The active development of space industry necessitates the cre-ation of novel materials with unique properties,including shape memory alloys(SMAs).The development of ultra-high temperature SMAs(UHTSMAs)with operating t...The active development of space industry necessitates the cre-ation of novel materials with unique properties,including shape memory alloys(SMAs).The development of ultra-high temperature SMAs(UHTSMAs)with operating temperatures above 400℃is a significant challenge[1-3].It is known that reversible thermoelas-tic martensitic transformation(MT)is the basis for shape mem-ory behavior[4].Currently,there are several systems in which MT temperatures meet the above requirements,for example,RuNb[5],HfPd[6],TiPd[7].展开更多
Precipitate hardening is the most easiest and effective way to enhance strain recovery properties in NiTiHf high-temperature shape memory alloys.This paper discusses the precipitation,coarsening and age hardening of H...Precipitate hardening is the most easiest and effective way to enhance strain recovery properties in NiTiHf high-temperature shape memory alloys.This paper discusses the precipitation,coarsening and age hardening of H-phase precipitates in Ni_(50)Ti_(30)Hf_(20)alloy during isothermal aging at temperatures between 450℃and 650℃for time to 75 h.The H-phase mean size and volume fraction were determined using transmission electron microscopy.Precipitation kinetics was analyzed using the Johnson-Mehl-Avrami-Kolmogorov equation and an Arrhenius type law.From these analyses,a Time-Temperature-Transformation diagram was constructed.The evolution of H-phase size suggests classical matrix diffusion limited Lifshitz-Slyozov-Wagner coarsening for all considered temperatures.The coarsening rate constants of H-phase precipitation have been determined using a modified coarsening rate equation for nondilute solutions.Critical size of H-phase precipitates for breaking down the precipitate/matrix interface coherency was estimated through a combination of age hardening and precipitate size evolution data.Moreover,time-temperature-hardness diagram was constructed from the precipitation and coarsening kinetics and age hardening of H-phase precipitates in Ni_(50)Ti_(30)Hf_(20)alloy.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
The martensitic phase transformation in Ti_(40.4)Ni_(48)Hf_(11.6) shape memory alloys is leveraged for bi-directional actuation with TiNiHf/SiO_(2)/Si com-posites.The shape memory properties of magnetron sputtered Ti_...The martensitic phase transformation in Ti_(40.4)Ni_(48)Hf_(11.6) shape memory alloys is leveraged for bi-directional actuation with TiNiHf/SiO_(2)/Si com-posites.The shape memory properties of magnetron sputtered Ti_(40.4)Ni_(48)Hf_(11.6) films annealed at 635℃-5 min are influenced by film thickness and the underlying substrate.Decreasing TiNiHf film thick-ness from 21μm to 110 nm results in the reduction of all characteristic transformation temperatures until a critical thickness is reached.Particularly,Ti_(40.4)Ni_(48)Hf_(11.6) thin films as low as 220 nm show transfor-mations above room temperature when deposited on SiO_(2) buffer layer,which is of great interest in nano-actuation.In comparison,220 nm films on Si substrates are austenitic at room temperature,and thus not suitable for actuation.Thermal fatigue tests on TiNiHf/SiO_(2)/Si bimorphs demonstrate better functional fatigue characteristics than freestanding films,with an average reduction of 15℃ after 125 cycles,with tempera-ture stabilization subsequently.Experimental bi-directional actuation results are promising in the development of bistable actuators within a PMMA/TiNiHf/Si trimorph composite,whereby the additional PMMA layer undergoes a glass transition at 105℃.With the aid of constitutive modeling,a route is elaborated on how bistable actuation can be achieved at micro-to nanoscales by showing favorable thickness combinations of PMMA/TiNiHf/Si composite.展开更多
基金supported by the National Natural Science Foundation of China(Nos.52201207 and 52271169)the Fundamental Research Funds for the Central University(No.3072024LJ1002).
文摘The active development of space industry necessitates the cre-ation of novel materials with unique properties,including shape memory alloys(SMAs).The development of ultra-high temperature SMAs(UHTSMAs)with operating temperatures above 400℃is a significant challenge[1-3].It is known that reversible thermoelas-tic martensitic transformation(MT)is the basis for shape mem-ory behavior[4].Currently,there are several systems in which MT temperatures meet the above requirements,for example,RuNb[5],HfPd[6],TiPd[7].
基金supported by the National Natural Sci-ence Foundation of China(Nos.52050410340 and 51971072)the Fundamental Research Funds for the Central University(No.3072021CFJ1002).
文摘Precipitate hardening is the most easiest and effective way to enhance strain recovery properties in NiTiHf high-temperature shape memory alloys.This paper discusses the precipitation,coarsening and age hardening of H-phase precipitates in Ni_(50)Ti_(30)Hf_(20)alloy during isothermal aging at temperatures between 450℃and 650℃for time to 75 h.The H-phase mean size and volume fraction were determined using transmission electron microscopy.Precipitation kinetics was analyzed using the Johnson-Mehl-Avrami-Kolmogorov equation and an Arrhenius type law.From these analyses,a Time-Temperature-Transformation diagram was constructed.The evolution of H-phase size suggests classical matrix diffusion limited Lifshitz-Slyozov-Wagner coarsening for all considered temperatures.The coarsening rate constants of H-phase precipitation have been determined using a modified coarsening rate equation for nondilute solutions.Critical size of H-phase precipitates for breaking down the precipitate/matrix interface coherency was estimated through a combination of age hardening and precipitate size evolution data.Moreover,time-temperature-hardness diagram was constructed from the precipitation and coarsening kinetics and age hardening of H-phase precipitates in Ni_(50)Ti_(30)Hf_(20)alloy.
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.
文摘The martensitic phase transformation in Ti_(40.4)Ni_(48)Hf_(11.6) shape memory alloys is leveraged for bi-directional actuation with TiNiHf/SiO_(2)/Si com-posites.The shape memory properties of magnetron sputtered Ti_(40.4)Ni_(48)Hf_(11.6) films annealed at 635℃-5 min are influenced by film thickness and the underlying substrate.Decreasing TiNiHf film thick-ness from 21μm to 110 nm results in the reduction of all characteristic transformation temperatures until a critical thickness is reached.Particularly,Ti_(40.4)Ni_(48)Hf_(11.6) thin films as low as 220 nm show transfor-mations above room temperature when deposited on SiO_(2) buffer layer,which is of great interest in nano-actuation.In comparison,220 nm films on Si substrates are austenitic at room temperature,and thus not suitable for actuation.Thermal fatigue tests on TiNiHf/SiO_(2)/Si bimorphs demonstrate better functional fatigue characteristics than freestanding films,with an average reduction of 15℃ after 125 cycles,with tempera-ture stabilization subsequently.Experimental bi-directional actuation results are promising in the development of bistable actuators within a PMMA/TiNiHf/Si trimorph composite,whereby the additional PMMA layer undergoes a glass transition at 105℃.With the aid of constitutive modeling,a route is elaborated on how bistable actuation can be achieved at micro-to nanoscales by showing favorable thickness combinations of PMMA/TiNiHf/Si composite.
