THESIS. Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University

Transcription

1 Development of Predictive Formulae for the A 1 Temperature in Creep Strength Enhanced Ferritic Steels THESIS Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University By Lun Wang, B.S. Graduate Program in Welding Engineering The Ohio State University 2010 Master's Examination Committee: Dr. John C. Lippold, Advisor Dr. Sudarsanam S. Babu

2 Copyright by Lun Wang 2010

3 Abstract The creep strength enhanced ferritic (CSEF) steels P91 and P92 are extensively used in fossil supercritical power plants due to their improved creep strength at elevated temperatures. Loss of creep strength and/or toughness may occur in CSEF steel welds due to formation of fresh martensite, ferrite, or retained austenite during welding and post weld heat treatment (PWHT). Predicting the critical phase transformation temperatures is of practical importance for the development of appropriate welding and PWHT procedures. The available empirical formula proposed by Andrew [1] for the determination of the A 1 temperature does not cover the composition range of CSEF steels and the effect of carbon and nitrogen has not been taken into account. Thermodynamic simulation software such as Thermo-Calc and JMat-Pro could predict equilibrium transformation temperatures (i.e. equilibrium formation of austenite, ferrite in steels). However, the microstructure in CSEF steels which is of practical importance for determination of the A 1 temperature is tempered martensite (in as delivered condition). Santella [65] developed two formulae for P91 and P92 steels based on thermodynamic simulation data recently. Predictions by Santella s formulae tend to be conservative estimates of the A 1 temperature in P91 and P92 steels [65]. Thermodynamic simulations and experimental measurement based Design of Experiment (DOE) approaches and fractional data analysis were applied to develop formulae for predicting the A 1 temperature in P91 and P92 steels. The alloying elements ii

4 with a significant effect on the A 1 temperature in P91 steels screened out by the thermodynamic simulation based DOE approach are: Ni, Mn, Si, N, Cr, Mo, C, V. No interactions were found between these significant alloying elements. A measurement based DOE was developed based on the elements with significant effect on the A 1 temperature determined by the thermodynamic simulation based DOE. The test samples were melted using a button melting system in argon environment. These samples were subjected to homogenizing, rolling, normalizing, and tempering in order to reproduce to the manufacturing process of commercial CSEF steels. The A 1 temperatures in these samples were measured by Single Sensor Differential Thermal Analysis (SS-DTA). The results of the measurement based DOE were processed using fractional data analysis to develop predictive formulae for the A 1 temperature in P91 and P92 steels. It was found that C, N, Si and Cr could influence the A 1 temperature largely; Ni and Mn were not the only determining factor of the A 1 temperature. The relations between the concentrations of most alloying elements and the A 1 temperature of P91 and P92 steels were quadratic function. The predictions of these formulae were validated by comparison to measured values of the A 1 temperature in P91 and P92 steels by dilatometry. iii

5 Dedication This document is dedicated to my family. iv

6 Acknowledgments A special thanks is extended to Dr. Boian Alexandrov and Dr. John Lippold for their guidance over the last two years. It has been a great experience and I have learned so much from both of you. Thanks to Dr. Sudarsanam Babu for your inspirations of deep thought. I would like to acknowledge Prof. Theodore Allen of ISE department for his help with software JMP8 and data analysis. Thanks to all the members of Welding and Joining Metallurgy Group, I will miss our lunch time conversations. This project was funded by American Electric Power and Babcock & Wilcox. I am grateful for their support. Additional chemical analysis was sponsored by Babcock & Wilcox. Lastly, I want to thank John Siefert for his valuable support and suggestions throughout this time. v

7 Vita March Born Anqing, China High School Diploma, Susong High School, Anqing, Anhui Province, China B.S. Welding Engineering, Harbin Institute of Technology, China 2008 to present...graduate Research Associate, Welding Engineering Program, The Ohio State University Publications Lun Wang, Boian Alexandrov and John Lippold, Predicting A 1 Temperature in CSEF Steels, MS&T 10, ASM International, Houston, TX, October 2010 Fields of Study Major Field: Welding Engineering vi

8 Table of Contents Abstract... ii Dedication... iv Acknowledgments... v Vita... vi Publications... vi Fields of Study... vi Table of Contents... vii List of Tables... xi List of Figures... xiv Chapter 1 INTRODUCTION... 1 Chapter 2 LITERATURE REVIEW Creep Strength Enhanced Ferritic (CSEF) Steels History of CSEF Steels P91 Steel P92 Steel... 8 vii

9 P122 Steel Microstructure of CSEF Steels Basic Phases in CSEF Steels Precipitates Effect of Alloying Elements Carbon Chromium Nickel Boron Nitrogen Molybdenum Tungsten Molybdenum versus Tungsten Copper Vanadium and Niobium Cobalt Tantalum Manganese and Silicon Phase Transformation Analysis Techniques viii

10 2.2.1 Differential Thermal Analysis (DTA) Differential Scanning Calorimetry (DSC) Dilatometry Single Sensor Differential Thermal Analysis (SS-DTA) The Electrothermomechanical Tester (ETMT) Predicting Methods for Critical Temperatures Predictive Formulae for M s and M f Temperatures Predictive Formulae for B s and B f Temperatures Predictive Formulae for Ac 1 and Ac 3 Temperatures Chapter 3 OBJECTIVES Chapter 4 MATERIALS AND EXPERIMENTAL PROCEDURES Design of Experiments Model Based DOE Measurement based DOE and separate experimental study Sample Preparation Materials Sample Preparation Evaluation of Composition Control Determination of the A 1 temperature ix

11 4.5 Validation of formula predicted A 1 temperature Chapter 5 RESULTS AND DISCUSSION Model Based DOE Experimental Results Measurement based DoE for P91 Steel Determination of the effect of carbon and sillicon on the A 1 temperature in P91 Steel Determination of the effect of nitrogen on the A 1 temperature in P91 Steel Determination of the effect of molybdenum and tungsten on the A 1 temperature in P92 Steel Determination of the effect of copper on the A 1 temperature in P122 Steel Measurement based DOE formulae for the A 1 Temperatures in CSEF Steels Predictive formula for the A 1 temperature in P91 Steel Predictive formula for the A 1 temperature in P92 Steel Validation of formula predicted A 1 temperature Chapter 6 CONCLUSIONS References x

12 List of Tables Table 2.1: Composition of specified 9Cr Steels (wt.%) [11] [12]... 7 Table 2.2: Development of M s temperature predictive formulae [61] Table 2.3: Applicable composition range of formulae for M s (wt.%) [1][56]~[63] Table 2.4: Formulae for B s and B f [60] [62] [64] Table 2.5: Applicable composition range of formulae for B s and B f (wt.%) [60] [62] [64] Table 2.6: Formulae for Ac 1 and Ac 3 [1] Table 2.7: Applicable composition range of formulae for Ac 1 and Ac 3 (wt.%) [1] Table 4.1: ASTM A335 specification for P91, P92 and P122 steels (wt.%) Table 4.2: The first part of model based DOE table with JMat-Pro predicted A 1 temperature values (wt.%) Table 4.3: The second part of model based DOE table with JMat-Pro predicted A 1 temperature values (wt.%) Table 4.4: Purity of pure elements Table 4.5: Composition of base materials and 1080 steel wire (wt.%) Table 4.6: Compositions of melted and heat treated samples (wt.%) Table 4.7: Composition and SS-DTA measured A 1 temperature of P91 steel specimens (wt.%) xi

13 Table 5.1: Composition range of measurement based DoE for P91 steels (wt.%) Table 5.2: Chemical analysis of measurement based DOE samples (wt.%) Table 5.3: Application range of Equation (2) (wt.%) Table 5.4: Measured composition of P91 steel samples with varied C concentration (wt.%) Table 5.5: Measured composition of P91 steel samples with varied Si concentration (wt.%) Table 5.6: Composition of commercial and laboratory heats of P91 steel samples used for determination of the effect of nitrogen on the A 1 temperature (wt.%) Table 5.7: Measured composition of P92 steel samples with varied W concentration (wt.%) Table 5.8: Measured composition of P92 steel samples with varied Mo concentration Table 5.9: Measured composition of P122 steel samples with varied Cu concentration. 95 Table 5.10: Composition (wt.%) of 10 commercial P91 steels and corresponding measured (by dilatometry) and formula predicted A 1 temperature Table 5.11: Predictions for P91 steel samples from different available models ( C) Table 5.12: Composition (wt.%) of 19 commercial P92 steels and corresponding measured (by dilatometry) and formula predicted A 1 temperature Table 5.13: Predictions for P92 steel samples from different available models ( C) Table 5.14: Composition and hardness of tempered P91 specimens xii

14 xiii

15 List of Figures Figure 1.1: Deviations of models predicted A 1 temperature from measured A 1 temperature... 3 Figure 2.1: Development history of 9-12Cr steels [8]... 5 Figure 2.2: Applied stress to rupture time curves of NF616 and P91 [12]... 8 Figure 2.3: Influence of alloying elements on properties in HCM12A [5] Figure 2.4: Typical microstructures of P91 steel: a) fully tempered martensite; b) tempered martensite and δ-ferrite [4] Figure 2.5: Influence of δ-ferrite on toughness and creep strength: a) Relation between percentage of δ-ferrite and toughness at room temperature; b) Relation between percentage of δ-ferrite and creep-rupture time [5] [6] Figure 2.6: Typical precipitates and their distribution in CSEF steels [3] [22] Figure 2.7: Growth of precipitate particles in CSEF steels [3] Figure 2.8: Growth of Laves phase particles in P92 steel at (a) 600 C; (b) 650 C [3] Figure 2.9: Time to rupture and minimum creep rate of 9Cr steel at 600 C and 140 Mpa as a function of carbon concentration [18] Figure 2.10: Amount of M 23 C 6 carbide and MX carbonitrides in 9Cr steel at tempering temperature of 800 C as a function of carbon concentration [18] xiv

16 Figure 2.11: Y-groove restraint weld cracking test of 11Cr-2W-0.4Mo-1Cu-v-Nb-0.003B steels with different carbon concentrations at different preheating temperature [5] Figure 2.12: Influence of Ni and Cu on the extrapolated creep rupture strength of 0.1C- 11Cr-2W-0.4Mo-Cu steels [5] Figure 2.13: Formation process of M 23 (CB) 6 during heat treatment [18] Figure 2.14: (a) Enrichment of boron in M 23 C 6 near PAGBs in 9Cr-3W-3Co-0.2V-0.08C steel with 139 ppm boron; (b) Size of M 23 C 6 particles as a function of boron concentration [18] Figure 2.15: Time to rupture and minimum creep rate of 9Cr-3W-3Co-0.2V-0.05Nb- 0.08C steel with 140ppm B at 650 C and 120Mpa as a function of nitrogen concentration [18] Figure 2.16: Creep rupture strength of 9Cr-0.5Mo-W steels tested at 600 C and 700 C [28] Figure 2.17: Tensile properties of 9Cr-0.5Mo-W steels tested at room temperature [28]. 28 Figure 2.19: Effect of W+Mo on creep rupture strength and Charpy absorbed energy in 12Cr turbine steels [32] Figure 2.18: (a) Effect of Mo+W on creep rupture strength of 12% Cr steels [27]; (b) Effect of Mo and W on 10 5 h creep rupture strength at 650 C and Charpy absorbed energy at 20 C of 12% Cr turbine steels (0.13C-0.05Si-0.5Mn-11.2Cr-0.8Ni-0.2V-0.5Nb-0.05N- Mo-W) [31] Figure 2.20: (a) Effect of V and Nb on 10 4 h creep rupture strength of NF616 at 600 C; (2) Effect of V and Nb on 10 4 h creep rupture strength of NF616 at 650 C [30] xv

17 Figure 2.21: (a) Influence of V and Nb on tensile properties ultimate tensile strength at 823K; (b) Influence of V and Nb on ductile-brittle transition temperature after aging at 873K for 6000h [34] Figure 2.22: Influence of Nb on creep rupture time at 873K [34] Figure 2.23: Influence of V on creep rupture time at 873K [34] Figure 2.24: (a) Schematic of DTA installation [48]; (b) Typical DTA curves of low alloy Cr-Mo steel a- hot rolled and b- as quenched [49] Figure 2.25: (a) Schematic of DSC installation; (b) Typical DSC result. [50] Figure 2.26: Linear thermal expansion coefficient of ferrite and austenite [51] Figure 2.27: Typical Dilatometry curve of steel [ASTM A ] Figure 2.28: Schematic of SS-DTA installation [54] Figure 2.29: SS-DTA result of steel HSLA-65: (a) sample cooling curve and reference curve; (b) difference between sample cooling curve and reference curve as a function of sample temperature [54] Figure 2.30: Image of ETMT [55] Figure 2.31: Formula of M s developed by Beres and Irmer(1994) [63] Figure 4.1: Structure of the model based DOE used in this study Figure 4.2: JMP8 starter menu and DOE features Figure 4.3: Defining the response for model based DOE in JMP Figure 4.4: Defining the factors for model based DOE in JMP Figure 4.5: Defining the model effects for model based DOE in JMP xvi

