Microstructural changes during heating of highly alloyed Cr26Ni7 type super duplex stainless steel （SDSS2607） and its thermal deformation behavior were investigated. At different heating rates, the mechanism of phas...Microstructural changes during heating of highly alloyed Cr26Ni7 type super duplex stainless steel （SDSS2607） and its thermal deformation behavior were investigated. At different heating rates, the mechanism of phase transition from y phase to 6 phase and growth modes of ~ phase differed. Variations in microstructures for as- cast SDSS2607 during heat preservation at 1 220 ~C indicated two kinds of transformations from y phase to 6 phase. In-situ observations of microstructural changes during the tensile process at 1 050 showed a mutual coordination between y and 6 phases. When the true strain increased, the mutual coordination between 7 and 6 phases was damaged. Subsequently, cracks nucleated at the ＂y/g interface. With the increase in temperature, the strength of as- cast SDSS2607 decreased while its plasticity increased. Its thermoplasticity was poor, and the reduction in area of tensile specimens was less than 80%. When the deformation strain of hot compression increased, the stable deformation zone in the heat processing maps enlarged gradually. Moreover, the unstable deformation zones were extended.展开更多
A porous yttrium oxide crucible with both thermal shock resistance and erosion resistance was developed by structural optimization.The structure-optimized yttrium oxide crucible was proved to be suitable for melting h...A porous yttrium oxide crucible with both thermal shock resistance and erosion resistance was developed by structural optimization.The structure-optimized yttrium oxide crucible was proved to be suitable for melting highly reactive titanium alloys.Low-cost(TiB+Y2O_(3))-reinforced titanium matrix composites were prepared by vacuum induction melting using the prepared crucible.The thermal deformation behavior and microstructure evolution of(TiB+Y2O_(3))-reinforced tita-nium matrix composites were investigated at deformation temperatures of 900-1100℃with strain rates of 0.001-1 s-1.The results showed that the prepared yttrium oxide crucible had both thermal shock and erosion resistance,the low-cost titanium matrix composites could be prepared by the developed yttrium oxide crucibles which were homogeneous in composition and highly sensitive to strain rate and deformation temperature,and the peak and theological stresses decreased with increasing deformation temperature or decreasing strain rate.In addition,the average thermal deformation activation energy of the composites was calculated to be 574.6 kJ/mol by establishing the Arrhenius constitutive equation in consideration of the strain variables,and the fitting goodness between the predicted stress value and the measured value was 97.624%.The calculated analysis of the hot processing map showed that the best stable thermal deformation zone was located in the deformation temperature range of 1000-1100℃and strain rate range of 0.001-0.01 s^(-1),where the peak dissipation coefficient wasη=71%.In this zone,the deformation of the reinforcement and matrix was harmonious,the reinforcement was less likely to fracture,dynamic recrystallization occurred more fully and the alloy exhibited near steady rheological characteristics.展开更多
The thermal deformation behavior of a novel CrFeNiSi_(0.15)medium entropy alloy(MEA)was studied via isothermal compression experiments,with the processing parameter range of 900–1200℃and 0.001–1 s^(−1).According to...The thermal deformation behavior of a novel CrFeNiSi_(0.15)medium entropy alloy(MEA)was studied via isothermal compression experiments,with the processing parameter range of 900–1200℃and 0.001–1 s^(−1).According to experimental data,the modified constitutive equation had been obtained,which precisely predicted the flow behavior of CrFeNiSi_(0.15)MEA during thermal deformation.At the same time,the processing map was established on the basis of the dynamic material model(DMM)theory.According to the map,the optimal processing parameters were determined at 1130–1200℃/0.06–1 s−1,under which the power dissipation efficiency could reach above 34%.The peak efficiency was above 38%,which occurred at 1200℃/1 s^(−1).In such parameter,complete dynamic recrystallization(DRX)also occurred.The flow instability of CrFeNiSi_(0.15)MEA was estimated to occur at 900–985℃/0.12–1 s^(−1),which was shown as grain boundaries cracking.Furthermore,both the continuous DRX(CDRX)and discontinuous DRX(DDRX)occurred simultaneously during thermal deformation.Meanwhile,some twins were also newly formed during DRX process,most of which were primary twins.The occurrence of twinning was beneficial to promote the development of DRX behavior.展开更多
