The thermal deformation behavior of FV520B stainless steel is investigated.Isothermal compression tests were conducted at temperatures ranging from 600 to 900℃ and strain rates from 0.001 to 10 s^(−1).The true stress...The thermal deformation behavior of FV520B stainless steel is investigated.Isothermal compression tests were conducted at temperatures ranging from 600 to 900℃ and strain rates from 0.001 to 10 s^(−1).The true stress–strain curves were corrected for friction and temperature due to the drum shape and adiabatic heating.The comparison shows that there is a large difference between the stress before and after the correction,which proves that the correction is necessary.Five constitutive models were developed:the original Arrhenius model,the strain correction Arrhenius model,a new modified Arrhenius model,the back propagation neural network model(BPNN)and the dandelion optimization BPNN model(DO-BPNN).The DO-BPNN model showed the highest prediction accuracy though it was more computationally intensive than the other models.The new modified Arrhenius model performed a better predictive capacity than the strain correction version,while it showed a negligible increase in the number of parameters and computational time.Although artificial neural network-based models exhibit superior accuracy compared to the Arrhenius models,their application in finite element simulations still faces notable challenges.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.52275373)the National Natural Science Foundation of China(Grant No.52105397)the Open Foundation of National Key Laboratory of Metal Forming Technology and Heavy Equipment(Grant No.S2308100.W08).
文摘The thermal deformation behavior of FV520B stainless steel is investigated.Isothermal compression tests were conducted at temperatures ranging from 600 to 900℃ and strain rates from 0.001 to 10 s^(−1).The true stress–strain curves were corrected for friction and temperature due to the drum shape and adiabatic heating.The comparison shows that there is a large difference between the stress before and after the correction,which proves that the correction is necessary.Five constitutive models were developed:the original Arrhenius model,the strain correction Arrhenius model,a new modified Arrhenius model,the back propagation neural network model(BPNN)and the dandelion optimization BPNN model(DO-BPNN).The DO-BPNN model showed the highest prediction accuracy though it was more computationally intensive than the other models.The new modified Arrhenius model performed a better predictive capacity than the strain correction version,while it showed a negligible increase in the number of parameters and computational time.Although artificial neural network-based models exhibit superior accuracy compared to the Arrhenius models,their application in finite element simulations still faces notable challenges.