18 Figure 4.6: Defining the number of center points and replicates for model based DOE in JMP Figure 4.7: The model fitting platform for model based DOE in JMP Figure 4.8: Schematics of sample melting, casting and heat treatment: a) Tungsten arc button melting system; b) Sample casting system; c) Cubic copper casting mold; d) Shape of sample after casting; e) Shape of sample after hot-rolling; f); Heat history of normalizing; g) Heat history of tempering Figure 4.9: EDX map analysis result of heat treated samples Figure 4.10: A 2, A 1, A 3 temperatures in laboratory melted heat of P91 steel determined by SS-DTA Figure 5.1: The JMat-Pro predicted A 1 temperature versus A 1 temperature predicted by the model based DOE Figure 5.2: Elements with significant influence on the JMat-Pro predicted A 1 temperature in P91 steel and their estimated effects Figure 5.3: Prediction profiler of model based DOE model of A 1 temperature in P91 steel Figure 5.4: Interaction profiler of model based DOE model of A 1 temperature in P91 steel Figure 5.5: Defining the response for measurement based DOE in JMP Figure 5.6: Defining the factors for measurement based DOE in JMP Figure 5.7: Defining the model for measurement based DOE in JMP xvii

19 Figure 5.8: Defining the number of center points and replicates for measurement based DOE in JMP Figure 5.9: Measurement based DOE table with measured A 1 temperature values (testing error of type K thermocouple is ± 0.4%) Figure 5.10: Predictive model of the A 1 temperature in P91 steel as a function of composition of Ni, Mn, Cr, Mo, V and Cu developed using JMP8 by measurement based DOE study Figure 5.12: Elements with significant influence on measured A 1 temperature in P91 steel and their estimate effects Figure 5.11: The actual (measured) A 1 temperature versus the experiment based DOE model predicted A 1 temperature Figure 5.13: Prediction profiler of measurement based DOE model of A 1 temperature in P91 steel Figure 5.14: Influence of C and Si on the A 1 temperature in P91 steel Figure 5.15: Influence of W and Mo on the A 1 temperature in P92 steel Figure 5.16: Influence of Cu on the A 1 temperature in P122 steel Figure 5.17: Comparison of A 1 temperature in P91 steels predicted by measurement based DOE formula and A 1 temperature determined by dilatometry Figure 5.18: Predictions for P91 steel samples from different available models Figure 5.19: Comparison of A 1 temperature in P92 steels predicted by the proposed measurement based DOE formula and A 1 temperature determined by dilatometry Figure 5.20: Predictions for P92 steel samples from different available models xviii

20 Figure 5.21: Microscopy of tempered samples (X1000): a) Specimen1 tempered at 825 C for 30min, hardness is 140 HV; b) Specimen1 tempered at 850 C for 30min, hardness is 297 HV; c) Micro-hardness of specimen1 tempered at 850 C (load 50g); d) Specimen2 tempered at 825 C for 30min, hardness is 152 HV; e) Specimen2 tempered at 850 C for 30min, hardness is 238 HV; f) Micro-hardness of specimen2 tempered at 850 C (load 50g) xix

21 Chapter 1 INTRODUCTION In order to improve the thermal efficiency of fossil power plants and reduce the emission of carbon dioxide, CSEF steels (P91 and P92) have been used extensively. In these steels, (Cr, Fe, Mo) 23 C 6 and fine niobium and vanadium carbonitrides (MX) form within a martensite matrix during the tempering treatment. Because of the stability at elevated temperature, the precipitation of vanadium nitride plays a decisive role in maintaining creep strength at high temperature [2]. Degradation occurs during the creep process, due to coarsening of precipitates and growth of Laves phase (Fe,Cr) 2 Mo [2]. Compared to P91 steel, fine stable Laves phase particles (Fe,Cr) 2 (Mo,W) were found in P92 steel due to the addition of tungsten [3]. A small amount of boron (of about wt.%) has a strong stabilizing effect on M 23 C 6 particles [3]. To guarantee the high temperature creep strength, the microstructure of CSEF steels should be tempered martensite with properly distributed carbide and carbonitride precipitates [4]. Tempering above the A 1 temperature results in formation of ferrite and fresh martensite, and dissolution /coarsening of the carbides, which adversely affects toughness and creep strength of CSEF steels [5] [6]. Accurate determination of the A 1 temperature in CSEF steels is essential for the development of proper tempering procedures. The A 1 temperature in CSEF steels is strongly affected by the steel composition and there is no data available. Andrew [1] 1

22 proposed a formula for the Ac 1 temperature as shown below, based on collected data of 196 general steels with a wide composition range: Ac1( C) = Mn Ni Si Cr + 290As W This function does not consider the influence of C, N, Mo, V, and Cu on the A 1 temperature, and its application range does not cover the content of Cr, Cu in CSEF steels. The formula proposed by Andrew shows high deviation from A 1 temperatures in CSEF steels measured by dilatometry and SS DTA. Thermodynamic simulation software such as Thermo-Calc and JMat-Pro also could be used to predict equilibrium phase transformation temperatures in steels. However, by comparing to our data of measured A 1 temperatures, A 1 temperature calculated by thermodynamics calculation with JMat-Pro software was found to be lower than measured A 1 temperature in CSEF steels. Based linear regression analysis of thermodynamic simulation data base, Santella [65] developed two expressions which were supposed to predict the A 1 temperature in P91 and P92 steels thermodynamically: Grade 91 steel: A1( C) = Cr Mo Si V Nb 67.3C 130.6N 60.5Mn 72.3Ni Grade 92 steel: A1( C) = Cr Mo +10.8W Si V Nb 80.6C 150.7N 55.1Mn 68.0Ni 2

23 The deviations of different models predicted A 1 temperature from measured A 1 temperature (heating rate is 28 C/h) in 8 commercial P91 steels are shown in Figure ZC131 ZC132 ZC ZC134 ANP8 SRP1 ZC133 ZC11 Andrew s Function JMat-Pro Thermo-Calc Michael's Expression Figure 1.1: Deviations of models predicted A 1 temperature from measured A 1 temperature This paper describes the development of formulae for predicting the A 1 temperature in CSEF steels. These formulae have been developed using a DOE approach based on SS-DTA measurements of the A 1 temperature in laboratory heats of P91 and P92 steels. The proposed formulae have been validated by comparison to measured values of the A 1 temperature in P91 and P92 steels. These formulae show better agreement with the A 1 temperature in commercial CSEF steel measured by SS DTA and dilatometry than the available predictive formulae and models. 3

24 Chapter 2 LITERATURE REVIEW 2.1 Creep Strength Enhanced Ferritic (CSEF) Steels History of CSEF Steels According to Scarlin[7], power plant steels should meet the following general requirements: high 100,000h creep-rupture strength (about 100Mpa) at elevated temperatures of 600 C or even higher; good weldability for pipes and tubes; fabricable for large components; highly resistant to high temperature steam oxidation and corrosion for boiler tubes; uniform hardening and mechanical properties in large rotor components; high toughness and low susceptibility to embrittlement and softening when service at elevated temperatures for long time. The development history of 9-12Cr ferritic steels is shown in Figure 2.1 [8]; the creep-rupture strength at 600 C and 10 5 hours is believed to be the most important mechanical property for power plant steels. In the 1940s [9], 2.25Cr-1Mo steel were first used as a high chromium steel in power plants. Until the 1950s [10], 2.25Cr-1Mo steel was the most used high chromium steel in Europe and the US; however the poor creep resistance of 2.25Cr-1Mo steel was an issue for some of components. In order to achieve higher thermal efficiency of fossil-fuel power plants and reduce the CO 2 emission, new 9-12Cr ferritic steels with higher creep-rupture strength at elevated temperatures and improved oxidation and corrosion resistance were developed in Europe, Japan and the US [10] [11] [12]. 4

25 (P92) (P122) Figure 2.1: Development history of 9-12Cr steels [8] 5

26 P91 Steel In the middle 1960s [11], the first 9Cr steel was developed by Center for Metallurgical Research of Liege in Belgium. Several years later in France, the first material named EM 12 was fabricated and was approved for power plant applications. As shown in Table 2.1, EM 12 contains 9 wt.% chromium and 2 wt.% molybdenum. Since 1978, EM 12 had been installed in lots of fossil fuel power boilers mainly in Europe and some other countries and many are still in use today. High ferrite stabilizers in EM12 result in a duplex microstructure containing δ-ferrite and low impact toughness. So EM12 is not applicable for thick components. In the 1970s [10], the Oak Ridge National Laboratory (ORNL) in the US paid much attention to developing a modified 9Cr steel. The composition of modified 9Cr steel (T91) is shown in Table 2.1. Composition of EM 12 was optimized by ORNL, modified 9Cr-1Mo steel contains lower manganese, molybdenum, niobium and vanadium, and nitrogen of controlled extent was added for the first time. In the early 1980s, ASTM approved the modified 9Cr-1Mo steel for power plant applications in standard A 213 T91 and A 335 P91. Excellent corrosion resistance of T91 and P91 steels meets the requirement of modern materials to be applied in modern fossil-fuel power boilers with operating temperature of about 600 C to 625 C. 6

27 Grades EM12 T91 ASTM NF616 P92 ASTM A 213 A 335 C 0.15 max Mn Si max. S Max Same as Cr-2Mo steel P Max Same as Cr-2Mo steel Cr Ni 0.3 max max. Same as 0.40 max. 9Cr-2Mo steel Mo V Nb N W B ppm Al max max. Sn max Table 2.1: Composition of specified 9Cr Steels (wt.%) [11] [12]. 7

28 P92 Steel In 1993 [12], Nippon Steel developed a 9Cr-0.5Mo-1.8W-Nb-V steel, named NF616. Composition range of first four experimentally tested NF616 steels is shown in Table 2.1. The composition of NF616 samples were the same as T91 steel except that part of molybdenum was replaced by tungsten in order to obtain solid-solution strengthening. Vanadium and niobium were added to ensure precipitation strengthening with carbonitrides. In NF616 microstructure of fully tempered martensite without δ- ferrite was achieved. As shown in Figure 2.2, at 600 C the slope of applied stress to rupture time curve of NF616 was gentler than P91 steel, and the creep-rupture strength at 600 C and 10 5 hours of NF616 was significantly higher than P91 steel. In the 1990s, the NF616 was standardized by ASTM as pipe steel (P92) and as tube steel (T92) [13] [14]. Figure 2.2: Applied stress to rupture time curves of NF616 and P91 [12]. 8

29 P122 Steel In the late 1960s [10], in order to solve the problem with duplex phase EM12, the first 12Cr-1Mo steel was developed in Germany with the designation of X20CrMoV12-1 (X20). While X20 had a fully martensitic microstructure, it exhibits lower creep strength than EM12 at elevated temperatures and was difficult to weld, primarily due to high carbon content of 0.20 wt.% [15]. In the 1980 s [16], a 0.1C-12Cr-1Mo-1W-V-Nb steel tube (HCM12) was manufactured by Sumitomo Metal in Japan, the creep-rupture strength of both base metal and welded joint of HCM12 was higher than P91 and T91 steels. However, for thick components, formation of δ-ferrite in HCM12 decreases impact toughness [5]. The influence of each alloying element on the properties of HCM12 steel is shown in Figure 2.3. In 1992 [5], by optimizing the composition of HCM12, a 0.1C-11Cr-2W-0.4Mo-1Cu steel pipe (HCM12A) was produced by Sumitomo Metal in Japan. Copper was added to prohibit the formation of δ-ferrite, and also make it less susceptible to the HAZ softening. Adding more tungsten instead of molybdenum, and around wt.% boron, effectively improved the creep property of that steel. In 1994 [14], HCM12A was standardized by ASME Code as P122. 9

