Deformation Behavior at Low Temperature in 9mass%Ni Steel

Abstract

The strain distribution in the 9mass%Ni steel introduced by tensile deformation at cryogenic temperature was visualized using a digital image correlation method, and the relationship between the strain distribution and the microstructure of the steel was systematically investigated. Based on the obtained results, the factors that influence the strain distribution were discussed. In the present 9mass%Ni steel, regions consisting of tempered- and fresh-martensite and austenite (TFMA) were embedded in the tempered martensite matrix. The volume fraction of the retained austenite varied along the normal direction of the hot-rolled plates, indicating that Ni segregation occurred during the manufacturing process. As the tensile stress increased, the total elongation remained constant with decreasing temperature. Strain was introduced inhomogeneously via tensile deformation at 77 K. The high- and low-strain regions tended to be distributed in a unit of the block. It indicates that the deformability differed among the blocks. The average strain in the block (ε_block) was strongly correlated with the Schmid factor of the slip system on the habit plane (SF_habit) and area fraction of TFMA in a block (A_MA). The least absolute shrinkage and selection operator regression revealed that the contributions of SF_habit and A_MA to ε_block were nearly equal. Therefore, the deformability of the block in the 9mass%Ni steel is dominated by SF_habit and A_TFMA.

1. Introduction

Generally, carbon steels with body-centered cubic (bcc) structures exhibit strong temperature dependence of mechanical properties.¹⁾ As the strength increases with the decreasing temperature, the elongation decreases drastically at a certain temperature, which is called the ductile-brittle transition temperature (DBTT). DBTT is a representative parameter of the low-temperature toughness of steels, which is important for structural materials used in ships, buildings, bridges, and tanks. Carbon steels have a considerably high DBTT; therefore, many methods have been proposed to be lower the DBTT, such as grain refinement²⁾ and reduction of impurity alloying elements of P and S.³⁾ On the other hand, the 9mass%Ni (9Ni) steel developed in the 1950s has long been used as a structural material for cryogenic applications because 9Ni steels exhibit high ductility even at 77 K (in liquid nitrogen), despite having a bcc structure. To date, various studies on the improvement of the low-temperature toughness of the 9Ni steel have been performed; the Quench-Lamellarize-Temper (QLT) process is one of the most important techniques.^4,5,6,7,8,9) Figure 1 shows a schematic illustration of the microstructure after the QLT process.⁹⁾ The matrix is the tempered martensite produced during the Q process. Tempered martensite has a hierarchical structure consisting of prior austenite grains, packets, blocks, and laths, identical to a typical martensite structure.¹⁰⁾ In addition, the austenite phase precipitates during the L process and is distributed throughout the matrix. Part of those austenites transform into martensite during water quenching after the L process and is tempered during the T process. Some of the other austenite is transformed into martensite during water quenching after the T process, which is called fresh martensite. The austenite remaining after the T process is called retained austenite. Eventually, the austenite regions precipitated during the L process are transformed into a complex region composed of tempered- and fresh-martensite and austenite, which is referred to as the TFMA region in this study, as shown in Fig. 1. The mechanical properties and microstructure of 9Ni steel have been extensively studied; however, its deformation and fracture behaviors have not been thoroughly investigated.

Fig. 1. Schematic illustration of microstructure after Quench-Lamerallize-Temper process in 9mass% Ni steel.⁹⁾ (Online version in color.)

Recently the relationship between deformation behavior and microstructure has been revealed using the digital image correlation (DIC) method on high-resolution scanning electron microscopy (SEM) images.^{11,12,13,14,15,16)} DIC analysis has the advantages of quantitative strain analysis and visualization. In addition, it has been confirmed that the DIC method can be applied to low-temperature deformations.^17,18) In martensitic steel, strain is introduced inhomogeneously into a block unit during tensile deformation.^19,20) In transformation-induced plasticity (TRIP) steel composed of a ferrite matrix and retained austenite, strain is also distributed inhomogeneously by tensile deformation.²¹⁾ The region where the area fraction of retained austenite is high tends to exhibit low strain in the TRIP steel because the hardness of the retained austenite is higher than that of the ferrite matrix. Therefore, the inhomogeneous distribution of retained austenite is one of the factors affecting the inhomogeneous strain distribution in TRIP steel. As the 9Ni steel has a more complicated structure as shown above, compared with the single-phase martensitic steel or the TRIP steel, the deformation behavior must be complicated.

