Prediction of Hot Deformation Behavior of 7Mo Super Austenitic Stainless Steel Based on Back Propagation Neural Network

The hot compression tests of 7Mo super austenitic stainless(SASS)were conducted to obtain flow curves at the temperature of 1000-1200℃and strain rate of 0.001 s^(-1)to 1 s^(-1).To predict the non-linear hot deformation behaviors of the steel,back propagation-artificial neural network(BP-ANN)with 16×8×8 hidden layer neurons was proposed.The predictability of the ANN model is evaluated according to the distribution of mean absolute error(MAE)and relative error.The relative error of 85%data for the BP-ANN model is among±5%while only 42.5%data predicted by the Arrhenius constitutive equation is in this range.Especially,at high strain rate and low temperature,the MAE of the ANN model is 2.49%,which has decreases for 18.78%,compared with conventional Arrhenius constitutive equation.

Comparison of flow behaviors of near beta Ti-55511 alloy during hot compression based on SCA and BPANN models

The flow behavior of Ti-55511 alloy was studied by hot compression tests at temperatures of 973−1123 K and strain rates of 0.01−10 s^(−1).Strain-compensated Arrhenius(SCA)and back-propagation artificial neural network(BPANN)methods were selected to model the constitutive relationship,and the models were further evaluated by statistical analysis and cross-validation.The stress−strain data extended by two models were implanted into finite element to simulate hot compression test.The results indicate that the flow stress is sensitive to deformation temperature and strain rate,and increases with increasing strain rate and decreasing temperature.Both the SCA model fitted by quintic polynomial and the BPANN model with 12 neurons can describe the flow behaviors,but the fitting accuracy of BPANN is higher than that of SCA.Sixteen cross-validation tests also confirm that the BPANN model has high prediction accuracy.Both models are effective and feasible in simulation,but BPANN model is superior in accuracy.

Variation of activation energy determined by a modified Arrhenius approach:Roles of dynamic recrystallization on the hot deformation of Ni-based superalloy

The hot deformation behaviors of Ni18 Cr9 Co9 Fe5 Nb3 Mo superalloy were explored in the formation temperature range free ofγ’phase with various strain rates applied.The hot deformation behaviors are initially modeled with Arrhenius equation which gives an average activation energy of 581.1 kJ mol^(-1).A modified Arrhenius approach,including the updated Zener-Hollomon parameter is proposed to consider the change of activation ene rgy under different deformation conditions which turns out a relatively accurate computation for activation energy of hot deformation,i.e.,the standard variance for modified model calculated in the covered deformation condition is just 35.4%of that for Arrhenius equation.The modified model also proposes a map for activation ene rgy which ranges from 571.5-589.0 kJ mol^(-1)for various deformation conditions.Microstructural features of the representative superalloy specimens were characterized by electron backscattered diffraction(EBSD)techniques in order to clarify the influence of activation energy on the microstructural formation.It is found that the Ni-based superalloy samples with higher activation energy are promoted by the degree of dynamic recrystallization which suggests that the rise in activation energy gives either a better recrystallization rate or finer grains.