30 Figure 2.3: Influence of alloying elements on properties in HCM12A [5] 10

31 2.1.2 Microstructure of CSEF Steels Basic Phases in CSEF Steels In order to guarantee the creep strength at high temperature (600 C to 650 C), the optimal microstructure of CSEF steels is tempered martensite [4]. Figure 2.4(a) shows the optimal microstructure of fully temepred martensite for P91 steel; Figure 2.4(b) shows the existence of δ-ferrite in P91 steel. Testing results showed sample (a) had both better tensile strength and creep-rupture strength at 650 C than sample (b). a) b) δ Figure 2.4: Typical microstructures of P91 steel: a) fully tempered martensite; b) tempered martensite and δ-ferrite [4]. It was found that the presence of δ-ferrite in the microstructure degraded both toughness and creep strength of CSEF steels [5] [6]. As shown in Figure 2.5(a), the 11

32 toughness of a 0.1C-11Cr-W-Mo-V steel decreased with the content of δ-ferrite increasing: Figure 2.5(b) showed the increasing of δ-ferrite formation reduced the rupture time of a 0.1C-9Cr-1Mo-V steel at 600 C and 167Mpa. a) b) Figure 2.5: Influence of δ-ferrite on toughness and creep strength: a) Relation between percentage of δ-ferrite and toughness at room temperature; b) Relation between percentage of δ-ferrite and creep-rupture time [5] [6]. The reason of δ-ferrite degrading creep strength of CSEF steels was researched by Kimura et al. [45]. It was found that significant C and N concentration gradients between tempered martensite and δ-ferrite stimulated formation of some large precipitate particles thus degraded the creep strength of the steel. 12

33 Precipitates The main advantage of CSEF steels is their high creep strength at elevated temperature (600 C to 650 C), which requires that the steel could keep high dislocation density and fine subgrain microstructure during the long-term creep process at 600 C to 650 C [3]. It was found that precipitation hardening was the main hardening mechanism in CSEF steels; fine stable precipitations particles could pin the dislocations and subgrain boundaries. As shown in Figure 2.6, the main precipitates in CSEF steels are M 23 C 6 carbides, MX carbonitrides and Laves phase. The M 23 C 6 carbides and MX carbonitrides could form during tempering process, Laves phase only forms during the high temperature creep service. The M 23 C 6 carbides and Laves phase primarily form at prior austenite boundaries and lath boundaries, while fine MX carbonitrides precipitate in ferrite matrix within the laths [22]. Figure 2.6: Typical precipitates and their distribution in CSEF steels [3] [22]. 13

34 a) P91 P92 Laves phase P91 P92 M 23 C 6 b) Figure 2.7: Growth of precipitate particles in CSEF steels [3]. J. Hald collected data and developed diagram of precipitate particles growing in P91 and P92 steels [2]. As shown in Figure 2.7(a), at 600 C the growth of M 23 C 6 carbides in P92 steel was much slower than that of P91 steel, this was because of addition of boron in P92 steel. Figure 2.7(b) shows that the MX carbonitride particles are stable during aging at 600 C up to 60,000 hours. Figure 2.7(a) also shows that after aging at 600 C for 50,000 hours the mean radius of Laves phase particles in P91 is five times larger than that of P91 steel. This was believed to be the main reason for P92 steel having higher creep strength than P91 steel. Since the aging temperature (600 C) is close to the dissolution temperature of Mo-rich Laves phase (650 C) in P91 steel, the nucleation of Mo-rich 14

35 Laves phase became very difficult and only a few particles nucleated and then grew into extended large particles. The W-rich Laves phase in P92 steel had a higher dissolution temperature (720 C) than Mo-rich Laves phase. This explanation also was verified by the growth of Laves phase particles in P92 steel at 650 C, as shown in Figure 2.8. At 650 C the Laves phase in P92 steel could grow into much bigger particles at relatively shorter time than at 600 C, because the aging temperature of 650 C is too close to the dissolution temperature of W-rich Laves phase (720 C) [3]. Figure 2.8: Growth of Laves phase particles in P92 steel at (a) 600 C; (b) 650 C [3]. A study on 11Cr-0.87Cu steel (P122) showed that copper precipitate particles formed during tempering process grew fast during aging process [27]. It was also observed that Laves phase particles in P122 steel were finer than these in P92 steel. This was explained by the presence of more nucleation sites for Laves phase in P122 steel because the copper precipitates could serve as nucleation sites for Laves phase [27]. 15

36 In 1986 Z-phase (Cr(V, Nb)N) was first observed by Schnabel et al. in steel X19CrMoVNbN11-1 which contained 12 wt.% chromium [46]. The niobium content of this 12Cr steel was around five times higher than CSEF steels, so Z-phase was not expected to form in CSEF steels. However, recently Z-phase in form of Cr(V, Nb)N was found in a T122 steel with 12 wt.% chromium after exposure at elevated temperature for more than 10,000 hours, and this caused breakdown of long-term stable creep strength of this CSEF steel [3]. It was explained by the formation of Z-phase which consumed V, Nb and N from the MX carbonitrides and thus dissolved fine MX carbonitrides particles that were essential for long-term creep strength of CSEF steels. It was believed that the high chromium content accelerated the formation of Z-phase. This could be a potential problem for niobium containing steel with more than 10 wt.% chromium [3]. This became a challenge for the development of new CSEF steels with higher oxidation resistance that required higher chromium concentration. Z-phase in form of Cr(V, Ta)N was also observed in a steel containing 12 wt. % chromium and tantalum. In 2009, Hald and Danielsen proposed the idea of Z-phase strengthened 12Cr steel [47]. Accelerating the precipitation of Z-phase particles and forming fine Z-phase could make the Z-phase particles induce precipitating strengthening to steel. This could be achieved by increasing the chromium to as high as possible (12 wt.% for CSEF steels) and adding cobalt. It was also calculated that Z-phase in form of CrNbN and CrTaN grows at much slower rate than CrVN or Cr(V, Nb)N, even at the same level as the MN nitrides. Onging work in TU Denmark showed that it is possible to developed high chromium Z-phase (main population is below 30nm) strengthened matensitic steels [47]. 16

37 2.1.3 Effect of Alloying Elements Carbon Copeland and Licina s research revealed that increasing the carbon content from 0.01 wt.% to 0.13 wt.% increased the creep and rupture strength in 2.25Cr-1Mo steel. They also found that as the temperature increased, the effect of carbon on creep and rupture strength decreased [17]. Masuyama et al. proposed that in modified 9Cr weld metal a minimum of 0.08 wt.% carbon content is required to guarantee the creep strength [9]. The creep strength in steel with low carbon content is reduced because of the formation of insufficient amount of M 23 C 6 and MC 6 precipitates. However, as shown in Figure 2.9, recent research by Fujio Abe showed that the 9Cr-3W-3Co-0.2V-0.05Nb-0.05N steel with lower than 0.05 wt.% carbon had higher time to rupture and lower minimum creep rate at 600 C and 140Mpa [18]. The rupture time was independent of carbon concentration when carbon concentration was higher than wt.%, but it significantly increased when carbon concentrations was below wt.%. This agreed with the Thermo-calc evaluation of equilibrium phases as shown in Figure 2.10, the creep strength of 9Cr steel was significantly improved by elimination of M 23 C 6 and dispersion of nanosize MX nitrides. Wey et al. reported that the coarsening rate of Cr carbides in iron was much higher than carbonitrides of V and Nb at elevated temperatures [19]. The amount of M 23 C 6 carbides rich in Cr decreased with decreasing carbon concentration. There were more MX carbonitrides than M 23 C 6 carbides at carbon concentrations below 0.02 wt.% during tempering at 800 C. The MX carbonitrides consisted of mainly vanadium nitrides and of small amount of niobium nitrides [20]. 17

38 Figure 2.9: Time to rupture and minimum creep rate of 9Cr steel at 600 C and 140 Mpa as a function of carbon concentration [18] Figure 2.10: Amount of M 23 C 6 carbide and MX carbonitrides in 9Cr steel at tempering temperature of 800 C as a function of carbon concentration [18] 18

39 Carbon concentration also had significant effect on weldability of 9-12Cr steels. Iseda et al. carried on y-groove restraint weld cracking test for 11Cr-2W-0.4Mo-1Cu-v- Nb-0.003B steels with different carbon concentrations at different preheating temperatures, the result is shown in Figure 2.11 [5]. The result of y-groove restraint weld cracking test showed the susceptibility to cold weld cracking decreased by lowering the carbon concentration from 0.08 wt.% to 0.14 wt.%. The cracking was completely suppressed when the preheating temperature was adjusted above 150 C and carbon concentration was controlled less than 0.12 wt.%. Increasing of the carbon concentration could also result in decreasing the toughness [10]. Figure 2.11: Y-groove restraint weld cracking test of 11Cr-2W-0.4Mo-1Cu-v-Nb-0.003B steels with different carbon concentrations at different preheating temperature [5] 19

40 Chromium Chromium is one of the major alloying elements in Cr-Mo ferritic steels to promote oxidation and corrosion resistance [10]. With addition of chromium, a stable layer of oxide of stoichiometry (Fe,Cr) 2 O 3 forms on the steel surface, which protects steel from further oxidation and corrosion [21]. Experience shows that 12 wt.% chromium are needed to raise the oxidation resistance to an acceptable level [42]. Recent research by Abellan et al. showed that the oxidation resistance of their samples with 10.8 wt.% chromium and 11 wt.% chromium was distinctively better than of piping steel P92 in an environment of Ar-50%H 2 O at 600 C and 650 C, and even somewhat higher than sample with 12 wt.% chromium [43]. Although chromium does not exhibit an obvious effect on creep strength, high strength is more likely to be obtained when chromium is 2 wt.% and between 9 and 12 wt.% in a ferritic steel, strength declines between these two chromium concentrations [22]. However, the tested creep rupture time at 650 C and 100Mpa of newly developed ferritic-martensitic steels during last decades proved that highest creep strength was obtained at about 9.5 wt.% chromium [42]. Chromium is a strong carbide former; the most common Cr-rich carbide are M 23 C 6 carbides; M is predominantly Cr but also have Fe, Mo and W. The M 23 C 6 carbides are beneficial to the rupture strength when they are finely dispersed, however they are no stable at elevated temperature because they spheroidize easily and become large and blocky quickly [10]. Chromium is also a strong ferrite stabilizer, when adding chromium into Cr-Mo steel there is a risk of formation of δ-ferrite. 20

41 Nickel Nickel is a strong austenite stabilizer that could inhibit the formation of δ-ferrite to improve the toughness of steels. Nickel also could decrease the A 1 temperature [22]. The effect of nickel on creep rupture strength at 600 C for 10 5 hours is shown in Figure 2.12 [5]. Nickel decreased the creep rupture strength at 600 C for 10 5 hours quickly when its concentration is above 0.4 wt.%; it was believed that Laves phase precipitation was accelerated by excessive nickel addition. The ASTM specification limits the nickel content in both P91 and P92 steels bellow 0.4 wt.%. Figure 2.12: Influence of Ni and Cu on the extrapolated creep rupture strength of 0.1C- 11Cr-2W-0.4Mo-Cu steels [5] 21

42 Boron Boron has a strong stabilizing effect on M 23 C 6 carbides. Takahashi et al. reported that for 0.2C-10.5Cr-1.5Mo-0.2V-0.2Nb-0.02N steel, addiction of 120 to 370ppm boron improved the creep rupture strength at 650 C [23]. They observed that the M 23 C 6 carbides at the prior austenite grain boundaries (PAGBs) were finer than in a steel without boron. The influence of boron on M 23 C 6 carbides in a 9Cr-3W-3Co-0.2V-0.08C steel (without N) was recently researched by Fujio Abe [18]. B is a strong nitride former, so nitrogen was not added to avoid formation of boron nitride. Therefore only M 23 C 6 carbides and no MX cabonitrides formed after tempering. As shown in Figure 2.13, during the normalizing process grain boundary segregation of boron took place. During the tempering process precipitation of M 23 C 6 carbide could take place preferentially at grain boundaries. The M 23 C 6 enriched with B become M 23 (CB) 6 at the vicinity of PAGBs. As shown in Figure 2.14(a), after heat treatment and aging the segregation of B could be up to around 5 at.%. Figure 2.14(b) shows that after ageing at 625 C for hours, the size of M 23 C 6 carbides on the PAGBs is reduced at higher B concentration. Reduction of coarsening rate of M 23 C 6 carbides on PAGBs was considered to be related to the segregation of boron, but the detailed mechanism was not clarified. In Europe, 9-12Cr steel containing 100ppm or higher B were considered to be used in next-generation USC power plants at 625 C to 650 C [24] [25]. 22

