Static recrystallization(SRX)behaviors and corresponding recrystallization mechanisms of 7Mo super-austenitic stainless steel were studied under different deformation conditions.The order of influence of deformation p...Static recrystallization(SRX)behaviors and corresponding recrystallization mechanisms of 7Mo super-austenitic stainless steel were studied under different deformation conditions.The order of influence of deformation parameters on static recrystallization behaviors,from high to low,is followed by temperature,first-stage strain and strain rate.Meanwhile,the effect of holding time on static recrystallization behaviors is significantly controlled by temperature.In addition,with the increase in temperature from 1000 to 1200°C,the static recrystallization mechanism evolves from discontinuous static recrystallization and continuous static recrystallization(cSRX)to metadynamic recrystallization and cSRX,and finally to cSRX.The cSRX exists at all temperatures.This is because high stacking fault energy(56 mJ m−2)promotes the movement of dislocations,making the deformation mechanism of this steel is dominated by planar slip of dislocation.Large undissolved sigma precipitates promote static recrystallization through particle-stimulated nucleation.However,small strain-induced precipitates at grain boundaries hinder the nucleation of conventional SRX and the growth of recrystallized grains,while the hindering effect decreases with the increase in temperature.展开更多
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 deformati...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.展开更多
基金supported by National Natural Science Foundation of China(No.U1810207)the Innovation Pilot Project for Fusion of Science,Education and Industry(International Cooperation)from Qilu University of Technology(No.2020KJC-GH03).
文摘Static recrystallization(SRX)behaviors and corresponding recrystallization mechanisms of 7Mo super-austenitic stainless steel were studied under different deformation conditions.The order of influence of deformation parameters on static recrystallization behaviors,from high to low,is followed by temperature,first-stage strain and strain rate.Meanwhile,the effect of holding time on static recrystallization behaviors is significantly controlled by temperature.In addition,with the increase in temperature from 1000 to 1200°C,the static recrystallization mechanism evolves from discontinuous static recrystallization and continuous static recrystallization(cSRX)to metadynamic recrystallization and cSRX,and finally to cSRX.The cSRX exists at all temperatures.This is because high stacking fault energy(56 mJ m−2)promotes the movement of dislocations,making the deformation mechanism of this steel is dominated by planar slip of dislocation.Large undissolved sigma precipitates promote static recrystallization through particle-stimulated nucleation.However,small strain-induced precipitates at grain boundaries hinder the nucleation of conventional SRX and the growth of recrystallized grains,while the hindering effect decreases with the increase in temperature.
文摘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.
