超临界机组用耐热钢的开发及相关基础研究
|
摘要
摘要：随着电力机组蒸汽参数的不断提高,高压锅炉管的服役环境发生显著的变化,同时也对高压锅炉管用耐热钢的服役性能与寿命提出了更高的要求,特别是对高温力学性能和抗氧化性能提出了很高的性能指标,传统的耐热钢已无法满足使用要求。本文开展超临界火电机组用耐热钢的成分设计、热变形、热处理工艺、高温性能及相关基础研究,为企业开发高性能耐热钢提供技术支撑。
根据超临界火电机组用耐热钢的目标性能,利用人工神经网络技术开展了成分设计,确定了实验钢的成分范围。对实验钢的热变形行为进行了全面的研究,基于多种不同模型构建了本构方程,对Johnson Cook模型进行了改进。研究了T24钢在连续冷却过程中的相变行为,测定了相应的CCT图,利用解析化方法绘制了CCT图,分别与相近成分和不同成分的CCT图进行了比较。研究了实验钢的热处理工艺,在此基础上确定了实验钢的服役温度范围。对耐热钢的显微组织进行了分析,计算了晶粒度,利用EBSD手段分析了实验钢在不同状态的晶粒和晶界的变化。研究了实验钢在高温条件的氧化行为,分析了氧化膜的生长和剥落的过程,并测定了实验钢的高温物理性能。主要结论如下：
1.利用BP人工神经网络模型确定T24钢的成分范围(Wt,%)为：0.07C,0.21Si,0.47Mn,0.09Cu,0.04Ni2.41Cr,1.03Mo,0.06Ti,0.24V,0.1Al,P<0.01,S<0.005。
2.基于modified Zerilli-Armstrong、strain-compensated Arrhenius等模型,构建了本构方程准确预测材料在实验范围内的流变应力。Johnson CookR原始模型预测的相关系数=0.962, AARE=9.41%。通过引入温度与应变速率耦合影响因子对Johnson Cook原始模型进行改进,改进后模型预测的相关系数R=0.991, AARE=5.37%。
3.测得T24实验钢的CCT图,Ac1为773℃,Ac3为963℃,当冷却速度小于0.1℃/s时,发生高温转变和中温转变,转变产物为铁素体、珠光体和贝氏体的混合组织,在0.5℃/s～10℃/s的冷却速度范围,转变产物为粒状贝氏体,当冷却速度大于20℃/s时,转变产物为马氏体。当冷却速度为0.03℃/s、0.05℃/s和0.1℃/s时,过冷奥氏体先在高温区发生组织转变,转变量分别为49%、25%和22%,之后在中温区发生组织转变。相近成分T23钢的CCT图及临界点与T24钢相似,Ac1和Ac3分别为777℃、963℃,成分有较大差别的T91钢的CCT图及临界点与T24相差较大,Ac1和Ac3分别为758℃、871℃。
4.T24实验钢较优的奥氏体化工艺为1000℃/30min,较优回火工艺为750℃/70min。实验钢经1000℃/30min奥氏体化在750℃回火70min后,室温抗拉强度为615MPa,屈服强度为564MPa,伸长率为22.3%,在570℃的高温抗拉强度为497MPa,屈服强度为447MPa,伸长率为10.9%,服役温度不能超过580℃。
5.T24钢热处理后组织为粒状贝氏体,且铁素体基体上分布有岛状颗粒,组织晶粒度级别数为5～6。T23钢的组织与T24钢相似。T9钢和T91钢的组织相似,主要为回火马氏体,基体分布有较多的析出物。T24钢在不同的状态下,组织中小角度晶界的比例较高,尤其以2°～3°晶界为主,大角度晶界以60°为主,且规律为随着取向差角的增加,小角度晶界比例迅速递减,当取向差角大于50°,大角度晶界比例显著增长,在60°附近达到峰值。
6.T24钢在570℃高温氧化时,0～120h内的氧化速率为6.675×10-5,120～1700h内的氧化速率为5.996×10-8，1700～2600h内的氧化速率为2.686×10-6,2600～6200h内的氧化速率为4.513×10-7。T24钢在600℃高温氧化时,0～2000h内的氧化速率为1.161×10-5,2000～10000h内的氧化速率为4.162×10-6。T23钢在600℃的高温氧化时,0～1500h内的氧化速率为1.174×10-5,1500～5000h内的氧化速率为5.985×10-6,5000～10000h内的氧化速率为1.759×10-8。T91钢在625℃高温氧化时,0～1000h内的氧化速率为2.313×10-6,1000～10000h内,氧化速率为3.171×10-11。T9钢在600℃高温氧化时,0～500h内的氧化速率为3.367×10-5,500～6000h内的氧化速率为2.752×10-12,6000～10000h内的氧化速率为1.978×10-7。
7.T24钢和T23钢的导热性能均高于T9钢和T91钢,在常温下,T9钢的导热系数为26.5W/m·K,略低于T91钢,当温度升高到800℃时,T91钢的导热系数为21.6W/m·K,比T9钢的高2.9W/m·K。在100～500℃的温度区间内,T23钢和T24钢的线膨胀系数大小差别不大,高于T9钢的线膨胀系数,T91钢的线膨胀系数最小。在温度低于500℃的情况下,T24钢的弹性模量最大,T91钢次之,T23钢的弹性模量最小。
Abstract:With the increase of steam parameters of generator sets in the power plants in recent years, service environment of high pressure boiler tube changed greatly correspondingly, which put forward higher requirements for service performance and life of heat-resistant steel. Because traditional heat-resistant steels can not satisfy these requirements, it is necessary to develop new steels with excellent service performance, especially for high temperature mechanical properties and oxidation resistance. In this paper, chemical composition design, hot deformation behavior, heat treatment process, high temperature properties and related basic research of the heat-resistant steel were investigated systematically. These studies could provide the steel enterprises with technical support for developing and manufacturing high pressure boiler tubes.
According to the target performance of heat-resistant steels used for supercritical units, the composition of experimental steel was designed by BP artificial neural network. The comprehensive and systematic investigation on hot deformation behavior of experimental steel was carried out, and the constitutive equations were established based on several models. Hot compression test data was used to modify the Johnson Cook model. The phase transformation behavior of T24steel during continuous cooling process was studied systematically and the CCT diagram was obtained based on the study. The obtained CCT diagram was compared with other heat-resistant steels'. The CCT diagram of T24steel was also obtained by mathematical analysis method. Heat treatment process of the experimental steel was studied systematically and the service temperature range was determined based on this investigation. Microstructure of the steels was analyzed, and grain size was also evaluated using three methods respectively. Variations of grains and grain boundaries of the experimental steel in different states were investigated by means of EBSD. High temperature oxidation behavior of the steel was studied. In addition, growth process and exfoliation of oxidation layer were also analyzed. High temperature physical properties of the experimental steels were determined and analyzed. The main conclusions can be drawn as follows:
1. By utilizing artificial neural network, the chemical composition of T24steel could be designed as (in wt.%):0.07C,0.21Si,0.47Mn,0.09Cu,0.04Ni,2.41Cr,1.03Mo,0.06Ti,0.24V,0.1A1, P<0.01, S<0.005.
2. Constitutive equations was established based on the modified Zerilli-Armstrong, strain-compensated Arrhenius and ANN models, and the flow stress could be predicted accurately. The original Johnson Cook model was modified by introducing a variable factor which considers the couple effect of temperature and strain rate. The correlation coefficient(R) and average absolute relative error(AARE) for modified model is0.991and5.37%respectively, which is higher than the values0.962and9.41%of the original model.
3. The CCT diagram of T24steel was obtained and the critical temperatures of AC1and AC3were determined as773℃and963℃respectively by DSC. When the cooling rates are below0.1℃/s, the transformation product consists of ferrite, pearlite and bainite. When the cooling rates are in the range of0.5～10℃/s, transformation product is mainly composed of granular bainite. When the cooling rates is above20℃/s, martensite is the main transformation product. High temperature transformation occurs at first when the super-cooled austenite is cooled at the rates of0.03℃/s,0.05℃/s and0.1℃/s, and then middle temperature microstructure transformation begins to take place. The percentages of transformation product in high temperature ranges are49%,25%and22%correspondingly. Difference in compositon between T23steel and T24steel is small and therefore the CCT diagrams of the two steels are similar. The critical temperatures Ac1and AC3of T23steel are777℃and963℃respectively. There are considerable differences in CCT diagram and critical temperatures for T91steel T24steel due to the big difference in composition and the AC1and AC3of T91steel are758℃and871℃respectively.