43 Figure 2.13: Formation process of M 23 (CB) 6 during heat treatment [18]. (ppm) Figure 2.14: (a) Enrichment of boron in M 23 C 6 near PAGBs in 9Cr-3W-3Co-0.2V-0.08C steel with 139 ppm boron; (b) Size of M 23 C 6 particles as a function of boron concentration [18]. 23

44 Nitrogen Nitrogen is more effective than carbon in improving creep rupture strength in ferritic steels [6]. Nitrogen has low solubility in the ferrite and tends to form precipitate as fine MX carbonitrides. However, other large nitrides particles like Cr 2 N and BN should be avoided to prevent deterioration of toughness [18] [26]. As shown in Figure 2.15, for a 9Cr-3W-3Co-0.2V-0.05Nb-0.08C steel with 140ppm boron the peak time to rupture and minimum creep rate at 650 C and 120Mpa were located at around 80 to 100 ppm nitrogen [18]. By calculation, at normalizing temperature of about 1100 C in 9Cr- 3W-3Co-0.2V-0.05Nb-0.08C steel with 140ppm boron, only around 95 ppm nitrogen could dissolve in the matrix without formation of boron nitride. Addition of small amount of nitrogen without formation of boron nitride during normalizing significantly improved the creep strength of steel. However, formation of boron nitride when more nitrogen was added caused the degradation of creep strength of steel. It was also reported by Sawada et al. that large Cr 2 N particles could form along the lath, block and pocket boundaries and PAGBs after tempering at 790 C for 4 hours in 9Cr steel with 0.07 wt.% nitrogen [26]. During creep at 650 C, the agglomeration and coarsening rates of MX carbonitrides in steel with 0.07 wt.% nitrogen were greater than those in steel with 0.05 wt.% nitrogen. It is believed that there should be an optimal content of nitrogen relative to other nitride forming elements such as boron [22]. 24

45 (ppm) Figure 2.15: Time to rupture and minimum creep rate of 9Cr-3W-3Co-0.2V-0.05Nb- 0.08C steel with 140ppm B at 650 C and 120Mpa as a function of nitrogen concentration [18]. 25

46 Molybdenum Molybdenum is a ferrite stabilizer and strong carbide former. It was believed that for a 2.25Cr-Mo-0.25V steel, molybdenum increased the creep resistance of steel when it was present either in solid solution or as carbide precipitate [29]. It was found that for a 3Cr-Mo steel, the rupture time tested at 538 C and different stress levels increased with increasing of Mo from 0.8 wt.% to 1.6 wt.% [29]. In a recent research conducted by Hald, it was proposed that solid solution strengthening from molybdenum had no significant effect on long-term microstructure stability of 9-12Cr steels, because at 600 C the solute molybdenum atoms diffused at a rate similar to the dislocation velocity [3]. Laves phases (Fe,Cr) 2 Mo or (Fe,Cr) 2 (Mo,W) would form during long-term aging process. Mo is also one of the strong M 23 C 6 carbide formers. 26

47 Tungsten Tungsten is a ferrite stabilizer. It was believed that the replacement of part of molybdenum by tungsten in NF616 (P92) steel improved the creep rupture strength by solid solution strengthening provided by tungsten [12]. It was reported that for a 9Cr- 0.5Mo-W steel both tensile strength and creep rupture strength increased with increasing tungsten content [28]. As shown in Figure 2.16, at 600 C and 700 C, the creep rupture strength of a 9Cr-0.5Mo-W steel increased with increasing the tungsten content. It was believed that this effect was due to solid solution hardening by tungsten. However, as shown in Figure 2.17, the impact toughness decreased with increasing tungsten content in both tempered and aged conditions. Considering the creep rupture strength and toughness, the optimum tungsten content was determined to be around 2 wt.%. Recently Hald suggested that solid solution strengthening from tungsten had no significant effect on long-term microstructure stability of 9-12Cr steels [3]. It was found that after aging at 600 C for hours, tungsten in P92 steel produced stable and finer Laves phase particles than those in P91 steel, thus improving the creep rupture strength of P92 steel. This was explained by the W-rich Laves phase (Fe,Cr) 2 (Mo,W) having a higher dissolution temperature (720 C) than Mo-rich Laves phase (Fe,Cr) 2 Mo (650 C). Aging temperature (600 C) is close to the dissolution temperature of Mo-rich Laves phase (650 C), so nucleation of Mo-rich Laves phase became very difficult and only a few particles nucleated then grown into extended large particles. In P92 steel, loss of tungsten from solid solution through Laves phase precipitating also improved the stability of M 23 C 6 carbides, because tungsten is also one of the strong M 23 C 6 carbide formers. 27

48 Figure 2.16: Creep rupture strength of 9Cr-0.5Mo-W steels tested at 600 C and 700 C [28]. Figure 2.17: Tensile properties of 9Cr-0.5Mo-W steels tested at room temperature [28]. 28

49 Molybdenum versus Tungsten As discussed above both molybdenum and tungsten were believed to be solid solution strengthening elements [12] [29], later research found both of them were strong M 23 C 6 carbides formers and during the aging process they were strong Laves phases formers [3]. So the combination effect of molybdenum versus tungsten was of interest. As shown in Figure 2.18(a), increasing the tungsten ratio while keeping the Mo equivalent (Mo+0.5W) at 1.5 wt.% was the most efficient for creep strengthening [30]. Result of further research on 12Cr turbine steels is shown in Figure 2.18(b). The content of tungsten and molybdenum were changed by increasing tungsten and decreasing molybdenum on the basis of 1 wt.% W and 1 wt.% Mo in 12Cr steel. The time to rupture was found to be longest at the combination of 1.8 wt.% W and 0.7 wt.% Mo [31]. The effects of molybdenum and tungsten concentrations on creep rupture strength at 650 C for 10 5 hours and the Charpy impact energy are shown in Figure 2.14 [32]. It was empirically shown that creep strength achieved peak when the Mo equivalent (Mo+0.5W) was set at 1.5 wt.%. It was found that increasing tungsten content improved creep strength while reduced ductility and toughness. The line in Figure 2.19 represents Mo equivalent of 1.5 wt.%. Consequently, for particular composition of a 9-12Cr steel there should be an optimal balance of tungsten and molybdenum content. 29

50 (a) (b) Figure 2.18: (a) Effect of Mo+W on creep rupture strength of 12% Cr steels [27]; (b) Effect of Mo and W on 10 5 h creep rupture strength at 650 C and Charpy absorbed energy at 20 C of 12% Cr turbine steels (0.13C-0.05Si-0.5Mn-11.2Cr-0.8Ni-0.2V-0.5Nb-0.05N-Mo-W) [31]. Figure 2.19: Effect of W+Mo on creep rupture strength and Charpy absorbed energy in 12Cr turbine steels [32]. 30

51 Copper Copper is austenite stabilizer, high copper was added to HCM12 for the first time in order to suppress the formation of δ-ferrite and without reducing the creep strength [5]. Addition of copper enhanced the resistance to HAZ softening without deterioration of creep rupture strength and ductility [10]. As shown in Figure 2.12, copper did not decrease the creep rupture strength at 600 C for 10 5 hours up to 2 wt.% [5]. However, it was found that for copper contents up to 2 wt.%, after creep rupture at 650 C and 98Mpa load for 5306 hours, fine copper phase precipitated on the lath boundaries and within the lath. These precipitates are thought not to be effective in creep strength. It was proposed that guarantee the high creep strength, copper should be less than 2 wt.% [5]. Copper precipitation in a P122 steel with 0.87 wt% copper was researched by Hattestrand and Andren. They reported a dense distribution of copper precipitates formed during tempering at 770 C [27]. During aging at 600 C or 650 C, copper precipitates coarsened rapidly were believed to be less effective for long-term creep strength than M 23 C 6 and VN precipitates. However, compared to P92 steel a finer distribution of Laves phase was observed in P122 steel. This was believed to be an effect of accelerated nucleation, because of copper precipitates working as suitable nucleation site for Laves phase [27]. 31

52 Vanadium and Niobium Both vanadium and niobium are strong MX carbonitrides forms, and MX carbonitrides are extremely stable against coarsening at elevated temperature (600 C) [3]. The combination effect of vanadium versus niobium on 10 4 h creep rupture strength at 600 C and 650 C in NF616 (P92) with low carbon (0.05 wt.%) was researched by Nippon Steel [33]. As shown in Figure 2.20, the optimized creep strength was obtained when niobium was round 0.05 wt.% and the optimal vanadium content was around from 0.10 wt.% to 0.25 wt.%. A similar result was proposed for 12Cr steels, with optimal contents for vanadium and niobium of approximately 0.05 wt.% and 0.20 wt.% [30]. (a) (b) Figure 2.20: (a) Effect of V and Nb on 10 4 h creep rupture strength of NF616 at 600 C; (2) Effect of V and Nb on 10 4 h creep rupture strength of NF616 at 650 C [30]. 32

53 A recent study about effects of vanadium and niobium on precipitation behavior and mechanical properties of a 0.11C-0.7Mn-10.2Cr-1.2Mo-0.05N-V-Nb was done by Onizawa et al. [34]. Figures 2.21 shows higher strength was obtained with increasing vanadium and niobium contents, but the impact properties degrade with the increase of vanadium and niobium contents. The ductility also degraded with increasing vanadium and niobium contents [34]. As shown in Figure 2.22 and 2.23, the creep rupture time became longer with increasing vanadium and niobium contents, however the effect of niobium tended to be saturated at about 0.01 wt.%. It was observed that fine MX carbonitrides V(C,N) and Nb(C,N) formed in this 10Cr steel, the number of MX carbonitrides increased with the contents of vanadium and niobium. During aging at 873K for 6000 hours MX carbonitrides did not grow and not any new precipitates were observed. Figure 2.21: (a) Influence of V and Nb on tensile properties ultimate tensile strength at 823K; (b) Influence of V and Nb on ductile-brittle transition temperature after aging at 873K for 6000h [34]. 33

54 Figure 2.22: Influence of Nb on creep rupture time at 873K [34]. Figure 2.23: Influence of V on creep rupture time at 873K [34]. 34

55 Cobalt The composition range of cobalt is not defined in ASTM specification for P91, P92 and P122. Cobalt was added to a 12Cr-W steel in order to suppress the formation of δ-ferrite like copper [36]. It was reported that δ-ferrite was entirely eliminated when 1.05 wt.% cobalt was added. I was also found that cobalt improved the creep rupture strength at 650 C for 10 4 hours; however the influence of cobalt was saturated when concentration of cobalt was around 1 wt.% [36][37] Tantalum For nuclear fusion application tantalum was added into Cr steels to replace niobium in order to meet reduced-activation criteria [38] [39]. It was proposed that tantalum increased resistance to irradiation-induced embrittlement of a 9Cr-2WVTa steel, because tantalum in the solution caused either an increase in fracture stress or a change in flow behavior [39]. Tantalum is a MX carbonitride former; it was also found that tantalum entered Z-phase as a minor element [40]. Recent research by Y. Wang et al. found a Ta-alloyed11%Cr steel exhibited a distinctively higher creep strength at 650 C than the advanced 9%Cr steel and piping steel P92 [41]. It was believed this was due to enhanced strengthening by fine precipitates, in particular tantalum containing MX cabonitride precipitates that were found by high resolution TEM. 35

56 Manganese and Silicon Manganese is an austenite stabilizer that could suppress the formation of δ-ferrite and has a significant influence on A 1 transformation temperature as nickel [22]. Manganese and nickel could impair high temperature stability of the ferrite structure by decreasing A 1 transformation temperature. Mi+Ni contents were believed to be a significant factor for the A 1 temperature in steels. It was believed that the steels of higher A 1 temperature may have better creep rupture strength; copper and cobalt also could suppress the formation of δ-ferrite but decrease A 1 temperature less than manganese and nickel [44]. Compared to manganese and nickel, copper and cobalt are better alloying elements. Silicon is a ferrite stabilizer and silicon decreased toughness of steel by promoting Laves phase precipitation. Reduction of both manganese and silicon could improve creep strength of steel [22]. 36

57 2.2 Phase Transformation Analysis Techniques Differential Thermal Analysis (DTA) As shown in Figure 2.24(a), Differential Thermal Analysis (DTA) requires one test sample and one reference sample [48]. These two samples are put in the same environment and undergo identical heat flux. Thermal couples are used to record the instant temperature of samples. During the thermal cycle phase transformation does not take place in the inert reference sample. The heat absorption and emission relative with phase transformation in the testing sample can be detected by comparing the instant temperature records of testing sample and reference sample. As shown in Figure 2.24(b), this is a difference between the instant temperatures of the test sample and the reference sample as a function of the temperature [49]. A local deviation (peak) in the curve represents a phase transformation. The start and the end of this deviation correspond to the beginning and the finish of a phase transformation. (a) (b) Figure 2.24: (a) Schematic of DTA installation [48]; (b) Typical DTA curves of low alloy Cr-Mo steel a- hot rolled and b- as quenched [49]. 37

