and
0.988 7, respectively. While BP-ANN model is adopted, the value of AARE and R slumps to 1.75% and 0.999 5.
BP-ANN performs better in dealing with the complex relationship between deformation parameter and flow stress.
王旻曦，刘建涛，张义文. 基于 Arrhenius和神经网络模型对镍基合金热变形行为的研究[J]. 粉末冶金工业, 2024, 34(03): 46-53.
WANG Minxi，LIU Jiantao，ZHANG Yiwen. Research on hot deformation behavior of a novel P/M superalloy based on Arrhenius and artificial neural network model. Powder Metallurgy Industry, 2024, 34(03): 46-53.
2024, Vol. 34 