4. Based on the experiment and analysis, the optimized austenitizing process and tempering technology can be determined as1000℃/30min and750℃/70min respectively. When the experimental steel is heat treated applying this process, room temperature tensile strength, yield strength and elongation of the steel are615MPa,564MPa and22.3%respectively. High temperature(570℃) tensile strength, yield strength and elongation are497MPa,447MPa and10.9%using the same tempering process。The service temperature of T24steel should be controlled below580℃.
5. The main microstructure of the heat-treated T24and T23steel is granular bainite with some island-shaped particles on it, and the grain size grade of T24steel are evaluated as5-6. The main microstructure of the heat-treated T9and T91steel is tempered martensite with some precipitates in matrix. Low-angle boundaries always account for a large proportion of the total in the microstructure of T24steel in many states, especially for the boundaries with angles of2°～3°. Boundaries with angles of60°are the main wide-angle boundaries. With the increase of misorientation angle, the proportion of low-angle boundary decrease sharply. When the misorientation angle is higher than50°, the boundaries with angles of60°account for maximum propotion.
6. When T24steel is heated isothermally in the air at570℃, the oxidation rate is6.675×10-5within120h, and5.996×10-8in the time range of120-1700h. Oxidation rate are2.686×10-6and4.513×10-7respectively for the time range of1700-2600h and2600-6200h. When the heating temperature increases to600℃, the oxidation rates are1.161×10-5within2000h and1.161×10-5in the time range from2000h to10000h. Under the same heating conditions(600℃),the oxidation rates of T23steel are1.174×10-5,5.985×10-6and1.759×10-8corresponding to the time ranges of0-1500h,1500-5000h and5000-10000h respectively. As for T9steel, the oxidation rates are determined as3.367×10-6,2.752×10-12and1.978×10-1rcorresponding to the time range0～500,500-6000h, and6000-10000h respectively. When T91steel is heated in the air at625℃, the oxidation rate are2.313×10-6and3.171×10-11respectively corresponding to the time ranges of0-1000h and1000-10000h.
7. Heat-conducting property of T23andT24steel is higher than that of T9and T91steel. Heat conductivity coefficient of T9steel is26.5W/m-K at room temperature, which is a bit lower than that of T91steel. Heat conductivity coefficient of T91steel is21.6W/m-K at800℃,2.9 W/m-K higher than that of T9steel. The difference in linear expansion coefficient between T23andT24steel is very small in the temperature of100～500℃, and the values are higher than that of T91and T9steel. The minimum value of inear expansion coefficient is gained from T91steel. Elasticity modulus of T24steel is higher than that of other steels, and the minimum value of it can be obtained from T23steel.
根据超临界火电机组用耐热钢的目标性能,利用人工神经网络技术开展了成分设计,确定了实验钢的成分范围。对实验钢的热变形行为进行了全面的研究,基于多种不同模型构建了本构方程,对Johnson Cook模型进行了改进。研究了T24钢在连续冷却过程中的相变行为,测定了相应的CCT图,利用解析化方法绘制了CCT图,分别与相近成分和不同成分的CCT图进行了比较。研究了实验钢的热处理工艺,在此基础上确定了实验钢的服役温度范围。对耐热钢的显微组织进行了分析,计算了晶粒度,利用EBSD手段分析了实验钢在不同状态的晶粒和晶界的变化。研究了实验钢在高温条件的氧化行为,分析了氧化膜的生长和剥落的过程,并测定了实验钢的高温物理性能。主要结论如下：
1.利用BP人工神经网络模型确定T24钢的成分范围(Wt,%)为：0.07C,0.21Si,0.47Mn,0.09Cu,0.04Ni2.41Cr,1.03Mo,0.06Ti,0.24V,0.1Al,P<0.01,S<0.005。
2.基于modified Zerilli-Armstrong、strain-compensated Arrhenius等模型,构建了本构方程准确预测材料在实验范围内的流变应力。Johnson CookR原始模型预测的相关系数=0.962, AARE=9.41%。通过引入温度与应变速率耦合影响因子对Johnson Cook原始模型进行改进,改进后模型预测的相关系数R=0.991, AARE=5.37%。
3.测得T24实验钢的CCT图,Ac1为773℃,Ac3为963℃,当冷却速度小于0.1℃/s时,发生高温转变和中温转变,转变产物为铁素体、珠光体和贝氏体的混合组织,在0.5℃/s～10℃/s的冷却速度范围,转变产物为粒状贝氏体,当冷却速度大于20℃/s时,转变产物为马氏体。当冷却速度为0.03℃/s、0.05℃/s和0.1℃/s时,过冷奥氏体先在高温区发生组织转变,转变量分别为49%、25%和22%,之后在中温区发生组织转变。相近成分T23钢的CCT图及临界点与T24钢相似,Ac1和Ac3分别为777℃、963℃,成分有较大差别的T91钢的CCT图及临界点与T24相差较大,Ac1和Ac3分别为758℃、871℃。
4.T24实验钢较优的奥氏体化工艺为1000℃/30min,较优回火工艺为750℃/70min。实验钢经1000℃/30min奥氏体化在750℃回火70min后,室温抗拉强度为615MPa,屈服强度为564MPa,伸长率为22.3%,在570℃的高温抗拉强度为497MPa,屈服强度为447MPa,伸长率为10.9%,服役温度不能超过580℃。
5.T24钢热处理后组织为粒状贝氏体,且铁素体基体上分布有岛状颗粒,组织晶粒度级别数为5～6。T23钢的组织与T24钢相似。T9钢和T91钢的组织相似,主要为回火马氏体,基体分布有较多的析出物。T24钢在不同的状态下,组织中小角度晶界的比例较高,尤其以2°～3°晶界为主,大角度晶界以60°为主,且规律为随着取向差角的增加,小角度晶界比例迅速递减,当取向差角大于50°,大角度晶界比例显著增长,在60°附近达到峰值。
6.T24钢在570℃高温氧化时,0～120h内的氧化速率为6.675×10-5,120～1700h内的氧化速率为5.996×10-8，1700～2600h内的氧化速率为2.686×10-6,2600～6200h内的氧化速率为4.513×10-7。T24钢在600℃高温氧化时,0～2000h内的氧化速率为1.161×10-5,2000～10000h内的氧化速率为4.162×10-6。T23钢在600℃的高温氧化时,0～1500h内的氧化速率为1.174×10-5,1500～5000h内的氧化速率为5.985×10-6,5000～10000h内的氧化速率为1.759×10-8。T91钢在625℃高温氧化时,0～1000h内的氧化速率为2.313×10-6,1000～10000h内,氧化速率为3.171×10-11。T9钢在600℃高温氧化时,0～500h内的氧化速率为3.367×10-5,500～6000h内的氧化速率为2.752×10-12,6000～10000h内的氧化速率为1.978×10-7。
7.T24钢和T23钢的导热性能均高于T9钢和T91钢,在常温下,T9钢的导热系数为26.5W/m·K,略低于T91钢,当温度升高到800℃时,T91钢的导热系数为21.6W/m·K,比T9钢的高2.9W/m·K。在100～500℃的温度区间内,T23钢和T24钢的线膨胀系数大小差别不大,高于T9钢的线膨胀系数,T91钢的线膨胀系数最小。在温度低于500℃的情况下,T24钢的弹性模量最大,T91钢次之,T23钢的弹性模量最小。
Abstract:With the increase of steam parameters of generator sets in the power plants in recent years, service environment of high pressure boiler tube changed greatly correspondingly, which put forward higher requirements for service performance and life of heat-resistant steel. Because traditional heat-resistant steels can not satisfy these requirements, it is necessary to develop new steels with excellent service performance, especially for high temperature mechanical properties and oxidation resistance. In this paper, chemical composition design, hot deformation behavior, heat treatment process, high temperature properties and related basic research of the heat-resistant steel were investigated systematically. These studies could provide the steel enterprises with technical support for developing and manufacturing high pressure boiler tubes.