58 2.2.2 Differential Scanning Calorimetry (DSC) A Differential Scanning Calorimetry operation system is shown in Figure 2.25(a). The test and the reference sample are put on material with high heat conductivity and undergo identical heat flux [50]. Because of high heat conductivity of sample holder, the difference between instant temperatures of test sample and reference sample relative to phase transformation would induce a heat flux between them through sample holder. As shown in Figure 2.25(b), a peak in the heat flux record represents a phase transformation. a) b) Figure 2.25: (a) Schematic of DSC installation; (b) Typical DSC result. [50] 38

59 2.2.3 Dilatometry In 1982, Bhadeshia detected the difference between linear thermal expansion coefficients of ferrite and austenite in steels [51]. He studied three steel samples, two designed alloys Fe-0.39C-2.05Si-4.08Ni and Fe-0.22C-2.03Si-3.0Mo, and one commercial steel 300M. Samples were machined in shape of 3.2mm outer diameter, 1.5mm inner diameter and 20mm length cylinder rod. The linear thermal expansion was detected by a length transducer measuring the diameter change of cylinder rod samples. Thermal expansion of ferrite and austenite were studied at 25 C-600 C and 800 C-990 C respectively. As shown in Figure 2.26, for these three steels linear thermal expansion coefficient of austenite was higher than that of ferrite. Composition could influence the linear thermal expansion coefficient of ferrite and austenite. In 1989, after studied the isothermal transformation of austenite in a Fe-0.3C- 4.08Cr steel at 420 C, Takahashi and Bhadeshia proposed that the relative diameter change was proportional to the volume fraction of ferrite, till at least volume fraction of ferrite arrived 0.7 [52]. Figure 2.26: Linear thermal expansion coefficient of ferrite and austenite [51]. 39

60 The application of dilatometry to study the phase transformations in hypoeutectoid carbon and low alloy steel is defined in ASTM A If there is no phase transformation, relative diameter change (linear strain) should be proportional to temperature during continuous heating or cooling process. If phase transformation happens, because of difference in linear thermal expansion coefficients of different phases, the linear feature of linear strain curve as a function of temperature would be changed. As shown in Figure 2.27, a change in the linearity of the strain vs. temperature curve represents a volume change that is related to a certain phase transformation. Figure 2.27: Typical Dilatometry curve of steel [ASTM A ]. 40

61 2.2.4 Single Sensor Differential Thermal Analysis (SS-DTA) Single Sensor Differential Thermal Analysis (SS-DTA) has been recently developed by Alexandrov and Lippold [53].The principle of SS-DTA is via detecting the latent heat of phase transformation to determine the phase transformation starting and finishing temperatures. Compared to DTA and DSC, SS-DTA does not need a reference sample. SS-DTA uses a reference temperature curve that is calculated based on actual thermal history of the tested material. As shown in Figure 2.28, the tested sample is put in argon atmosphere in a resistive heated convection furnace. Type K thermocouples are used to record the instant sample temperature [54]. In Figure 2.29(a), the blue curve represents the temperature history of the test sample and red curve represents the calculated reference curve. As shown in Figure 2.29(b), the difference between sample and the reference cooling curves is plotted as a function of sample temperature, deviation peak in this curve represents phase transformation; the start and the end of the deviation peak indicates the beginning and finish of corresponding phase transformation [54]. 41

62 Sample Inconel tube Lid Ar out Compression fitting Ar in Ar in Furnace tube Ceramic insulator Gasket Thermocouple Figure 2.28: Schematic of SS-DTA installation [54]. (a) (b) Figure 2.29: SS-DTA result of steel HSLA-65: (a) sample cooling curve and reference curve; (b) difference between sample cooling curve and reference curve as a function of sample temperature [54]. 42

63 2.2.5 The Electrothermomechanical Tester (ETMT) As shown in Figure 2.30, the Electrothermomechanical Tester (ETMT) at Ohio State University was developed by Instron and NPL [55]. ETMT could detect the change in resistance of metal samples relative to phase transformation thus inspect the phase transformation process in metal samples. ETMT heated the sample in size of 40*2*1mm by resistive heating, thermal couple was applied to measure the sample temperature. At the temperature when resistance started to change indicates the beginning of phase transformation in sample. Figure 2.30: Image of ETMT [55] 43

64 2.3 Predicting Methods for Critical Temperatures Predictive Formulae for M s and M f Temperatures The development of predictive formulae for M s temperature in steels is shown in Table 2.2. In 1944 [56], by studying 17 designed steels made in the lab Payson and Savage developed the first formula for M s temperature as a linear function of compositions. At that time the M s temperature was determined by optical microscopy study of quenched and then tempered steel samples [56]. In order to achieve better agreement, Carapella applied multiplying-factor-principle to Payson s formula and developed a formula for M s temperature by multiplying composition values [57]. In 1946 [58], Rowland and Lyle adjusted the coefficient of carbon in Payson s function from to , so the deviation between the calculated value (by Payson s formula) and experimental value of M s temperature in nine commercial steels became smaller. In 1946 [59], by collecting all available published data, Grange and Stewart developed a new predictive formula for M s temperature. The calculated M s temperature were all within 6.7 C of the measured value of 13 commercial low-alloy steels. At the same time, Payson s formula was revised by Nehrenberg, coefficient of C was changed from to -300 [57]. In 1956 [60], Steven and Haynes studied 59 commercial steels and several steel samples made in the lab. The method of least squares was used to analyze measured M s temperatures. Constant of M s formula was adjusted to 561.4, coefficient of C, Cr and Mo also were all modified. Calculated M s temperature by Steven and Haynes s formula had much smaller deviation from measured values in steels with less than 0.25 wt.% carbon. 44

65 In 1965 [1], Andrew studied available data of 184 commercial steels including the 59 from Steven and Hayne s study. Both a linear formula and a product formula were developed. For linear formula, the coefficient of Mo was adjusted to much lower, constant and coefficient of C were also modified. The possible influence of second order terms were taken into account in the product formula. It was found that Cr content was influenced by C interaction especially when Cr was above 2 wt.%. In 1982 [61], C.Y.Kung and J.J.Rayment evaluated the validity of all available predictive formula for M s temperature; it was proposed that cobalt should be included in formula for M s temperature and coefficient of cobalt was 10. The term of Co was added to each formula in Table 2.2. In 1992 [62], by through literature survey, Jicheng Zhao developed predictive formulae for M s of both lath martensite and twinned martensite. Zhao s formula had the largest applicable composition range. In 1994 [63], Beres and Irmer developed a new formula for M s temperature, as shown in Figure The M s temperature was influenced by composition of Mn, Ni, Mo and value of term Ms c and Cr a. The term of Ms c and Cr a were calculated by defined function, and coefficient X, Y and Z varied with composition range as indicated in Figure The applicable composition ranges for all these formulae are shown in Table

66 Author Payson and Savage(1944) Carapella (1944) Rowland and Lyle (1946) Grange and Stewart (1946) Nehrenberg (1946) Steven and Haynes (1956) Andrew (1965) Formulae Ms( C)= C-33.3Mn-27.8Cr-16.7Ni-11.1Si- 11.1Mo-11.1W (+10Co) M s ( C)=496*(1-0.62C)( Mn)(1-0.07Cr)( Ni) ( Si)( Mo)( W) ( Co) M s ( C)= C-33.3Mn-27.8Cr-16.7Ni-11.1Si- 11.1Mo-11.1W (+10Co) M s ( C)= C-38.9(Mn+Cr)-19.4Ni-27.8Mo (+10Co) M s ( C)= C-33.3Mn-22.2Cr-16.7Ni-11.1Si- 11.1Mo (+10Co) M s ( C)= C-33.3Mn-16.7(Cr+Ni)-21.1Mo (+10Co) M s ( C)= C-30.4Mn-17.7Ni-12.1Cr-7.5Mo (+10Co) M s ( C)= C-16.9Ni+15Cr-9.5Mo+217C Mn*C-67.6Cr*C (+10Co) Zhao (1992) M s LM ( C)= C N-24.65Ni+1.36Ni Ni Cr+1.42Cr Mn+2.25Mn Mn Mo Co-0.468Co Co Cu-17.66Ru M s TM ( C)= C-72.65N-43.46N Ni Ni Ni Cr Mn+1.296Mn Mn Mo+12.86Co Co Co Cu-16.28Ru+1.72Ru Ru 3 Table 2.2: Development of M s temperature predictive formulae [61] 46

67 Ms( C)=Ms c -YMn-XNi-ZCr a -7.5Mo Ms c ( C)= C+4.2/C 0.03 C% 0.35 Ms c ( C)= C+40/C 0.35<C% 1.3 Ms c ( C)= C% 2.3 Cr a =Cr+1.5Si+W+V+Al+0.5Nb+0.5Ta+2Ti Group 1 2 Content of elements (wt.%) Coefficients C, Cr,Si Ni X Y Z 0.03 C<0.5 and (Cr+1.5Si)> C<2.3 and (Cr+1.5Si) 6 or 0.5 C<2.3 and (Cr+1.5Si)>6 > ~5 1.6*Ni+65/Ni < > Mn 1.75 Mn< ~5 1.2*Ni+48.7/Ni < Figure 2.31: Formula of M s developed by Beres and Irmer(1994) [63]. Steven and Haynes measured the amount of martensite formed when sample was quenched to designed temperatures [60]. It was reported that the first 70% martensite formed rapidly when quench temperature decreasing from M s ; however at lower quench temperatures formation of martensite became much slower. They found the following relationships (M x means x% martensite formed): M 10 ( C) = M s -10 ±3 M 50 ( C) = M s -47 ±9 M 90 ( C) = M s -103 ±12 M f ( C) = M s -215 ±15 47

68 Payson and Savage (1944) Rowland and Lyle (1946) Grange and Stewart (1946) Nehrenberg (1946) Steven and Haynes (1956) Si < Cr Ni Mo Andrew (1965) C <1.0 <1.0 < Mn < Zhao (1992) Beres and Irmer (1994) < < All range < <3.50 <4.610 <10 All range < <5.0 <5.040 <34 All range < <1.00 <5.400 <2.5 - W N <3 - Co <60 - Cu <7 - Ru <20 - Table 2.3: Applicable composition range of formulae for M s (wt.%) [1][56]~[63]. 48

69 2.3.2 Predictive Formulae for B s and B f Temperatures Available formulae of B s and B f temperatures were shown in Table 2.4. The first formula for B s temperature was developed by Steven and Haynes; they also developed formulae for B 50 and B f [60]. In 1992 [62], via through literature survey, Jicheng Zhao developed a formula for B s temperature. The applicable composition ranges for these two formulae are shown in Table 2.5. In 2000 [64], Z. Zhao et al. developed a formula for B s temperature; carbon was eliminated from this formula. It was reported that for 84 low alloy steels this formula achieved better agreement than Steven and Haynes formula. It was also proposed by Z. Zhao et al. that B s temperature was approximately C lower than B 0 (the theoretical upper temperature limit) temperature. B 0 was derived from the intersection of thermodynamic equilibrium curves of austenite to ferrite transformation and austenite to cementite transformation. 49

70 Author Formulae Steven and Haynes (1956) B s ( C)= C-90Mn-37Ni-70Cr-83Mo B 50 ( C)= Bs-60 B f ( C)= Bs-120 Zhao(1991) B s ( C)= C C Ni+6.06Ni Ni Cr+2.17Cr Mn+7.82Mn Mn Mo+9.16Co Co Co Cu-46.15Ru Z.Zhao et B s ( C)=630-45Mn-45V-35Si-30Cr-25Mo-20Ni-15W al.(2000) Table 2.4: Formulae for B s and B f [60] [62] [64]. Steven and Haynes (1956) Zhao (1991) Z.Zhao et al. (2000) C Mn Si Ni Cr Mo N Cu Co Ru <5.0 <3.5 < <0.8 <10 - <20 <10 <2.5 <0.6 <7 <20 <5 For commercial low alloy steels Table 2.5: Applicable composition range of formulae for B s and B f (wt.%) [60] [62] [64]. 50