The study of how to “control forming and performance” during the thermal deformation of metal materials has always been a central theme in academic research, particularly in addressing the processing challenges asso...The study of how to “control forming and performance” during the thermal deformation of metal materials has always been a central theme in academic research, particularly in addressing the processing challenges associated with difficult-to-form alloys that possess unique functionalities. However, neither the currently commonly used phenomenological constitutive model, physical constitutive model, Dynamic Material Model (DMM) thermal processing theoretical model, and Ruano-Wadsworth-Sherby (R-W-S) deformation mechanism map model incorporating dislocation density nor the reported machine learning method has established a universal model that can achieve a quantitative description of the process-microstructure-formability of thermal processing. It is only possible first to use modeling research to obtain the law of thermal deformation behavior of alloys and then use the results of microscopic characterization to verify the theory. The research methods lack the characteristics of diagnosis and prediction optimization. This study proposes a machine learning framework for optimizing the random forest (RF) model based on a multivariate decision tree, including microstructure images and hot working process parameter information. It predicts the critical performance parameters, energy dissipation behavior, optimal processing window, and softening mechanism of ternary shape memory alloy Ni_(47)Ti_(33)Hf_(20) in the hot working process. This model has a certain universality. It enables coupled analysis of image information and process parameter data and introduces the calculation and ranking of feature importance, reflecting the applicability of feature values in model construction. Finally, the visualization technique Grad-CAM describes the correlation between the input microscopic image and the output, generating critical hotspots in the heat map. The model of accuracy in predicting the power dissipation rate is confirmed by the grain misorientation angles, thus realizing the establishment of a mechanism-driven model based on the evolution of critical microscopic structures during the thermal deformation of the alloy, which dramatically improves the interpretability of the machine learning model. This machine learning framework provides valuable guidance for quantitatively predicting the thermal deformation processing-microstructure-formability relationship of the Ni_(47)Ti_(33)Hf_(20) shape memory alloy and can potentially be applied to other alloys.展开更多
基金sponsored by National key R & D program (No.2016YFB0300201)
文摘Microstructural changes during heating of highly alloyed Cr26Ni7 type super duplex stainless steel （SDSS2607） and its thermal deformation behavior were investigated. At different heating rates, the mechanism of phase transition from y phase to 6 phase and growth modes of ~ phase differed. Variations in microstructures for as- cast SDSS2607 during heat preservation at 1 220 ~C indicated two kinds of transformations from y phase to 6 phase. In-situ observations of microstructural changes during the tensile process at 1 050 showed a mutual coordination between y and 6 phases. When the true strain increased, the mutual coordination between 7 and 6 phases was damaged. Subsequently, cracks nucleated at the ＂y/g interface. With the increase in temperature, the strength of as- cast SDSS2607 decreased while its plasticity increased. Its thermoplasticity was poor, and the reduction in area of tensile specimens was less than 80%. When the deformation strain of hot compression increased, the stable deformation zone in the heat processing maps enlarged gradually. Moreover, the unstable deformation zones were extended.
基金supported by the National Natural Science Foundation of China(52071065)the Fundamental Research Funds for the Central Universities(N2007007)+1 种基金the National Key R&D Program of China(2016YFB-0301201)the Ten Technology Research Projects of Hunan Province(No.2022GK1050).