According to the target performance of heat-resistant steels used for supercritical units, the composition of experimental steel was designed by BP artificial neural network. The comprehensive and systematic investigation on hot deformation behavior of experimental steel was carried out, and the constitutive equations were established based on several models. Hot compression test data was used to modify the Johnson Cook model. The phase transformation behavior of T24steel during continuous cooling process was studied systematically and the CCT diagram was obtained based on the study. The obtained CCT diagram was compared with other heat-resistant steels'. The CCT diagram of T24steel was also obtained by mathematical analysis method. Heat treatment process of the experimental steel was studied systematically and the service temperature range was determined based on this investigation. Microstructure of the steels was analyzed, and grain size was also evaluated using three methods respectively. Variations of grains and grain boundaries of the experimental steel in different states were investigated by means of EBSD. High temperature oxidation behavior of the steel was studied. In addition, growth process and exfoliation of oxidation layer were also analyzed. High temperature physical properties of the experimental steels were determined and analyzed. The main conclusions can be drawn as follows:
1. By utilizing artificial neural network, the chemical composition of T24steel could be designed as (in wt.%):0.07C,0.21Si,0.47Mn,0.09Cu,0.04Ni,2.41Cr,1.03Mo,0.06Ti,0.24V,0.1A1, P<0.01, S<0.005.
2. Constitutive equations was established based on the modified Zerilli-Armstrong, strain-compensated Arrhenius and ANN models, and the flow stress could be predicted accurately. The original Johnson Cook model was modified by introducing a variable factor which considers the couple effect of temperature and strain rate. The correlation coefficient(R) and average absolute relative error(AARE) for modified model is0.991and5.37%respectively, which is higher than the values0.962and9.41%of the original model.
3. The CCT diagram of T24steel was obtained and the critical temperatures of AC1and AC3were determined as773℃and963℃respectively by DSC. When the cooling rates are below0.1℃/s, the transformation product consists of ferrite, pearlite and bainite. When the cooling rates are in the range of0.5～10℃/s, transformation product is mainly composed of granular bainite. When the cooling rates is above20℃/s, martensite is the main transformation product. High temperature transformation occurs at first when the super-cooled austenite is cooled at the rates of0.03℃/s,0.05℃/s and0.1℃/s, and then middle temperature microstructure transformation begins to take place. The percentages of transformation product in high temperature ranges are49%,25%and22%correspondingly. Difference in compositon between T23steel and T24steel is small and therefore the CCT diagrams of the two steels are similar. The critical temperatures Ac1and AC3of T23steel are777℃and963℃respectively. There are considerable differences in CCT diagram and critical temperatures for T91steel T24steel due to the big difference in composition and the AC1and AC3of T91steel are758℃and871℃respectively.
4. Based on the experiment and analysis, the optimized austenitizing process and tempering technology can be determined as1000℃/30min and750℃/70min respectively. When the experimental steel is heat treated applying this process, room temperature tensile strength, yield strength and elongation of the steel are615MPa,564MPa and22.3%respectively. High temperature(570℃) tensile strength, yield strength and elongation are497MPa,447MPa and10.9%using the same tempering process。The service temperature of T24steel should be controlled below580℃.
5. The main microstructure of the heat-treated T24and T23steel is granular bainite with some island-shaped particles on it, and the grain size grade of T24steel are evaluated as5-6. The main microstructure of the heat-treated T9and T91steel is tempered martensite with some precipitates in matrix. Low-angle boundaries always account for a large proportion of the total in the microstructure of T24steel in many states, especially for the boundaries with angles of2°～3°. Boundaries with angles of60°are the main wide-angle boundaries. With the increase of misorientation angle, the proportion of low-angle boundary decrease sharply. When the misorientation angle is higher than50°, the boundaries with angles of60°account for maximum propotion.
6. When T24steel is heated isothermally in the air at570℃, the oxidation rate is6.675×10-5within120h, and5.996×10-8in the time range of120-1700h. Oxidation rate are2.686×10-6and4.513×10-7respectively for the time range of1700-2600h and2600-6200h. When the heating temperature increases to600℃, the oxidation rates are1.161×10-5within2000h and1.161×10-5in the time range from2000h to10000h. Under the same heating conditions(600℃),the oxidation rates of T23steel are1.174×10-5,5.985×10-6and1.759×10-8corresponding to the time ranges of0-1500h,1500-5000h and5000-10000h respectively. As for T9steel, the oxidation rates are determined as3.367×10-6,2.752×10-12and1.978×10-1rcorresponding to the time range0～500,500-6000h, and6000-10000h respectively. When T91steel is heated in the air at625℃, the oxidation rate are2.313×10-6and3.171×10-11respectively corresponding to the time ranges of0-1000h and1000-10000h.
7. Heat-conducting property of T23andT24steel is higher than that of T9and T91steel. Heat conductivity coefficient of T9steel is26.5W/m-K at room temperature, which is a bit lower than that of T91steel. Heat conductivity coefficient of T91steel is21.6W/m-K at800℃,2.9 W/m-K higher than that of T9steel. The difference in linear expansion coefficient between T23andT24steel is very small in the temperature of100～500℃, and the values are higher than that of T91and T9steel. The minimum value of inear expansion coefficient is gained from T91steel. Elasticity modulus of T24steel is higher than that of other steels, and the minimum value of it can be obtained from T23steel.
引文
[1]林富生,王治政,王宝忠等.中国电站用耐热钢及合金的研制、应用与发展[J].动力工程学报,2010,30(4)：235-244.
[2]艾国安,谷力功,曹俊等.高压锅炉管生产现状[J].高压锅炉管生产现状,2006,1：18-22.
[3]王艳华.超超临界汽轮机耐热钢组织和性能研究[D].哈尔滨：哈尔滨工程大学,2006.
[4]宁保群,刘永长,殷红旗.超高临界压发电厂锅炉管用铁素体耐热钢的发展现状与研究前景[J].材料导报,2006,20(12)：83-86.
[5]干勇.关于研发超(超)临界火电机组高级锅炉管的建议[J].中国冶金,2006,15(2)：1-4.
[6]杨富,李为民,任永宁.超临界、超超临界锅炉用钢[J].电力设备,2004,5(10)：41-46.
[7]Zinkle J S. Advanced materials for fusion technology[J]. Fusion Engineering and Design,2005,74(1-4):31-40.
[8]王兴,唐桦,傅佳隽.1000MW超超临界机组主蒸汽与再热汽温度的调整方法[J].上海电力,2009,6：440-442.
[9]季献武,段鹏,李驹等.T23钢在超超临界1000MW机组的应用及现状[J].华东电力,2009,37(12)：2097-2100.
[10]Moitra A, Parameswaran P, Sreenivasan P R, et al. A toughness study of the weld heat-affected zone of a 9Cr-1Mo steel[J]. Materials Characterization, 2002,,48(1):55-56.
[11]Abe FB. Bainitic and martensitic creep-resistant steels[J]. Current Opinion in Solid State and Materials Science,8(3-4):305-311.
[12]Hirose T, Shiba K, Ando M, et al. Joining technologies of reduced activation ferritic/martensitic steel for blanket fabrication[J]. Fusion Engineering and Design,2006,81(1-7):645-651.
[13]Zeman A, Debarberis L, Kocik J, et al. Microstructural analysis of candidate steels pre-selected for new advanced reactor systems[J]. Journal of Nuclear Materials,2007,362(2-3):259-267.
[14]Cho H S, Ohkubo H, Iwata N, et al. Improvement of compatibility of advanced ferritic steels with super critical pressurized water toward a higher thermally efficient water-cooled blanket system[J]. Fusion Engineering and Design,2006, 81(8-14):1071-1076.
[15]Stefano C. Carbon dioxide separation from high temperature fuel cell power plants[J]. Journal of power sources,2002,112(1):273-289.
[16]李兵.超临界压力锅炉过热蒸汽、再热蒸汽气温变工况特性研究[D].济南：山东大学,2008.
[17]张俊峰,王黎明,仲学兰.超超临界火电机组主蒸汽、再热蒸汽管道选材分析[J].华中电力,2010,23(4)：49-52.
[18]罗家华.热处理工艺对12Cr2Mo WVTiB(102钢)组织和性能的影响[D].长沙：中南大学,2006.
[19]孟繁茂,付俊岩.现代含铌不锈钢[M].北京：冶金工业出版社,2004.