71 2.3.3 Predictive Formulae for Ac 1 and Ac 3 Temperatures As shown in Table 2.6, the only available predictive formulae for Ac 1 and Ac 3 temperatures in steels were developed by Andrew in 1965 [1]. Andrew collected available data of 196 Ac 1 temperatures and 155 Ac 3 temperatures. The applicable composition ranges for these two formulae were shown in Table 2.7. Formulae Ac 1 Ac 3 Ac 1 ( C)= Mn- 16.9Ni+29.1Si+16.9Cr+290As+6.38W Ac 3 ( C)= C- 15.2Ni+44.7Si+104V+31.5Mo+13.1W Table 2.6: Formulae for Ac 1 and Ac 3 [1]. Ac Ac C Mn Si Ni Cr Mo N V W Table 2.7: Applicable composition range of formulae for Ac 1 and Ac 3 (wt.%) [1]. 51

72 Chapter 3 OBJECTIVES The objective of this study was to develop predictive formulae for the A 1 temperature in P91, P92 and P122 steels as a function of steel composition. The research steps were designed as follows: 1. Screen out the alloying elements with a significant effect on the A 1 temperature in P91 steel using a model based DOE. The JMP8 software was utilized to design the modeling experiments and the thermodynamic software JMat-Pro was used to predict the A 1 temperature. 2. Develop a predictive formula for the A 1 temperature in P91 steel as a function of the content of metallic alloying elements using a measurement based DOE. The JMP8 software was utilized to design the experiments and the SS DTA technique was used to measure the A 1 temperature in laboratory heats of P91 steel. 3. Study the effect of C, Si, and N on the A 1 temperature in P91 steel. 4. Study the effect of Mo and W on the A 1 temperature in P92 steel. 5. Study the effect of Cu on the A 1 temperature in P122 steel. 6. Develop the predictive formulae for the A 1 temperature in P91 and P92 steels. 7. Validation of the formulae for predicting the A 1 temperature in P91 and P92 steels. 52

73 Chapter 4 MATERIALS AND EXPERIMENTAL PROCEDURES 4.1 Design of Experiments Model Based DOE The chemical compositions of P91, P92 and P122 steels specified by ASTM A335 are shown in Table 4.1. In order to screen out alloying elements with significant effect on predicted A 1 temperature in P91 steel, DOE software JMP8 was used to fulfill effect screening. Samples with various compositions inside the ASTM specification range were designed using JMP8, and their A 1 temperatures were calculated by the thermodynamic simulation software JMat-Pro. By analyzing the data, the alloying elements with significant influence on the predicted A 1 temperature in P91 steel were determined. The results of the JMat-Pro based DOE were used to select the significant elements for further experimental study. The structure of the model based DOE used in this study is shown in Figure 4.1 The JMP software is designed by SAS Institute to perform statistical analysis. JMP8 could also be used to design experiments. As shown in Figure 4.2, at the JMP8 starter menu, DOE was chosen to start experimental design. It was assumed that the alloying elements have quadratic relation with the predicted A 1 temperature. Custom Design was chosen so that both linear and quadratic terms could be defined. As shown in Figure 4.3, at first, the response of model based DOE was ready to be defined. Here the 53

74 response is JMat-Pro predicted A 1 temperature and it was defined as A1 in JMP8. More than one response could be defined for one experimental deign. As shown in Figure 4.4, factors for model based DOE were designed after response was fixed. Here the factors are the contents of alloying elements in P91 steel. Because composition is numeric data, composition of each alloying element was defined as continuous factor. The composition range was also defined according to ASTM specification for P91 steels as shown in Table 4.1. All of the alloying elements were defined as factors except for P, S and Al. The effect of these elements on predicted A 1 temperature is negligible because their concentrations are extremely low. Figure 4.1: Structure of the model based DOE used in this study. 54

75 Figure 4.2: JMP8 starter menu and DOE features. Figure 4.3: Defining the response for model based DOE in JMP8. 55

76 Figure 4.4: Defining the factors for model based DOE in JMP8. In order to find out all possible relations between the steel composition and the JMat-Pro predicted A 1 temperature, linear term, quadratic term and second order interaction term were all defined for model based DOE model, as shown in Figure 4.5. Figure 4.6 shows how to define number of center points and replicates for model based DOE. Because the response of this experimental design is software JMat-Pro calculated A 1 temperature, there would be no deviation between replicated runs with the same composition. This is why no replicates and one center point were defined. Under the Custom Design of DOE features in JMP8, response, factors, model, number of center points and replicates were defined for model based DOE step by step. As shown in Table 4.2 and 4.3, the model based DOE table was designed using JMP8. There were totally 65 runs, which means 65 P91 steels with differnent composition were designed using JMP8. The A 1 temperature in all 65 compositions of P91 steel were predicted using JMat-Pro and were included in the response column for A 1, as shown in Tables 4.2 and

77 Figure 4.5: Defining the model effects for model based DOE in JMP8. 57

78 Figure 4.6: Defining the number of center points and replicates for model based DOE in JMP8. Element P91 P92 P122 C Mn Si <0.50 <0.50 S <0.010 <0.010 <0.010 P <0.020 <0.020 <0.020 Cr Ni <0.40 <0.40 <0.50 Cu Mo W V Nb N B ppm <0.005 Al <0.040 <0.040 <0.040 Table 4.1: ASTM A335 specification for P91, P92 and P122 steels (wt.%). 58

79 Run C Mn Si Cr Ni Mo V Nb N A 1 / C Table 4.2: The first part of model based DOE table with JMat-Pro predicted A 1 temperature values (wt.%). 59

80 Run C Mn Si Cr Ni Mo V Nb N A 1 / C Table 4.3: The second part of model based DOE table with JMat-Pro predicted A 1 temperature values (wt.%). 60

81 With the JMat-Pro prodicted A 1 temperatures, the model based DOE data were ready to be analyzed using JMP8. As shown in Figure 4.7, a model was tailored for model based DOE data in the model fitting platform. A1 was defined as the role variable of the model. The model effects were the same as we defined in Figure 4.5, including all linear terms, quadratic terms and second order interaction terms. The JMat-Pro predicted A 1 temperature data was modeled by a standard least square approach. Effects screening was defined as model fitting emphasis, thus factors with significant effects will be showed in a scaled parameter report with a graph and the prediction profiler. Figure 4.7: The model fitting platform for model based DOE in JMP8. 61

82 4.1.2 Measurement based DOE and separate experimental study The alloying elements with significant influence on JMat-Pro predicted A 1 temperatures in P91 steel were screened out using the JMP8 software. These significant elements were selected as factors in our measurement based DOE. Because we could not control the concentration of C, N and Si as accurate as of the metallic elements, only metallic elements with significant effect on predicted A 1 temperatures were defined as the factors of DOE model in this study. Additionally, we investigated the influence of C, N and Si on A 1 temperatures in P91 steel, the effect of Mo and W on A 1 temperatures in P92 steel and the effect of Cu on A 1 temperatures in P122 steel in separate experimental studies. Based on the experimental DOE study and these separate experimental studies we could develop the formulae including comprehensive factors for predicting A 1 temperature in P91 and P92 steels. 62

83 4.2 Sample Preparation Materials Test samples of 25g of various alloy compositions were made by tungsten arc button melting in argon environment. The test samples were made by adding alloying elements to particular commercial heats of P91, P92 and P122 steels, which were chosen as base materials. Carbon was added to the test samples in form of 1080 steel wire. The other elements were added in form of pure elements: manganese pieces, silicon granules, chromium pieces, nickel wire, copper wire, molybdenum wire, tungsten wire, vanadium wire and iron granules, their purities are shown in Table 4.4. The compositions of specified P91, P92 and P122 steels base materials and 1080 steel wire are shown in Table 4.5. Element Cr Mn Mo Ni Si V Cu W Fe Purity 99% 99.9% % % 99.8% 99.9% 99.95% 99.98% Table 4.4: Purity of pure elements. C N Cr Mn Mo Ni Si V P S Cu W P P P Table 4.5: Composition of base materials and 1080 steel wire (wt.%). 63

84 4.2.2 Sample Preparation In order to simulate the manufacturing process of commercial P91, P92 and P122 steels, the procedures of sample melting and heat treatments were designed as follows: 1. Based on sample size of 25g, the weights of the base metal (P91 or P92 steel) and of the pure elements needed to produce a sample with particular composition were calculated. 2. The base metal and pure elements needed for each particular test sample were weighted by an electronic balance (Mettler Toledo AB135-S), within 0.001g of target weight. 3. The charge of each test sample (base metal and pure elements) was melted by a tungsten arc button melting system in argon environment as shown in Figure 4.8(a). The charge was melted into a button sample over a copper hearth. The melting arc current was 300A. To achieve homogeneous distribution of alloying elements each button sample was flipped over and re-melted for two more times. 4. In the next step, the button sample was re-melted over an open copper hearth and cast into a copper mold to produce a test sample with cubic shape, Figure 4.8(b). The copper mold and the sample are shown in Figure 4.8(c) and Figure 4.8(d) correspondingly. The casting was performed utilizing the same gas-tungsten arc melting system in argon environment. The gas-tungsten arc current was 300A. 5. The test samples were homogenized at 1050 C for 4 hours, and then cooled in the furnace to room temperature. 64

85 6. The homogenized samples were hot-rolled at 1000 C for four times and the sample thickness was reduced 10% each time. After each hot-rolling step the samples were reheated in the furnace to the rolling temperature. The total thickness reduction was 34.4%. The sample shape after hot rolling is shown in Figure 4.8(e). 7. The hot-rolled samples were normalized at 1050 C for 0.5 hour. The samples were heated with rate of 600 C/h and then cooled in the air to room temperature. The thermal history of the normalizing process is shown in Figure 4.8(f). 8. As shown in Figure 4.8(g), the normalized samples were tempered at 750 C for 2 hours. Both the heating and the cooling rates were 200 C/h. 65

86 a) b) c) d) e) f) f) g) Figure 4.8: Schematics of sample melting, casting and heat treatment: a) Tungsten arc button melting system; b) Sample casting system; c) Cubic copper casting mold; d) Shape of sample after casting; e) Shape of sample after hot-rolling; f); Heat history of normalizing; g) Heat history of tempering. 66

87 4.3 Evaluation of Composition Control The chemical analyses of heat treated samples have been performed by NSL Analytical. The chemical analysis of elements except for nitrogen was performed by LECO Glow Discharge Spectrometer, and the concentration of nitrogen was measured by LECO Furnace. As shown in Table 4.6, the chemical analysis results showed that carbon, nitrogen and silicon were lost during the melting process, and even more during heat treatment process. The loss of the metallic elements was negligible. Energy Dispersive X-ray Spectroscopy (EDX) map analysis was performed by SEM (Quanta 200). The magnification was 600, resolution was 128*128, and frame was 64. As shown in Figure 4.9, the EDX map analysis showed that all added metallic elements were uniformly distributed inside the heat treated sample. 67

88 Cu Mo V Cr W Si Figure 4.9: EDX map analysis result of heat treated samples. Element Sample 1 Sample 1 melted Sample 2 Sample 2 melted and heat treated C Mn Si Cr Ni Cu Mo W V N Table 4.6: Compositions of melted and heat treated samples (wt.%). 68

89 4.4 Determination of the A 1 temperature The critical temperatures were determined by the SS-DTA technique as shown in Figure 2.28 [54]. The samples were heated at 600 C/h from 20 C to 650 C, and then at 28 C/h from 650 C to 950 C (in accordance with ASTM A ) in argon atmosphere in a resistive heated convection furnace. Special Limit of Error type K thermocouples (0.4% measurement error) were used for the temperature measurements. The heating rate of the furnace is controlled by separate thermocouple of the furnace control system, which is different from the thermocouple for sample measurement. The SS-DTA technique was used to determine the thermal effect of reversion of martensite to austenite and its starting and finishing temperatures. From 650 C to 950 C, the furnace heating rate was 28 C/h. The reference heating curve which is free of phase transformation thermal effects should be a straight line with gradient of 28 C/h. The digitally acquired heating history of sample was compared with the reference heating curve free of phase transformation, and the temperature difference (δt) was plotted as a function of temperature (T). There are two peaks in the plot of δt shown in Figure The first one corresponds to the ferro-magnetic to paramagnetic transformation (Curie temperature A 2 ). The second peak indicates the austenitization process; the start and the end of the deviation peak indicate the beginning and finish of austenitization (correspondingly the A 1 and A 3 temperatures). 69

90 950 A(bar)-2 28C-hr 900 A 3 Temp (deg.c) X: Y: X: Y: A A δt (deg.c) Figure 4.10: A 2, A 1, A 3 temperatures in laboratory melted heat of P91 steel determined by SS-DTA. 70