文摘A porous yttrium oxide crucible with both thermal shock resistance and erosion resistance was developed by structural optimization.The structure-optimized yttrium oxide crucible was proved to be suitable for melting highly reactive titanium alloys.Low-cost(TiB+Y2O_(3))-reinforced titanium matrix composites were prepared by vacuum induction melting using the prepared crucible.The thermal deformation behavior and microstructure evolution of(TiB+Y2O_(3))-reinforced tita-nium matrix composites were investigated at deformation temperatures of 900-1100℃with strain rates of 0.001-1 s-1.The results showed that the prepared yttrium oxide crucible had both thermal shock and erosion resistance,the low-cost titanium matrix composites could be prepared by the developed yttrium oxide crucibles which were homogeneous in composition and highly sensitive to strain rate and deformation temperature,and the peak and theological stresses decreased with increasing deformation temperature or decreasing strain rate.In addition,the average thermal deformation activation energy of the composites was calculated to be 574.6 kJ/mol by establishing the Arrhenius constitutive equation in consideration of the strain variables,and the fitting goodness between the predicted stress value and the measured value was 97.624%.The calculated analysis of the hot processing map showed that the best stable thermal deformation zone was located in the deformation temperature range of 1000-1100℃and strain rate range of 0.001-0.01 s^(-1),where the peak dissipation coefficient wasη=71%.In this zone,the deformation of the reinforcement and matrix was harmonious,the reinforcement was less likely to fracture,dynamic recrystallization occurred more fully and the alloy exhibited near steady rheological characteristics.
基金the Project of Natural Science Foundation of Shandong Province(No.ZR2021ME234).
文摘The thermal deformation behavior of a novel CrFeNiSi_(0.15)medium entropy alloy(MEA)was studied via isothermal compression experiments,with the processing parameter range of 900–1200℃and 0.001–1 s^(−1).According to experimental data,the modified constitutive equation had been obtained,which precisely predicted the flow behavior of CrFeNiSi_(0.15)MEA during thermal deformation.At the same time,the processing map was established on the basis of the dynamic material model(DMM)theory.According to the map,the optimal processing parameters were determined at 1130–1200℃/0.06–1 s−1,under which the power dissipation efficiency could reach above 34%.The peak efficiency was above 38%,which occurred at 1200℃/1 s^(−1).In such parameter,complete dynamic recrystallization(DRX)also occurred.The flow instability of CrFeNiSi_(0.15)MEA was estimated to occur at 900–985℃/0.12–1 s^(−1),which was shown as grain boundaries cracking.Furthermore,both the continuous DRX(CDRX)and discontinuous DRX(DDRX)occurred simultaneously during thermal deformation.Meanwhile,some twins were also newly formed during DRX process,most of which were primary twins.The occurrence of twinning was beneficial to promote the development of DRX behavior.
文摘The study of how to “control forming and performance” during the thermal deformation of metal materials has always been a central theme in academic research, particularly in addressing the processing challenges associated with difficult-to-form alloys that possess unique functionalities. However, neither the currently commonly used phenomenological constitutive model, physical constitutive model, Dynamic Material Model (DMM) thermal processing theoretical model, and Ruano-Wadsworth-Sherby (R-W-S) deformation mechanism map model incorporating dislocation density nor the reported machine learning method has established a universal model that can achieve a quantitative description of the process-microstructure-formability of thermal processing. It is only possible first to use modeling research to obtain the law of thermal deformation behavior of alloys and then use the results of microscopic characterization to verify the theory. The research methods lack the characteristics of diagnosis and prediction optimization. This study proposes a machine learning framework for optimizing the random forest (RF) model based on a multivariate decision tree, including microstructure images and hot working process parameter information. It predicts the critical performance parameters, energy dissipation behavior, optimal processing window, and softening mechanism of ternary shape memory alloy Ni_(47)Ti_(33)Hf_(20) in the hot working process. This model has a certain universality. It enables coupled analysis of image information and process parameter data and introduces the calculation and ranking of feature importance, reflecting the applicability of feature values in model construction. Finally, the visualization technique Grad-CAM describes the correlation between the input microscopic image and the output, generating critical hotspots in the heat map. The model of accuracy in predicting the power dissipation rate is confirmed by the grain misorientation angles, thus realizing the establishment of a mechanism-driven model based on the evolution of critical microscopic structures during the thermal deformation of the alloy, which dramatically improves the interpretability of the machine learning model. This machine learning framework provides valuable guidance for quantitatively predicting the thermal deformation processing-microstructure-formability relationship of the Ni_(47)Ti_(33)Hf_(20) shape memory alloy and can potentially be applied to other alloys.