[20]任文超.E91 1钢组织与性能的研究[D].哈尔滨：哈尔滨工程大学,2007.
[21]张业圣.我国火力发电用高压锅炉管现状与需求分析[J].钢管,2008,37(5)：1-10.
[22]江永静,成海涛.我国无缝钢管生产现状、差距及发展建议[J].钢管,2004,33(3)：1-6.
[23]杨秀琴.我国钢管工业的现状、问题与发展前景(下)[J].钢管,2008,37(2)：14.
[24]成海涛，郭元蓉.我国电站锅炉用管的现状与发展对策[J].钢管,2006,35(2)：5-9.
[25]廖建国.日本超超临界压力发电用高Cr铁素体耐热钢的开发及现状[J].世界金属导报,2007,010版：16.
[26]徐强,张幸红,韩杰才等.人工神经网络在材料科学中的应用与展望[J].材料科学与工艺,2005,13(4)：352-356.
[27]樊新民,孔见,金波.人工神经网络在材料科学研究中的应用[J].材料导报,2002,16(4)：28-30.
[28]李军,阮晓钢.一种基于神经网络的专家系统设计[J].北京工业大学学报,2003,29(2)：171-174.
[29]赖静,王清,孙东立.人工神经网络在材料研究中的应用[J].材料工程,2006,(1)：458-462.
[30]王海芳.神经网络专家系统的人机界面设计及其在材料设计中的应用[D]天津：河北工业大学,2006.
[31]董敏.人工神经网络与材料工艺的优化[J].辽宁师专学报,2000,2(2)： 100-103.
[32]从爽.神经网络、模糊系统及其在运动控制中的应用[M].合肥：中国科学技术大学出版社,2001.
[33]俞树荣,张俊武,李建华.基于模糊神经网络的材料力学性能识别[J].甘肃工业大学学报,2001,27(1)：8-12.
[34]郭鹏.基于神经网络的高温合金组织性能分析[D].西安：西北工业大学,2004.
[35]袁曾任.人工神经网络及其应用[M].北京：清华大学出版社,1999.
[36]左秀荣.人工神经网络在钢铁材料力学性能预测方面的应用[J].特殊钢,2004,25(5)：26-29.
[37]曾卫东,舒滢,周义刚.应用人工神经网络模型预测Ti-10V-2Fe-3A1合金的力学性能[J].稀有金属材料与工程,2004,33(10)：1041-1044.
[38]董万好,钱初钧,张戈.应用BP神经网络预测钢的回火温度及回火硬度[J].实验室研究与探索,2003,22(6)：50-52.
[39]吴良,钟文锋.基于人工神经网络的结构钢端淬曲线预测系统模型的研究[J].金属热处理学报,2000,(12)：13-17.
[40]任世和,吴良.基于人工神经网络的结构钢回火后力学性能预测[J].金属热处理,2002,27(12)：27-29.
[41]Lin Y C, Fang X, Wang Y P. Prediction of metadynamic softening in a multi-pass hot deformed low alloy steel using artificial neural network[J]. Journal of Materials Science,2008,43:5508-5515.
[42]阳范文,赵耀明.材料性能预测和配方优化的人工神经网络研究方法[J].合成材料老化与应用,2001,(1)：15-19.
[43]刘贵立,张国英,曾梅光.用人工神经网络模型研究微量元素对钢力学性能的影响[J].钢铁研究,2000, (1)：48-50.
[44]蔡安辉,孙国雄.基于人工神经网络的材料力学性能的预测与评估[J].铸造,2003,52(4)：269-271.
[45]左秀荣,薛向欣,姜茂发等.利用人工神经网络研究合金元素对SCM822H钢性能的影响[J].钢铁研究学报,2002,14(1)：51-54.
[46]黄俊.基于人工神经网络的焊接材料设计[D].南京：南京理工大学,2002.
[47]Li H Y, Wei D D, Li Y H, et al. Application of artificial neural network and constitutive equations to describe the hot compressive behavior of 28CrMnMoV steel[J]. Materials and Design,2012, (35):557-562.
[48]Lin Y C, Chen X M. A critical review of experimental results and constitutive descriptions for metals and alloys in hot working[J]. Materials and Design, 2011,32:1733-1759.
[49]Sun Y, Zeng W D, Zhao Y Q et al. Modeling constitutiverelationship of Ti40 alloy using artificial neural network[J]. Materials and Design,2011,32: 1537-1541.
[50]Sun Y, Zeng W D, Zhao Y Q, et al. Development of constitutive relationship model of Ti600 alloy using artificial neural network[J]. Computational Materials Science,2010,48:686-691.
[51]吴良.人工神经网络(ANN)智能技术与热处理[J].纺织机械,2001,(4)：36-38.
[52]Reddy N S, Lee Y H, Park C H, et al. Prediction of flow stress in Ti-6A1-4V alloy with an equiaxed [alpha]+[beta] microstructure by artificial neural networks[J]. Materials Science and Engineering:A,2008,492:276-282.
[53]Lin Y C, Zhang J, Zhong J. Application of neural networks to predict the elevated temperature flow behavior of a low alloy steel[J]. Computational Materials Science,2008,43:752-758.
[54]Li H Y, Hu J D, Wei D D et al. Artificial neural network and constitutive equations to predict the hot deformation behavior of modified 2.25Cr-1Mo steel[J]. Materials and Design,2012,42:192-197.
[55]徐强,张幸红,韩杰才等.人工神经网络在材料科学中的应用与展望[J].材料科学与工艺,2005,13(4)：352-356.
[56]田永君,袁秀信,高慧菊等.热变形中的动态回复过程[J].材料科学与工艺,1987,6(2)：68-74.
[57]Tang Z, Stumpf W. The role of molybdenum additions and prior deformation on acicular ferrite formation in microalloyed Nb-Ti low-carbon line-pipe steels[J]. Materials Characterization,2008,59(6):717-728.
[58]胡安民,孙祖庆.低碳钢多道次热变形中的应变强化相变与铁素体动态再结晶[J].金属学报,2000,36(11)：1192-1196.
[59]Johnson GR, Cook WH. A constitutive model and data for metals subjected to large strains, high strain rates and high temperatures [C]. In:Proceedings of the 7th International Symposium on Ballistics. Den Haag, The Netherlands,1983, p 541-543.
[60]Khan A S, Zhang H Y, Takacs L. Mechanical response and modeling of fully compacted nanocrystalline iron and copper[J]. Int J Plast,2000,16:1459- 1476.
[61]Khan A S, Suh Y S, Kazmi R. Quasi-static and dynamic loading responses and constitutive modeling of titanium alloys[J]. Int J Plast,2004,20:2233-2248.
[62]Farrokh B, Khan A S. Grain size, strain rate, and temperature dependence of flow stress in ultra-fine grained and nanocrystalline Cu and Al:synthesis, experiment, and constitutive modeling[J]. Int J Plast,2009,25:715-732.
[63]Khan A S, Suh Y S, Chen X, et al. Nanocrystalline aluminum and iron: mechanical behavior at quasi-static and high strain rates, and constitutive modeling[J]. Int J Plast,2006,22:195-209.
[64]Molinari A, Ravichandran G. Constitutive modeling of high-strain-rate deformation in metals based on the evolution of an effective microstructural length[J]. Mech Mater,2005,37:737-752..
[65]Sellars C M. On the mechanism of hot deformation[J]. Acta Met,1966,14: 1136-1138.
[66]Kocks U F. Laws for work-hardening and low-temperature creep[J]. J Eng Mater Technol,1976,98:76-85.
[67]Voce E. The relationship between stress and strain for homogeneous deformation[J]. J Inst Met,1948,74:537-62.
[68]Lin Y C, Liu G. A new mathematical model for predicting flow stress of typical high-strength alloy steel at elevated high temperature [J]. Comput Mater Sci, 2010,48:54-58.
[69]Zener C, Hollomon H. Effect of strain rate upon plastic flow of steel[J]. J Appl Phys,1944,15:22-32.
[70]Jonas J, Sellars CM. Tegart WJMcG. Strength and structure under hot-working conditions[J]. Int Metal Rev,1969,14:1-24.
[71]Shi H, McLaren A J, Sellars CM, et al. Constitutive equations for high temperature flow stress of aluminium alloys[J]. Mater Sci Technol,1997,13: 210-6.