91 4.5 Validation of formula predicted A 1 temperature In order to validate the formula predicted A 1 temperature, two lab made P91 steel specimens were chosen to be normalized at 1050 C for 30min and tempered at both 10 C below and 15 C above the formula predicted A 1 temperature for 30min and then air cooled to room temperature. Hardness testing and microscopy study were performed on these tempered specimens. If the specimen was tempered above the A 1 temperature, there should be austenite formed during the tempering process, after air cooling the austenite would transform to martensite. So the specimen tempered above the A 1 temperature was supposed to have higher hardness, and both tempered martensite and fresh martensite in the microstructure. The specimen tempered below the A 1 temperature was supposed to have full tempered martensite in the microstructure. The composition of these two specimens is shown in Table 4.7. Element Specimen1 Specimen2 C Mn Si Cr Ni Cu Mo V N Table 4.7: Composition and SS-DTA measured A 1 temperature of P91 steel specimens (wt.%). 71

92 Chapter 5 RESULTS AND DISCUSSION 5.1 Model Based DOE The elements with significant influence on JMat-Pro predicted A 1 temperature in P91 steel were screened out. Least square fitting model of A 1 temperature in P91 steel as a function of alloying compositions was developed using JMP8. As shown in Figure 5.1, there were almost no deviations between the JMat-Pro predicted A 1 temperatures and model predicted A 1 temperatures. The R-square value of shows that the reliability of this least square fitting model is 99.9%. R-square is calculated by diving regression sum of squares with total sum of squares, it predicts how well model predict value relate to actual value. As shown in Figure 5.2, the least square fitting model showed the alloying elements with significant influence on JMat-Pro predicted A 1 temperature in P91 steel are Ni, Mn, Si, N, Cr, Mo, C, V. Ni and Mn had the most significant effects. As shown in Figure 5.3, all significant alloying elements have linear relation with JMat-Pro predicted A 1 temperature in P91 steel. The ferrite stabilizing elements Cr, Mo, Si, V increased JMat-Pro predicted A 1 temperature in P91 steel; and the austenite stabilizing elements Ni, Mn, C, N decreased JMat-Pro predicted A 1 temperature. The influence of Mo and V on A 1 temperature in P91 steel were very small, and the effect of Nb on A 1 temperature was negligible. As shown in Figure 5.4, all of the influence curves of certain element to the predicted A 1 temperature were parallel to their corresponding original 72

93 curves, which meant that the changing of one element did not influence the effect of another element on the predicted A 1 temperature in P91 steel. It was conclude that there were no interactions between the significant alloying elements, so interactions were not taken into account for our further experimental study. What should be mentioned is that the all the relations between the predicted A 1 temperature and the composition was found based on the composition range defined according to ASTM standard for P91 steel. These relations are not supposed to be applicable to general steels with different composition range. Figure 5.1: The JMat-Pro predicted A 1 temperature versus A 1 temperature predicted by the model based DOE. 73

94 Figure 5.2: Elements with significant influence on the JMat-Pro predicted A 1 temperature in P91 steel and their estimated effects. Figure 5.3: Prediction profiler of model based DOE model of A 1 temperature in P91 steel. 74

95 Figure 5.4: Interaction profiler of model based DOE model of A 1 temperature in P91 steel. 75

96 5.2 Experimental Results Measurement based DoE for P91 Steel The results of the model based DoE study show that Ni, Mn, Si, N, Cr, Mo, C and V have significant influence on A 1 temperature in P91 steel. The concentration of C, N and Si in the test samples could not be controlled as accurately as for the metallic elements. For this reason only Ni, Mn, Cr, Mo, V and Cu were chosen as the factors of measurement based DOE model. The composition range of experiment based DOE for P91 steel is shown in Table 5.1. The original set point of Si, C and N for all samples were selected correspondingly as 0.22 wt.%, 0.15 wt.% and wt.%, assuming a loss to almost same extent for these elements during sample preparation process. Chemical analysis result in Table 5.2 shows Si, C and N became around 0.18 wt.%, wt.% and wt.% respectively after melting and heat treatment. It was found that commercial P91 steels contain up to around 0.2 wt.% copper, although copper is not included in the ASTM specification. The heat of P91 steel used as base material contained 0.14 wt.% Cu and during sample preparation Cu was diluted farthest to 0.12 wt.%. It was assumed that the effect of Cu on A 1 temperature is negligible at copper concentration lower than 0.12 wt.% Cu. The measurement based DOE was developed using JMP8. As shown in Figure 5.5, experimentally measured A 1 temperature was defined as response for measurement based DOE, designated as A1 in JMP8. Based on the analysis above, according to model based DOE study results and experimental feasibility, the factors for measurement based DOE were defined as shown in Figure 5.6. As shown in Figure 5.7, in order to achieve a model with high accuracy both linear and quadratic 76

97 terms were defined for the experimental model. Based on JMat-Pro based DOE study results, interaction terms were not taken into account. Two center points and one replicate were defined, as shown in Figure 5.8. A measurement based DOE table was generated Figure 5.9 using JMP8 that included a series of thirty six samples with different compositions inside the ASTM specification range. Thirty six alloy samples were made by button melting and then subjected to the homogenization, rolling and heat treatment procedures described in Section The A 1 temperatures of these thirty six samples measured using SS-DTA were entered in the response column for A 1 in the Figure 5.9. The testing error of type K thermocouple is ± 0.4%. However, specific data of the A 1 temperatures was need for the JMP8 to develop the predictive formula, so the testing error of type K thermocouple goes into the error of the predictive formula. Figure 5.5: Defining the response for measurement based DOE in JMP8. 77

98 Figure 5.6: Defining the factors for measurement based DOE in JMP8. Figure 5.7: Defining the model for measurement based DOE in JMP8. 78

99 Figure 5.8: Defining the number of center points and replicates for measurement based DOE in JMP8. Element Specification DOE C Cr Cu Mn Mo N Ni < Si V Table 5.1: Composition range of measurement based DoE for P91 steels (wt.%). 79

100 Figure 5.9: Measurement based DOE table with measured A 1 temperature values (testing error of type K thermocouple is ± 0.4%). 80

101 Based on the experimental DOE table with measured A 1 temperature values, statistical analysis was performed using JMP8. Least square fitting model of A 1 temperature in P91 steel as a function of alloying compositions was developed using JMP8, as shown in Figure As shown in Figure 5.11, the R-square value of this model is 0.843, which means the reliability of this least square fitting model is 84.3%. As shown in Figure 5.12, compositions of Ni, Mn, Cr, Mo, V and Cu have significant effects on A 1 temperature in P91 steel. For Ni, Cu and V, influence of both linear term and quadratic term were found to be significant. Linear terms of Mo and Cr, and quadratic term of Mn were believed to be insignificant terms, because their P > t value were too high. Probabilities around or less than 0.05 are often considered as significant evidence that the corresponding term is significant. As shown in Figure 5.13, Mn had almost linear effect on A 1 temperature in P91 steel, the other alloying elements had quadratic effects on A 1 temperature in P91 steel, specially the carbide formers Cr, Mo, V. 81

102 Figure 5.10: Predictive model of the A 1 temperature in P91 steel as a function of composition of Ni, Mn, Cr, Mo, V and Cu developed using JMP8 by measurement based DOE study 82

103 Figure 5.11: The actual (measured) A 1 temperature versus the experiment based DOE model predicted A 1 temperature Figure 5.12: Elements with significant influence on measured A 1 temperature in P91 steel and their estimate effects. 83

104 Figure 5.13: Prediction profiler of measurement based DOE model of A 1 temperature in P91 steel. A least square fitting model of the A 1 temperature in P91 steel as a function of compositions of metallic elements was developed, Equation (1).This model was based on calculated compositions of test samples. Linear terms of Mo and Cr, and quadratic term of Mn were deleted because they were believed to be insignificant terms. A1 ( C) = *((Mn-0.452)/0.148) *((Cr-8.75)/0.75) *((Ni )/0.146) *((Ni )/0.146) *((Cu )/0.1415) *((Cu )/0.1415) *((Mo )/0.1375) *((Mo )/0.1375) *((V- 0.2)/0.05) *((V-0.2)/0.05) (1) The results of chemical analysis performed by Materials Solution, Inc. (MSI) on 10 of the 65 measurement based DOE samples are compared to calculated composition of these samples in Table 5.2. The average and standard deviation of deviations between measured composition and calculated composition were calculated. 84

105 C Mn Si Cr Ni Cu Mo V N Measured Calculated Measured Calculated Measured Calculated Measured Calculated Measured Calculated Measured Calculated Measured Calculated Measured Calculated Measured Calculated Measured Calculated Average of Deviations STD* of Deviations Table 5.2: Chemical analysis of measurement based DOE samples (wt.%). *STD Standard Deviation 85

106 Average and standard deviation values of concentration loss show that C, N and Si were lost to almost same extent as we assumed. It was found that loss of all metallic elements except for Cr was negligible. Cr was lost around 0.6 wt.%, standard deviation of loss of Cr was 0.1 wt.%. Around 0.6 wt.% Cr was lost in all measurement based DOE samples. The Cr term 9.38*((Cr-8.75)/0.75) 2 of calculated composition based Equation (1) was adjusted to 9.38*((Cr )/0.75) 2, as shown in Equation (2). Equation (2) was based on measured composition of test samples; the application range of this model is shown in Table 5.3. A1 ( C) = *((Mn-0.452)/0.148) *((Cr-8.15)/0.75) *((Ni )/0.146) *((Ni )/0.146) *((Cu )/0.1415) *((Cu )/0.1415) *((Mo )/0.1375) *((Mo )/0.1375) *((V- 0.2)/0.05) *((V-0.2)/0.05) (2) Element Specification Application Range C Cr Cu Mn Mo N Ni < Si V Table 5.3: Application range of Equation (2) (wt.%). 86

107 5.2.2 Determination of the effect of carbon and sillicon on the A 1 temperature in P91 Steel Effects of C and Si in P91 steel on A 1 temperature were studied separately, because C and Si were lost during the sample melting and heat treatment process, so the concentration of C and Si could not be controlled as exact as metallic elements. The composition of P91 steel used as base material is shown in Table 4.5. C was added into samples in form of 1080 carbon wire, the composition of 1080 carbon wire is also shown in Table 4.5. Six samples with various C compositions between 0.01~0.09 wt.% were made by button melting system. Chemical analysis of these six samples was performed by MSI, as shown in Table 5.4. The standard deviation values of compositions showed that the loss of other elements was negligible. The plot of SS-DTA detected A 1 temperatures to C concentration of corresponding samples (after melting and heat treatment) is shown in Figure The concentration of carbon influences A 1 temperature in a linear way. A linear fitting model was developed using JMP8, as shown in Figure The R-square value of this linear model is 0.926, which means the reliability of this model is 92.6%, and the coefficient of C was Eight samples with various Si compositions between 0.20~0.50 wt.% were produced by adding pure Si granules into P91 base material. The result of chemical analysis by MSI is shown in Table 5.5. The standard deviation values of compositions showed that the loss of other elements was negligible. As shown in Figure 5.14, a linear relation was found between the concentration of Si and A 1 temperature in P91 steel. A 87

108 linear model was developed using JMP8. The R-square value of the linear model is 0.930, and the coefficient of Si is 87.5, as shown in Figure Based on separate experimental study of C and Si for P91 steel, two linear models of A 1 temperature in P91 steel as a function of concentration of C (3) and Si (4) were develop using JMP8: A1 ( C) = *C (3) A1 ( C) = *Si (4) Figure 5.14: Influence of C and Si on the A 1 temperature in P91 steel. 88

109 Sample C1-1 C1-2 C2-1 C2-2 C3-1 C3-2 STD* C Cr V Mn Mo N Ni Si Table 5.4: Measured composition of P91 steel samples with varied C concentration (wt.%). *STD Standard Deviation Sample P91-1 P92-2 Si3-1 Si3-2 Si2-1 Si2-2 Si1-1 Si1-2 STD* C Cr Cu Mn Mo N Ni Si V Table 5.5: Measured composition of P91 steel samples with varied Si concentration (wt.%). *STD Standard Deviation 89

