Research on hot deformation behavior of a novel P/M superalloy based on Arrhenius and artificial neural network model
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,inho-mogeneity 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 compo-nents such as turbine disc of advanced aero-engine and powder superalloy has become an important symbol of en-gine advancement.To obtain desired mechanical properties,superalloy is usually fabricated by extrusion,hot forg-ing 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 be-tween flow stress and deformation parameters.Among many constitutive models,Arrhenius-type constitutive mod-el is widely and commonly used in describing deformation behavior of nickel-based superalloy.Through the regres-sion analysis,the relationship between flow stress and strain rate,deformation temperature is established and defor-mation parameters can be optimized to acquire superalloy with homogeneous microstructure and excellent mechan-ical 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 ad-justment 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.
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