[72]Khan A S, Huang S. Experimental and theoretical study of mechanical behavior of 1100 aluminum in the strain rate range 10-5-104 s-1[J]. Int J Plast,1992,8: 397-424.
[73]Preston D L, Tonks D L, Wallace D C. Model of plastic deformation for extreme loading conditions [J]. J Appl Phys,2003,93:211-220.
[74]Tsuchida N, Tomota Y, Moriya H, et al. Application of the Kocks-Mecking model to tensile deformation of an austenitic 25Cr-19Ni steel[J]. Acta Mater, 2001,49:3029-3038.
[75]Voyiadjis G Z, Almasri A H. A physically based constitutive model for fcc metals with applications to dynamic hardness[J]. Mech Mater,2008,40: 549-563.
[76]Bodner SR, Partom Y. Constitutive equations for elastic-viscoplastic strain hardening materials[J]. J Appl Mech,1975,42:385-389.
[77]Goetz RL, Seetharaman V. Modeling dynamic recrystallization using cellular automaton[J]. Scripta Mater,1998,38:405-413.
[78]Cai M C, Niu L S, Ma X F, et al. A constitutive description of the strain rate and temperature effects on the mechanical behavior of materials [J]. Mech Mater,2010,42:774-781.
[79]Rusinek A, Klepaczko J R. Shear testing of a sheet steel at wide range of strain rates and a constitutive relation with strain-rate and temperature dependence of the flow stress[J]. Int J Plast,2001,17:87-115.
[80]Mecking H, Kocks U F. Kinetics of flow and strain-hardening[J]. Acta Metall, 1981,29:1865-1875.
[81]Vural M, Rittel D, Ravichandran G. Large strain mechanical behavior of 1018 cold-rolled steel over a wide range of strain rates [J]. Metallur Mater Trans A, 2003,34A:2873-2885.
[82]Nemat-Nasser S. Flow stress of FCC polycrystals with application to OFHC Cu[J]. Acta Mater,1998,46:565-577.
[83]Park JH, Tomota Y, Takagi S, et al. Estimation of flow stress by the Kocks-Mecking model at strain rate of 103/s for an ultra low carbon steel [J]. Tetsuto Hagane,2001,87:657-664.
[84]Mirzadeh H, Najafizadeh A. Flow stress prediction at hot working conditions[J].Mater Sci Eng A,2010,527:1160-1164.
[85]Zhang H J, Wen W D, Cui H T, et al. A modified Zerilli-Armstrong model for alloy IC10 over a wide range of temperatures and strain rates[J]. Mater Sci Eng A,2009,526:1-6.
[86]Lin Y C, Zhang J, Zhong J. Application of neural networks to predict the elevated temperature flow behavior of a low alloy steel[J]. Comput Mater Sci, 2008,43:752-758.
[87]Rusinek A, Rodriguez-Martinez J A, Arias A. A thermo-viscoplastic constitutive model for FCC metals with application to OFHC Copper[J]. Int J Mech Sci,2010,52:120-135.
[88]Lin Y C, Chen X M. A critical review of experimental results and constitutive descriptions for metals and alloys in hot working[J]. Materials and Design, 2011,32:1733-1759.
[89]Li H Y, Wang X F, Duan J Y, et al. A modified Johnson Cook model for elevated temperature flow behavior of T24 steel[J]. Mater Sci Eng A,2013,577: 138-146.
[90]Zhang H J, Wen W D, Cui H T. Behaviors of IC10 alloy over a wide range of strain rates and temperatures:experiments and modeling steel[J].. Mater Sci Eng A,2009,504:99-103.
[91]文九巴.材料科学与工程[M].哈尔滨：哈尔滨工业大学出版社,2007.
[92]赵乃勤.合金固态相变[M].长沙：中南大学出版社,2008.
[93]李松瑞,周善初.金属热处理[M].长沙：中南大学出版社,2003.
[94]Rometsch P A, Starink M J, Gregson P J. Improvements in quench factor modelling[J]. Materials Science and Engineering A,2003,339(1):255-264.
[95]Liu S D, Zhang X M, Huang Z B, et al. Prediction of hardness of aluminum alloy 7055 by quench factor analysis [J].Materials science forum,2007, 546:881-884.
[96]曹国洋.3Cr-1Mo-0.25V钢动态CCT曲线测定及形变与相变研究[D].秦皇岛：燕山大学,2009.
[97]李红英,耿进锋,李慎金.SWRCH22A钢过冷奥氏体的连续冷却转变,金属热处理,2005,30(9)：17-19.
[98]唐志勇.27MnCrB5钢的奥氏体连续冷却转变研究[J].热加工工艺,2007,6(20)：41-43.
[99]金自力.Gleeble 1500测定45钢CCT曲线的结果分析[J].包头钢铁学院学报,2004,23(4)：326-328.
[100]李红英,肖纳敏,谢先娇.H08Mn2S-钢等温转变曲线和连续冷却曲线[J].中南工业大学学报,2002,33(5)：495-499.
[101]胡怡.GQ112A钢CCT图的测定[J].金属热处理,2001,(7)：16-19.
[102]张黄强.合金钢CCT曲线研究[D].武汉：武汉科技大学,2008.
[103]乔志霞.连续冷却30CrNi3MoV超高强钢固态相变行为[D].天津：天津大学,2010.
[104]张希望.合金钢Q345的CCT曲线及断裂韧性研究[D].长沙：中南大学, 2008.
[105]王凤香,简方.27SiMn钢CCT曲线的测定[J]. 包钢科技,2007,33(2)：26-29.
[106]李红英,耿进锋,龚美涛.65Mn钢奥氏体连续冷却转变曲线(CCT图)[J].材料科学与工艺,2005,13(3)：302-304.
[107]吴化,刘姗姗.差示扫描量热法测定22Mn2SiVBS钢CCT曲线[J].理化检验-物理分册,2008,44(12)：684-688.
[108]商艳,张伟强.X70管线钢的CCT曲线研究[J].沈阳理工大学学报,2012,31(2)：84-86.
[109]赵宝纯, 李桂艳,柳永浩等.含Mo低碳微合金钢CCT曲线的测定与分析[J].热加工工艺,2008,37(22)：37-39.
[110]左碧强,王岩,米振莉等.管线钢X80的CCT曲线研究[J].热加工工艺,2010,39(4)：12-14.
[111]Lin Y C, Chen M S, Zhang J. Constitutive modeling for elevated temperature flow behavior of 42CrMo steel[J]. Comput Mater Sci,2008,424:470-477.
[112]Lin Y C, Chen M S, Zhong J. Study of metadynamic recrystallization behaviors in a low alloy steel[J]. J Mater Process Technol,2009,209:2477-2482.
[113]Lin Y C, Chen M S, Zhong J. Effects of deformation temperatures on stress/strain distribution and microstructure evolution of deformed 42CrMo steel[J]. Mater Des,2009,30:908-913.
[114]Lin Y C, Chen M S. Study of microstructural evolution during static recrystallization in a low alloy steel[J]. J Mater Sci,2009,44:835-842.
[115]Samantaray D, Mandal S, Bhaduri A K, et al. Analysis and mathematical modelling of elevated temperature flow behaviour of austenitic stainless steels[J]. Materials Science and Engineering:A,2011,528(4):1937-1943.
[116]Samantaray D, Mandal S, Borah U, et al. A thermo-viscoplastic constitutive model to predict elevated-temperature flow behaviour in a titanium-modified austenitic stainless steel[J]. Materials Science and Engineering:A,2009, 526(1):1-6.
[117]Phaniraj M P, Lahiri A K. The applicability of neural network model to predict flow stress for carbon steels[J]. Journal of Materials Processing Technology, 2003,141(2):219-227.
[118]徐光.金属材料CCT曲线测定及绘制[M].北京：化学工业出版社,2009.
[119]郭青苗,任吉堂.钢的过冷奥氏体连续转变(CCT)曲线的解析化[J].河北 理工学院学报,2006,28(2)：33-35
[120]Bergeon N, Kajiwara S, Kikuchi T. Atomic force microscope study of stress-induced martensite formation and its reverse transformation in a thermomechanically treated Fe-Mn-Si-Cr-Ni alloy[J]. Acta materialia,2000, 48(16):4053-4064.