110 5.2.3 Determination of the effect of nitrogen on the A 1 temperature in P91 Steel Losing of N during melting and heat treatment process could not be controlled and N could not be added into samples in our lab. The effect of N on A 1 temperature in P91 steel was determined by comparing the SS-DTA detected A 1 temperature of melted and heat treated P91 steel samples (P91-1 and P91-2) to the original commercial heat of P91 steel. The chemical analysis by MSI (Table 5.6) showed that certain amount of C, N, Si and small amount of metallic elements were lost during the melting and heat treatment process. The SS-DTA detected A 1 temperatures of these 3 samples are also shown in Table 5.6. By combining models (2) (3) (4), the effects of the other elements on A 1 temperature except for N could be calculated. The effect of N was determined as the difference in A 1 temperatures minus the effect of the other alloying elements. The A 1 temperature was assumed to a have linear relation with the concentration of N. The calculated coefficient for N is around Sample P91-1 P91-2 P91 C Cr Cu Mn Mo N Ni Si A 1 / C Table 5.6: Composition of commercial and laboratory heats of P91 steel samples used for determination of the effect of nitrogen on the A 1 temperature (wt.%). 90

111 5.2.3 Determination of the effect of molybdenum and tungsten on the A 1 temperature in P92 Steel From the specification of P91 and P92 steels, the composition range of all alloying elements in both steels is the same, except for Mo and W. The specification for Mo is wt. % in P92 steel and wt. % in P91 steel, and there is no W in P91 steel. The effects of Mo and W on A 1 temperature in P92 steel were studied separately. The composition of P92 steel used as base material is shown in Table 4.5, Mo and W were added into P92 steel in form of pure wire. Eight P92 steel samples with various W concentrations have been produced. The chemical analysis of these samples was performed by MSI. The standard deviation values of the composition showed that the loss of other elements was negligible, Table 5.7. The A 1 temperatures in these samples were determined by SS-DTA and plotted as a function of concentration of W in Figure A quadratic fitting model was developed using JMP8, as shown in Figure The R-square value of this model is 0.878, which means the reliability of this model is 87.8%. Six samples with various Mo compositions between wt.% were produced. The chemical analysis in these samples was performed by MSI, The standard deviation values of the composition showed that the loss of other elements was negligible, Table 5.8. The concentration of Mo had linear relation with A 1 temperature in P92 steel, as shown in Figure A linear fitting model was developed using JMP8. The R-square value of this linear model is 0.900, and the coefficient of Mo was

112 Based on separate experimental study of W and Mo for P92 steel, a quadratic model of A 1 temperature in P92 steel as a function of concentration of W (5), and a linear models of A 1 temperature in P92 steel as a function of concentration of Mo (6) were developed using JMP8: A1 ( C) = *W *(W ) 2 (5) A1 ( C) = *Mo (6) Figure 5.15: Influence of W and Mo on the A 1 temperature in P92 steel. 92

113 Element P92-1 P92-2 W1-1 W1-2 W2-1 W2-2 W3-1 W3-2 STD* C Cr W Mn Mo N Ni Si V Table 5.7: Measured composition of P92 steel samples with varied W concentration (wt.%). *STD Standard Deviation Sample P92-1 P92-2 Mo1-1 Mo1-2 Mo2-1 Mo2-2 STD* C Cr V Mn Mo N Ni Si Table 5.8: Measured composition of P92 steel samples with varied Mo concentration. *STD Standard Deviation 93

114 5.2.4 Determination of the effect of copper on the A 1 temperature in P122 Steel As shown in Table 4.1, the specification for Cu in P122 steel is wt.%. P122 was the first Cr steel designed with high content of Cu. The influence of Cu on A 1 temperature in P122 steel could be significant because of large composition range. Ten P122 steel samples with various Cu compositions between 0.04~1.73 wt.% were made by button melting system. The chemical analysis of these ten samples was performed by MSI. As shown in Table 5.9, the standard deviation values of the composition show the loss of alloying elements was negligible. The plot of SS-DTA detected A 1 temperatures to Cu concentration of corresponding samples is shown in Figure At the range of 0.04~1.73 wt.%, Cu reduced the A 1 temperature from around 835 C to 815 C. The concentration of Cu influences the A 1 temperature in a linear way. A linear fitting model for the influence of Cu on the A1 temperature in P122 steel was developed using JMP8, Figure The R-square value of this linear model is 0.950, which means the reliability of this model is 95.0%, and the coefficient of Cu was The specification for Cr in P122 steel is wt.%, as shown in Table 4.1. The heat of P122 steel used as base material contained wt.% Cr. Since Cr in this heat of P91 steel could only be diluted farthest to around 11 wt.%, test samples with varying Cr compositions between 10 to 11 wt.% could not be made. A heat of P122 steel with around 10 wt.% Cr needs to be collected in the future for determining the effect of Cr on the A 1 temperature in P122 steel. 94

115 Figure 5.16: Influence of Cu on the A 1 temperature in P122 steel. Sample C Mn Si Cr Ni Cu Mo W V P P Cu Cu Cu Cu Cu Cu Cu Cu STD* Table 5.9: Measured composition of P122 steel samples with varied Cu concentration. *STD Standard Deviation 95

116 5.3 Measurement based DOE formulae for the A 1 Temperatures in CSEF Steels Predictive formula for the A 1 temperature in P91 Steel Combining equations (2), (3), (4) and the coefficient of N derived in section 5.2.3, a formula for predicting the A 1 temperature in P91 steel that contain wt.% Cu was developed: A1 ( C) = *C - 174*N *Si *Mn *Cr *Cr *Ni *Ni + 164*Mo *Mo *V 2-570*V Cu *Cu (7) For P91 steel containing less than 0.12 wt.% Cu, the influence of Cu on A 1 temperature in P91 steel was assumed to be negligible. The proposed formula for predicting A 1 temperature in P91 steel with less than 0.12% Cu is: A1 ( C) = *C - 174*N *Si *Mn *Cr *Cr *Ni *Ni + 164*Mo *Mo *V 2-570*V (8) A 1 temperatures of ten commercial P91 steels determined by dilatometry measurements were collected to validate the proposed formula of the A 1 temperature in P91 steel. The composition of these ten commercial P91 steels and corresponding measured (by dilatometry) and formula predicted A 1 temperature are shown in Table The deviations of predicted from measured A 1 temperatures are shown in Figure 96

117 5.17. The deviation range is between -3 C and 3 C. The concentration of Mn and Ni is also shown in this diagram. The Mi+Ni content was believed to be a determining factor for the A 1 temperature in P91 and P92 steels. Steels containing higher combined amounts of Ni and Mn are expected to have lower A 1 temperature. The results of our study have shown that the Mi+Ni content is not the only determining factor for A 1 temperature in P91 steel, and that the other alloying elements also have significant influence Dilatometry Predictive Formula Ni+Mn Figure 5.17: Comparison of A 1 temperature in P91 steels predicted by measurement based DOE formula and A 1 temperature determined by dilatometry. 97

118 C N Mn Si Cr Ni Cu Mo V Tested Predict Dev A 1 / C A 1 / C Max Min Table 5.10: Composition (wt.%) of 10 commercial P91 steels and corresponding measured (by dilatometry) and formula predicted A 1 temperature. The predicted A 1 temperatures for the ten P91 steel samples by developed predictive formula, by JMatPro and by the expression developed by Santella [65] are shown in Table The data are plotted in Figure 5.18; our predictive formula has the closest agreement with measured values by dilatometry. The reasons for the significant deviation between the compared predictive methods require further clarification. Validation by comparison to a larger database of experimentally determined values of the A 1 temperature in P91 steel is also suggested. 98

119 Sample Dilatometry Predictive Formula Santella's Expression Jmat- Pro Table 5.11: Predictions for P91 steel samples from different available models ( C). 840 Unit: C Predictive Formula Santella's Expression Jmat-Pro equal line (Dilatometry 825 Measurement/ C) Figure 5.18: Predictions for P91 steel samples from different available models. 99

120 5.3.2 Predictive formula for the A 1 temperature in P92 Steel Combining equations (2), (5), (6) and the coefficient of N derived in section 5.2.3, a formula for predicting A 1 temperature in P92 steel that contain wt.% Cu was developed: A1 ( C) = *C - 174*N *Si *Mn *Cr *Cr *Ni *Ni + 38*Mo *V 2-570*V *Cu *Cu *W *W (9) For P92 steel containing less than 0.12 wt.% Cu, the influence of Cu on A 1 temperature in P92 steel was assumed to be negligible. The proposed formula for predicting A 1 temperature in P92 steel with less than 0.12% Cu is: A1 ( C) = *C - 174*N *Si *Mn *Cr *Cr *Ni *Ni + 38*Mo *V 2-570*V *W *W (10) The A 1 temperatures measured by dilatometry in nineteen commercial P92 steels are summarized in Figure The composition of these nineteen commercial P92 steels and corresponding measured (by dilatometry) and formula predicted A 1 temperature are shown in Table The deviations range of predicted from measured A 1 temperature is 100

121 between -11 C and 11 C. As in steel P91, the Mi+Ni content was not the only determining factor for A 1 temperature in P92 steel Dilatometry Predictive Formula Ni+Mn Figure 5.19: Comparison of A 1 temperature in P92 steels predicted by the proposed measurement based DOE formula and A 1 temperature determined by dilatometry. The predictions for the A 1 temperature in these nineteen P92 steel samples by developed predictive formula, by JMatPro and by the expression developed by Santella [65] are compared in Table 5.13 and in Figure The predictive formula developed in this work has the closest agreement with measured values by dilatometry. The larger deviation range between the A 1 temperature predicted by the formula developed in this work and the dilatometry measurements, and among the compared 101

122 predictive methods requires further clarification. Validation towards a larger database of experimentally determined A1 temperatures is necessary, since the currently available database for P92 steel covers only a narrow range of Mn + Ni contents (Figure 5.19). 102

123 Max Min Test Formula C Mn Si Cr Ni Cu Mo W V N A 1 / C A 1 / C Dev Table 5.12: Composition (wt.%) of 19 commercial P92 steels and corresponding measured (by dilatometry) and formula predicted A 1 temperature. 103

124 Sample Predictive Santella's Jmat- Dilatometry Formula Expression Pro Table 5.13: Predictions for P92 steel samples from different available models ( C). 820 Unit: C Predictive Formula Santella's Expression Jmat-Pro equal line (Dilatometry Measurement/ C) Figure 5.20: Predictions for P92 steel samples from different available models. 104

125 5.4 Validation of formula predicted A 1 temperature For validation of the A 1 temperature predictions by the measurement-based DOE formula, two P91 steel samples (specimen 1 and specimen 2) were collected. As shown in Table 5.12, the formula predicted A 1 temperatures of specimen 1 and specimen 2 are C and C correspondingly. Specimen 1 and specimen 2 were both tempered at 825 C and 850 C for 30 min and then air cooled to room temperature. The macrohardness of tempered samples is shown in Table As shown in Figure 5.19 (a) and (d), the microstructure of specimens tempered at 825 C was tempered martensite, which indicated they were not over tempered. As shown in Figure 5.19 (b) and (e), the microstructure in samples tempered at 850 C contained both ferrite and fresh martensite. This is a proof that the A1 temperature was exceeded and transformation to austenite occurred during tempering. Because 850 C is between the A 1 temperature and A 3 temperature, the equilibrium phases at 850 C are ferrite and austenite. During the air cooling fresh martensite formed from austenite. A 1 temperature in specimen 1 is lower than in specimen 2, so at 850 C more austenite formed in specimen 1. After air cooling, more martensite formed in specimen 1. Figure 5.19 (b) shows more martensite in the microstructure than Figure 5.19 (e), and the hardness of specimen 1 after tempering at 850 C was higher than of specimen 2. Figure 5.19 (c) and (f) show the micro-hardness of the two phases in the microstructure: the softer white phase is ferrite and the harder lath structure phase is martensite. The load of micro-hardness testing was 50g. 105

126 Specimen1 Specimen2 C Mn Si Cr Ni Cu Mo V N Formula Predicted A 1 / C Macro-hardness after tempering at 825 C/HV (testing load 1Kg) Macro-hardness after tempering at 850 C/HV (testing load 1Kg) Table 5.14: Composition and hardness of tempered P91 specimens. 106

127 a d Cr1-1 Spe C 825 C 140HV Spe C 152HV b e Spe C 297HV Spe C 238HV c f 323HV 364HV Cr1-1 Spe C 850 C 297HV 173HV Spe C 238HV 193HV Figure 5.21: Microscopy of tempered samples (X1000): a) Specimen1 tempered at 825 C for 30min, hardness is 140 HV; b) Specimen1 tempered at 850 C for 30min, hardness is 297 HV; c) Micro-hardness of specimen1 tempered at 850 C (load 50g); d) Specimen2 tempered at 825 C for 30min, hardness is 152 HV; e) Specimen2 tempered at 850 C for 30min, hardness is 238 HV; f) Micro-hardness of specimen2 tempered at 850 C (load 50g). 107