The austenite(γ)reversely transformed from lath martensite(LM),lath bainite(LB),granular bainite(GB)and pearlite+ferrite(P+F)in a high-strength steel was studied at high temperatures using in-situ electron backscatte...The austenite(γ)reversely transformed from lath martensite(LM),lath bainite(LB),granular bainite(GB)and pearlite+ferrite(P+F)in a high-strength steel was studied at high temperatures using in-situ electron backscatter diffraction(EBSD).The memory effect of initial γ significantly affects the nucleation of the reverted γ in LM and GB structures,while a weak influence on that of LB and P+F structures.This results in a significant difference in γ grain size after complete austenitization,with the first two obtaining larger γ grains while the latter two are relatively small.Crystallographic analysis revealed that the reverted γ with acicular morphology(γA),most of which maintained the same orientation with the prior γ,dominated the reaustenitization behavior of LM and GB structures through preferential nucleation within γ grains and coalesced growth modes.Although globular reverted γ(γ_(G))with random orientation or large deviation from the prior γ can nucleate at the grain boundaries or within the grains,it is difficult for it to grow and play a role in segmenting and refining the prior γ due to the inhibition of γ_(A) coalescing.For LB and P+F structures,the nucleation rate of intragranular γ_(G) increases with increasing temperature,and always shows a random orientation.These γ_(G) grains can coarsen simultaneously with the intergranular γ_(G),ultimately playing a role in jointly dividing and refining the finalγgrains.Research also found that the differences in the effects of four different microstructures on revertedγnucleation are closely related to the variant selection of the matrix structure,as well as the content and size of cementite(θ).High density of block boundaries induced by weakening of variant selection and many fineθformed in the lath are the key to promoting LB structure to obtain more intragranular γ_(G) formation,as well as the important role of the large-sized θ in P+F structure.展开更多
Gradient boosting decision tree(GBDT)machine learning(ML)method was adopted for the first time to automatically recognize and conduct quantitative statistical analysis of boundaries in bainitic microstructure using el...Gradient boosting decision tree(GBDT)machine learning(ML)method was adopted for the first time to automatically recognize and conduct quantitative statistical analysis of boundaries in bainitic microstructure using electron back-scatter diffraction(EBSD)data.In spite of lack of large sets of EBSD data,we were successful in achieving the desired accuracy and accomplishing the objective of recognizing the boundaries.Compared with a low model accuracy of<50%as using Euler angles or axis-angle pair as characteristic features,the accuracy of the model was significantly enhanced to about 88%when the Euler angle was converted to overall misorientation angle(OMA)and specific misorientation angle(SMA)and considered as important features.In this model,the recall score of prior austenite grain(PAG)boundary was~93%,high angle packet boundary(OMA>40°)was~97%,and block boundary was~96%.The derived outcomes of ML were used to obtain insights into the ductile-to-brittle transition(DBTT)behavior.Interestingly,ML modeling approach suggested that DBTT was not determined by the density of high angle grain boundaries,but significantly influenced by the density of PAG and packet boundaries.The study underscores that ML has a great potential in detailed recognition of complex multi-hierarchical microstructure such as bainite and martensite and relates to material performance.展开更多
基金financially supported by the National Natural Science Foundation of China(Nos.52271089 and 52001023)the Basic Research and Application Basic Research Foundation of Guangdong Province(Nos.2022A1515240016 and 2023B1515250006)the Fundamental Research Funds for the Central Universities(No.FRF-BD-23-01).
文摘The austenite(γ)reversely transformed from lath martensite(LM),lath bainite(LB),granular bainite(GB)and pearlite+ferrite(P+F)in a high-strength steel was studied at high temperatures using in-situ electron backscatter diffraction(EBSD).The memory effect of initial γ significantly affects the nucleation of the reverted γ in LM and GB structures,while a weak influence on that of LB and P+F structures.This results in a significant difference in γ grain size after complete austenitization,with the first two obtaining larger γ grains while the latter two are relatively small.Crystallographic analysis revealed that the reverted γ with acicular morphology(γA),most of which maintained the same orientation with the prior γ,dominated the reaustenitization behavior of LM and GB structures through preferential nucleation within γ grains and coalesced growth modes.Although globular reverted γ(γ_(G))with random orientation or large deviation from the prior γ can nucleate at the grain boundaries or within the grains,it is difficult for it to grow and play a role in segmenting and refining the prior γ due to the inhibition of γ_(A) coalescing.For LB and P+F structures,the nucleation rate of intragranular γ_(G) increases with increasing temperature,and always shows a random orientation.These γ_(G) grains can coarsen simultaneously with the intergranular γ_(G),ultimately playing a role in jointly dividing and refining the finalγgrains.Research also found that the differences in the effects of four different microstructures on revertedγnucleation are closely related to the variant selection of the matrix structure,as well as the content and size of cementite(θ).High density of block boundaries induced by weakening of variant selection and many fineθformed in the lath are the key to promoting LB structure to obtain more intragranular γ_(G) formation,as well as the important role of the large-sized θ in P+F structure.
基金financially supported by the National Key Research and Development Program of China(No.2017YFB0304900)。
文摘Gradient boosting decision tree(GBDT)machine learning(ML)method was adopted for the first time to automatically recognize and conduct quantitative statistical analysis of boundaries in bainitic microstructure using electron back-scatter diffraction(EBSD)data.In spite of lack of large sets of EBSD data,we were successful in achieving the desired accuracy and accomplishing the objective of recognizing the boundaries.Compared with a low model accuracy of<50%as using Euler angles or axis-angle pair as characteristic features,the accuracy of the model was significantly enhanced to about 88%when the Euler angle was converted to overall misorientation angle(OMA)and specific misorientation angle(SMA)and considered as important features.In this model,the recall score of prior austenite grain(PAG)boundary was~93%,high angle packet boundary(OMA>40°)was~97%,and block boundary was~96%.The derived outcomes of ML were used to obtain insights into the ductile-to-brittle transition(DBTT)behavior.Interestingly,ML modeling approach suggested that DBTT was not determined by the density of high angle grain boundaries,but significantly influenced by the density of PAG and packet boundaries.The study underscores that ML has a great potential in detailed recognition of complex multi-hierarchical microstructure such as bainite and martensite and relates to material performance.