[121]王泾文.淬火温度对20CrMnTi钢组织和性能的影响[J].热加工工艺,2005(1)：50-53.
[122]李安铭,王海瑞.亚温淬火对25MnV钢显微组织和力学性能的影响[J].机械工程材料,2009(12)：36-38.
[123]Wang X D, Xu W Z, Guo Z H, et al. Carbide characterization in a Nb-microalloyed advanced ultrahigh strength steel after quenching partitioning-tempering process[J]. Materials Science and Engineering:A, 2010,527(15):3373-3378.
[124]Lee Y K, Shin H C, Jang Y C, et al. Effect of isothermal transformation temperature on amount of retained austenite and its thermal stability in a bainitic Fe-3% Si-0.45% C-X steel[J]. Scripta materialia,2002,47(12):805-809.
[125]Jacques P J, Delannay F, Ladriere J. On the influence of interactions between phases on the mechanical stability of retained austenite in transformation-induced plasticity multiphase steels [J]. Metallurgical and Materials Transactions A,2001,32(11):2759-2768.
[126]GB/T228.1-2010,金属材料室温拉伸试验方法[S].北京：中国标准出版社,2010.
[127]GB/T4338-2006,金属材料高温拉伸试验方法[S].北京：中国标准出版社,2006.
[128]Benjamin F, Maxime S, Alexandra R, et al. Microstructural evolutions and cyclic softening of 9% Cr martensitic steels[J]. Journal of Nuclear Materials, 2009,386:71-74.
[129]GB/T13299-91,钢的显微组织评定方法[S].北京：中国标准出版社,1991.
[130]上官晓峰,王正品,耿波等.T91钢高温空气氧化动力学及层剥落机理[J].铸造技术,2005,26(7)：578-580.
[131]李辛庚,齐慧滨,王学刚等.火电厂锅炉再热器管高温腐蚀研究[J].材料保护,2003,6(36)：9-11.
[132]林俊滨.超超临界锅炉高温管内氧化皮形成机理及堵塞规律研究[D].广州：华南理工大学,2010.
[133]GB/T 13303-91,钢的抗氧化性能测定方法[S].北京：中国标准出版社,1991.
[134]GB/T 11108-1989,硬质合金热扩散率的测定方法[S].北京：中国标准出版社,1991.
[135]GB 4339-1999,金属材料热膨胀特性参数的测定[S].北京：中国标准出版社,1999.
[136]高巍,金耀华,王正品等.高温空气介质下T91钢氧化动力学研究[J].材料热处理,2007,36(20)：10-12.
[137]于君燕.9-12%Cr铁素体/马氏体耐热钢的显微组织和力学性能研究[D].山东：山东理工大学,2008.
[2]艾国安,谷力功,曹俊等.高压锅炉管生产现状[J].高压锅炉管生产现状,2006,1：18-22.
[3]王艳华.超超临界汽轮机耐热钢组织和性能研究[D].哈尔滨：哈尔滨工程大学,2006.
[4]宁保群,刘永长,殷红旗.超高临界压发电厂锅炉管用铁素体耐热钢的发展现状与研究前景[J].材料导报,2006,20(12)：83-86.
[5]干勇.关于研发超(超)临界火电机组高级锅炉管的建议[J].中国冶金,2006,15(2)：1-4.
[6]杨富,李为民,任永宁.超临界、超超临界锅炉用钢[J].电力设备,2004,5(10)：41-46.
[7]Zinkle J S. Advanced materials for fusion technology[J]. Fusion Engineering and Design,2005,74(1-4):31-40.
[8]王兴,唐桦,傅佳隽.1000MW超超临界机组主蒸汽与再热汽温度的调整方法[J].上海电力,2009,6：440-442.
[9]季献武,段鹏,李驹等.T23钢在超超临界1000MW机组的应用及现状[J].华东电力,2009,37(12)：2097-2100.
[10]Moitra A, Parameswaran P, Sreenivasan P R, et al. A toughness study of the weld heat-affected zone of a 9Cr-1Mo steel[J]. Materials Characterization, 2002,,48(1):55-56.
[11]Abe FB. Bainitic and martensitic creep-resistant steels[J]. Current Opinion in Solid State and Materials Science,8(3-4):305-311.
[12]Hirose T, Shiba K, Ando M, et al. Joining technologies of reduced activation ferritic/martensitic steel for blanket fabrication[J]. Fusion Engineering and Design,2006,81(1-7):645-651.
[13]Zeman A, Debarberis L, Kocik J, et al. Microstructural analysis of candidate steels pre-selected for new advanced reactor systems[J]. Journal of Nuclear Materials,2007,362(2-3):259-267.
[14]Cho H S, Ohkubo H, Iwata N, et al. Improvement of compatibility of advanced ferritic steels with super critical pressurized water toward a higher thermally efficient water-cooled blanket system[J]. Fusion Engineering and Design,2006, 81(8-14):1071-1076.
[15]Stefano C. Carbon dioxide separation from high temperature fuel cell power plants[J]. Journal of power sources,2002,112(1):273-289.
[16]李兵.超临界压力锅炉过热蒸汽、再热蒸汽气温变工况特性研究[D].济南：山东大学,2008.
[17]张俊峰,王黎明,仲学兰.超超临界火电机组主蒸汽、再热蒸汽管道选材分析[J].华中电力,2010,23(4)：49-52.
[18]罗家华.热处理工艺对12Cr2Mo WVTiB(102钢)组织和性能的影响[D].长沙：中南大学,2006.
[19]孟繁茂,付俊岩.现代含铌不锈钢[M].北京：冶金工业出版社,2004.
[20]任文超.E91 1钢组织与性能的研究[D].哈尔滨：哈尔滨工程大学,2007.
[21]张业圣.我国火力发电用高压锅炉管现状与需求分析[J].钢管,2008,37(5)：1-10.
[22]江永静,成海涛.我国无缝钢管生产现状、差距及发展建议[J].钢管,2004,33(3)：1-6.
[23]杨秀琴.我国钢管工业的现状、问题与发展前景(下)[J].钢管,2008,37(2)：14.
[24]成海涛，郭元蓉.我国电站锅炉用管的现状与发展对策[J].钢管,2006,35(2)：5-9.
[25]廖建国.日本超超临界压力发电用高Cr铁素体耐热钢的开发及现状[J].世界金属导报,2007,010版：16.
[26]徐强,张幸红,韩杰才等.人工神经网络在材料科学中的应用与展望[J].材料科学与工艺,2005,13(4)：352-356.
[27]樊新民,孔见,金波.人工神经网络在材料科学研究中的应用[J].材料导报,2002,16(4)：28-30.
[28]李军,阮晓钢.一种基于神经网络的专家系统设计[J].北京工业大学学报,2003,29(2)：171-174.
[29]赖静,王清,孙东立.人工神经网络在材料研究中的应用[J].材料工程,2006,(1)：458-462.
[30]王海芳.神经网络专家系统的人机界面设计及其在材料设计中的应用[D]天津：河北工业大学,2006.
[31]董敏.人工神经网络与材料工艺的优化[J].辽宁师专学报,2000,2(2)： 100-103.
[32]从爽.神经网络、模糊系统及其在运动控制中的应用[M].合肥：中国科学技术大学出版社,2001.
[33]俞树荣,张俊武,李建华.基于模糊神经网络的材料力学性能识别[J].甘肃工业大学学报,2001,27(1)：8-12.
[34]郭鹏.基于神经网络的高温合金组织性能分析[D].西安：西北工业大学,2004.
[35]袁曾任.人工神经网络及其应用[M].北京：清华大学出版社,1999.
[36]左秀荣.人工神经网络在钢铁材料力学性能预测方面的应用[J].特殊钢,2004,25(5)：26-29.
[37]曾卫东,舒滢,周义刚.应用人工神经网络模型预测Ti-10V-2Fe-3A1合金的力学性能[J].稀有金属材料与工程,2004,33(10)：1041-1044.
[38]董万好,钱初钧,张戈.应用BP神经网络预测钢的回火温度及回火硬度[J].实验室研究与探索,2003,22(6)：50-52.
[39]吴良,钟文锋.基于人工神经网络的结构钢端淬曲线预测系统模型的研究[J].金属热处理学报,2000,(12)：13-17.
[40]任世和,吴良.基于人工神经网络的结构钢回火后力学性能预测[J].金属热处理,2002,27(12)：27-29.
[41]Lin Y C, Fang X, Wang Y P. Prediction of metadynamic softening in a multi-pass hot deformed low alloy steel using artificial neural network[J]. Journal of Materials Science,2008,43:5508-5515.
[42]阳范文,赵耀明.材料性能预测和配方优化的人工神经网络研究方法[J].合成材料老化与应用,2001,(1)：15-19.
[43]刘贵立,张国英,曾梅光.用人工神经网络模型研究微量元素对钢力学性能的影响[J].钢铁研究,2000, (1)：48-50.
[44]蔡安辉,孙国雄.基于人工神经网络的材料力学性能的预测与评估[J].铸造,2003,52(4)：269-271.
[45]左秀荣,薛向欣,姜茂发等.利用人工神经网络研究合金元素对SCM822H钢性能的影响[J].钢铁研究学报,2002,14(1)：51-54.
[46]黄俊.基于人工神经网络的焊接材料设计[D].南京：南京理工大学,2002.
[47]Li H Y, Wei D D, Li Y H, et al. Application of artificial neural network and constitutive equations to describe the hot compressive behavior of 28CrMnMoV steel[J]. Materials and Design,2012, (35):557-562.
[48]Lin Y C, Chen X M. A critical review of experimental results and constitutive descriptions for metals and alloys in hot working[J]. Materials and Design, 2011,32:1733-1759.
[49]Sun Y, Zeng W D, Zhao Y Q et al. Modeling constitutiverelationship of Ti40 alloy using artificial neural network[J]. Materials and Design,2011,32: 1537-1541.
[50]Sun Y, Zeng W D, Zhao Y Q, et al. Development of constitutive relationship model of Ti600 alloy using artificial neural network[J]. Computational Materials Science,2010,48:686-691.
[51]吴良.人工神经网络(ANN)智能技术与热处理[J].纺织机械,2001,(4)：36-38.
[52]Reddy N S, Lee Y H, Park C H, et al. Prediction of flow stress in Ti-6A1-4V alloy with an equiaxed [alpha]+[beta] microstructure by artificial neural networks[J]. Materials Science and Engineering:A,2008,492:276-282.
[53]Lin Y C, Zhang J, Zhong J. Application of neural networks to predict the elevated temperature flow behavior of a low alloy steel[J]. Computational Materials Science,2008,43:752-758.
[54]Li H Y, Hu J D, Wei D D et al. Artificial neural network and constitutive equations to predict the hot deformation behavior of modified 2.25Cr-1Mo steel[J]. Materials and Design,2012,42:192-197.
[55]徐强,张幸红,韩杰才等.人工神经网络在材料科学中的应用与展望[J].材料科学与工艺,2005,13(4)：352-356.
[56]田永君,袁秀信,高慧菊等.热变形中的动态回复过程[J].材料科学与工艺,1987,6(2)：68-74.
[57]Tang Z, Stumpf W. The role of molybdenum additions and prior deformation on acicular ferrite formation in microalloyed Nb-Ti low-carbon line-pipe steels[J]. Materials Characterization,2008,59(6):717-728.
[58]胡安民,孙祖庆.低碳钢多道次热变形中的应变强化相变与铁素体动态再结晶[J].金属学报,2000,36(11)：1192-1196.
[59]Johnson GR, Cook WH. A constitutive model and data for metals subjected to large strains, high strain rates and high temperatures [C]. In:Proceedings of the 7th International Symposium on Ballistics. Den Haag, The Netherlands,1983, p 541-543.
[60]Khan A S, Zhang H Y, Takacs L. Mechanical response and modeling of fully compacted nanocrystalline iron and copper[J]. Int J Plast,2000,16:1459- 1476.
[61]Khan A S, Suh Y S, Kazmi R. Quasi-static and dynamic loading responses and constitutive modeling of titanium alloys[J]. Int J Plast,2004,20:2233-2248.
[62]Farrokh B, Khan A S. Grain size, strain rate, and temperature dependence of flow stress in ultra-fine grained and nanocrystalline Cu and Al:synthesis, experiment, and constitutive modeling[J]. Int J Plast,2009,25:715-732.
[63]Khan A S, Suh Y S, Chen X, et al. Nanocrystalline aluminum and iron: mechanical behavior at quasi-static and high strain rates, and constitutive modeling[J]. Int J Plast,2006,22:195-209.
[64]Molinari A, Ravichandran G. Constitutive modeling of high-strain-rate deformation in metals based on the evolution of an effective microstructural length[J]. Mech Mater,2005,37:737-752..
[65]Sellars C M. On the mechanism of hot deformation[J]. Acta Met,1966,14: 1136-1138.
[66]Kocks U F. Laws for work-hardening and low-temperature creep[J]. J Eng Mater Technol,1976,98:76-85.
[67]Voce E. The relationship between stress and strain for homogeneous deformation[J]. J Inst Met,1948,74:537-62.
[68]Lin Y C, Liu G. A new mathematical model for predicting flow stress of typical high-strength alloy steel at elevated high temperature [J]. Comput Mater Sci, 2010,48:54-58.
[69]Zener C, Hollomon H. Effect of strain rate upon plastic flow of steel[J]. J Appl Phys,1944,15:22-32.
[70]Jonas J, Sellars CM. Tegart WJMcG. Strength and structure under hot-working conditions[J]. Int Metal Rev,1969,14:1-24.
[71]Shi H, McLaren A J, Sellars CM, et al. Constitutive equations for high temperature flow stress of aluminium alloys[J]. Mater Sci Technol,1997,13: 210-6.
[72]Khan A S, Huang S. Experimental and theoretical study of mechanical behavior of 1100 aluminum in the strain rate range 10-5-104 s-1[J]. Int J Plast,1992,8: 397-424.
[73]Preston D L, Tonks D L, Wallace D C. Model of plastic deformation for extreme loading conditions [J]. J Appl Phys,2003,93:211-220.
[74]Tsuchida N, Tomota Y, Moriya H, et al. Application of the Kocks-Mecking model to tensile deformation of an austenitic 25Cr-19Ni steel[J]. Acta Mater, 2001,49:3029-3038.
[75]Voyiadjis G Z, Almasri A H. A physically based constitutive model for fcc metals with applications to dynamic hardness[J]. Mech Mater,2008,40: 549-563.
[76]Bodner SR, Partom Y. Constitutive equations for elastic-viscoplastic strain hardening materials[J]. J Appl Mech,1975,42:385-389.
[77]Goetz RL, Seetharaman V. Modeling dynamic recrystallization using cellular automaton[J]. Scripta Mater,1998,38:405-413.
[78]Cai M C, Niu L S, Ma X F, et al. A constitutive description of the strain rate and temperature effects on the mechanical behavior of materials [J]. Mech Mater,2010,42:774-781.
[79]Rusinek A, Klepaczko J R. Shear testing of a sheet steel at wide range of strain rates and a constitutive relation with strain-rate and temperature dependence of the flow stress[J]. Int J Plast,2001,17:87-115.
[80]Mecking H, Kocks U F. Kinetics of flow and strain-hardening[J]. Acta Metall, 1981,29:1865-1875.
[81]Vural M, Rittel D, Ravichandran G. Large strain mechanical behavior of 1018 cold-rolled steel over a wide range of strain rates [J]. Metallur Mater Trans A, 2003,34A:2873-2885.
[82]Nemat-Nasser S. Flow stress of FCC polycrystals with application to OFHC Cu[J]. Acta Mater,1998,46:565-577.
[83]Park JH, Tomota Y, Takagi S, et al. Estimation of flow stress by the Kocks-Mecking model at strain rate of 103/s for an ultra low carbon steel [J]. Tetsuto Hagane,2001,87:657-664.
[84]Mirzadeh H, Najafizadeh A. Flow stress prediction at hot working conditions[J].Mater Sci Eng A,2010,527:1160-1164.
[85]Zhang H J, Wen W D, Cui H T, et al. A modified Zerilli-Armstrong model for alloy IC10 over a wide range of temperatures and strain rates[J]. Mater Sci Eng A,2009,526:1-6.
[86]Lin Y C, Zhang J, Zhong J. Application of neural networks to predict the elevated temperature flow behavior of a low alloy steel[J]. Comput Mater Sci, 2008,43:752-758.
[87]Rusinek A, Rodriguez-Martinez J A, Arias A. A thermo-viscoplastic constitutive model for FCC metals with application to OFHC Copper[J]. Int J Mech Sci,2010,52:120-135.
[88]Lin Y C, Chen X M. A critical review of experimental results and constitutive descriptions for metals and alloys in hot working[J]. Materials and Design, 2011,32:1733-1759.
[89]Li H Y, Wang X F, Duan J Y, et al. A modified Johnson Cook model for elevated temperature flow behavior of T24 steel[J]. Mater Sci Eng A,2013,577: 138-146.
[90]Zhang H J, Wen W D, Cui H T. Behaviors of IC10 alloy over a wide range of strain rates and temperatures:experiments and modeling steel[J].. Mater Sci Eng A,2009,504:99-103.
[91]文九巴.材料科学与工程[M].哈尔滨：哈尔滨工业大学出版社,2007.
[92]赵乃勤.合金固态相变[M].长沙：中南大学出版社,2008.
[93]李松瑞,周善初.金属热处理[M].长沙：中南大学出版社,2003.
[94]Rometsch P A, Starink M J, Gregson P J. Improvements in quench factor modelling[J]. Materials Science and Engineering A,2003,339(1):255-264.
[95]Liu S D, Zhang X M, Huang Z B, et al. Prediction of hardness of aluminum alloy 7055 by quench factor analysis [J].Materials science forum,2007, 546:881-884.
[96]曹国洋.3Cr-1Mo-0.25V钢动态CCT曲线测定及形变与相变研究[D].秦皇岛：燕山大学,2009.
[97]李红英,耿进锋,李慎金.SWRCH22A钢过冷奥氏体的连续冷却转变,金属热处理,2005,30(9)：17-19.
[98]唐志勇.27MnCrB5钢的奥氏体连续冷却转变研究[J].热加工工艺,2007,6(20)：41-43.
[99]金自力.Gleeble 1500测定45钢CCT曲线的结果分析[J].包头钢铁学院学报,2004,23(4)：326-328.
[100]李红英,肖纳敏,谢先娇.H08Mn2S-钢等温转变曲线和连续冷却曲线[J].中南工业大学学报,2002,33(5)：495-499.
[101]胡怡.GQ112A钢CCT图的测定[J].金属热处理,2001,(7)：16-19.
[102]张黄强.合金钢CCT曲线研究[D].武汉：武汉科技大学,2008.
[103]乔志霞.连续冷却30CrNi3MoV超高强钢固态相变行为[D].天津：天津大学,2010.
[104]张希望.合金钢Q345的CCT曲线及断裂韧性研究[D].长沙：中南大学, 2008.
[105]王凤香,简方.27SiMn钢CCT曲线的测定[J]. 包钢科技,2007,33(2)：26-29.
[106]李红英,耿进锋,龚美涛.65Mn钢奥氏体连续冷却转变曲线(CCT图)[J].材料科学与工艺,2005,13(3)：302-304.
[107]吴化,刘姗姗.差示扫描量热法测定22Mn2SiVBS钢CCT曲线[J].理化检验-物理分册,2008,44(12)：684-688.
[108]商艳,张伟强.X70管线钢的CCT曲线研究[J].沈阳理工大学学报,2012,31(2)：84-86.
[109]赵宝纯, 李桂艳,柳永浩等.含Mo低碳微合金钢CCT曲线的测定与分析[J].热加工工艺,2008,37(22)：37-39.
[110]左碧强,王岩,米振莉等.管线钢X80的CCT曲线研究[J].热加工工艺,2010,39(4)：12-14.
[111]Lin Y C, Chen M S, Zhang J. Constitutive modeling for elevated temperature flow behavior of 42CrMo steel[J]. Comput Mater Sci,2008,424:470-477.
[112]Lin Y C, Chen M S, Zhong J. Study of metadynamic recrystallization behaviors in a low alloy steel[J]. J Mater Process Technol,2009,209:2477-2482.
[113]Lin Y C, Chen M S, Zhong J. Effects of deformation temperatures on stress/strain distribution and microstructure evolution of deformed 42CrMo steel[J]. Mater Des,2009,30:908-913.
[114]Lin Y C, Chen M S. Study of microstructural evolution during static recrystallization in a low alloy steel[J]. J Mater Sci,2009,44:835-842.
[115]Samantaray D, Mandal S, Bhaduri A K, et al. Analysis and mathematical modelling of elevated temperature flow behaviour of austenitic stainless steels[J]. Materials Science and Engineering:A,2011,528(4):1937-1943.
[116]Samantaray D, Mandal S, Borah U, et al. A thermo-viscoplastic constitutive model to predict elevated-temperature flow behaviour in a titanium-modified austenitic stainless steel[J]. Materials Science and Engineering:A,2009, 526(1):1-6.
[117]Phaniraj M P, Lahiri A K. The applicability of neural network model to predict flow stress for carbon steels[J]. Journal of Materials Processing Technology, 2003,141(2):219-227.
[118]徐光.金属材料CCT曲线测定及绘制[M].北京：化学工业出版社,2009.
[119]郭青苗,任吉堂.钢的过冷奥氏体连续转变(CCT)曲线的解析化[J].河北 理工学院学报,2006,28(2)：33-35
[120]Bergeon N, Kajiwara S, Kikuchi T. Atomic force microscope study of stress-induced martensite formation and its reverse transformation in a thermomechanically treated Fe-Mn-Si-Cr-Ni alloy[J]. Acta materialia,2000, 48(16):4053-4064.
[121]王泾文.淬火温度对20CrMnTi钢组织和性能的影响[J].热加工工艺,2005(1)：50-53.
[122]李安铭,王海瑞.亚温淬火对25MnV钢显微组织和力学性能的影响[J].机械工程材料,2009(12)：36-38.
[123]Wang X D, Xu W Z, Guo Z H, et al. Carbide characterization in a Nb-microalloyed advanced ultrahigh strength steel after quenching partitioning-tempering process[J]. Materials Science and Engineering:A, 2010,527(15):3373-3378.
[124]Lee Y K, Shin H C, Jang Y C, et al. Effect of isothermal transformation temperature on amount of retained austenite and its thermal stability in a bainitic Fe-3% Si-0.45% C-X steel[J]. Scripta materialia,2002,47(12):805-809.
[125]Jacques P J, Delannay F, Ladriere J. On the influence of interactions between phases on the mechanical stability of retained austenite in transformation-induced plasticity multiphase steels [J]. Metallurgical and Materials Transactions A,2001,32(11):2759-2768.
[126]GB/T228.1-2010,金属材料室温拉伸试验方法[S].北京：中国标准出版社,2010.
[127]GB/T4338-2006,金属材料高温拉伸试验方法[S].北京：中国标准出版社,2006.
[128]Benjamin F, Maxime S, Alexandra R, et al. Microstructural evolutions and cyclic softening of 9% Cr martensitic steels[J]. Journal of Nuclear Materials, 2009,386:71-74.
[129]GB/T13299-91,钢的显微组织评定方法[S].北京：中国标准出版社,1991.
[130]上官晓峰,王正品,耿波等.T91钢高温空气氧化动力学及层剥落机理[J].铸造技术,2005,26(7)：578-580.
[131]李辛庚,齐慧滨,王学刚等.火电厂锅炉再热器管高温腐蚀研究[J].材料保护,2003,6(36)：9-11.
[132]林俊滨.超超临界锅炉高温管内氧化皮形成机理及堵塞规律研究[D].广州：华南理工大学,2010.
[133]GB/T 13303-91,钢的抗氧化性能测定方法[S].北京：中国标准出版社,1991.
[134]GB/T 11108-1989,硬质合金热扩散率的测定方法[S].北京：中国标准出版社,1991.
[135]GB 4339-1999,金属材料热膨胀特性参数的测定[S].北京：中国标准出版社,1999.
[136]高巍,金耀华,王正品等.高温空气介质下T91钢氧化动力学研究[J].材料热处理,2007,36(20)：10-12.
[137]于君燕.9-12%Cr铁素体/马氏体耐热钢的显微组织和力学性能研究[D].山东：山东理工大学,2008.