In this study, the strain distribution introduced by tensile deformation at a cryogenic temperature in QLT-processed 9Ni steel was visualized using the DIC method, and the relationship between the strain distribution and the microstructure of the steel was systematically investigated. Based on the obtained results, the factors influencing the inhomogeneous strain distribution were discussed.

2. Experimental Procedure

2.1. Material

Hot-rolled 9Ni steel was used in this study; Table 1 lists the chemical composition of the 9Ni steel. The specimen was subjected to QLT process, as shown in Fig. 2. First, the specimen was solution treated at 1073 K for 3.6 ks, followed by water cooling (Q process), heating at 923 K for 3.6 ks and water cooling (L process). After the L process, the specimen was reheated at 838 K for 3.6 ks, and water-cooled (T process).

Table 1. Chemical composition of 9mass%Ni steel (in mass%).

C	Si	Mn	P	S	Al	Ni	N	Fe
0.060	0.21	0.59	0.010	0.004	0.012	9.20	0.040	bal.

Fig. 2. Heat diagram of Quench-Lamellarize-Temper (QLT) process.

2.2. Microstructure Observations

The microstructures of the specimens were observed using a field-emission SEM under an accelerating voltage of 20 kV. The specimens were polished with an SiC papers and finished using a colloidal silica suspension. A portion of the specimens was electro-polished using a mixture of perchloric acid and ethanol. The crystal orientation was analyzed using the electron backscattered diffraction (EBSD) method. Data were recorded every 0.1 or 0.02 μm and analyzed using software (Aztec Crystal, OXFORD INSTRUMENTS).

2.3. Tensile Tests

Tensile tests were performed on plate specimens with a gauge length of 8 mm, width of 2 mm and thickness of 0.7 mm. The initial strain rate was 1.0×10⁻³ s⁻¹. The atmospheres during the tensile tests were air (room temperature: RT) with the temperature of around 293 K and liquid nitrogen (77 K).

2.4. Strain Distribution Analysis

The strain distribution was analyzed using DIC software (VIC-2D). Interrupted tensile tests were conducted, and SEM images were obtained before and after the deformations. DIC analysis was performed using SEM images with a subset of 71 pixels and a step of 3 pixels. In addition, the high-resolution strain distribution was visualized using a subset of 21 pixels and a step of 2 pixels.

3. Results

3.1. Initial Microstructure

Figure 3 shows (a) SEM image and orientation map of (b) bcc and (c) fcc phases. The orientations parallel to the hot-rolling direction (RD) are shown in Figs. 3(b) and 3(c). In the SEM image (Fig. 3(a)), the regions with bright contrast represent the TFMA dispersed in the matrix. The orientation map of the bcc phase (Fig. 3(b)) shows acicular grains, which is a typical lath martensite structure. In Fig. 3(c), the distributions of the retained austenites are identical to those in the TFMA regions observed in Fig. 3(a). It was reported that austenite reversely precipitated from martensite often had an identical crystal orientation to that in prior austenite grain, which was termed “austenite memory effect”.²²⁾ The crystal orientations of the retained austenites are nearly identical within the prior austenite grain, as indicated by the white dotted line in Fig. 3(c). Therefore, austenite memory effects occurred in the QLT-processed 9Ni steel. The area fraction of the retained austenite measured in Fig. 3(c) was approximately 4.5%, which is significantly lower than the area fraction of the TFMA measured from the SEM image (approximately 25%); this indicates that the TFMA primarily consisted of tempered- or fresh-martensites. Figure 3(d) shows the variation in the area fraction of the retained austenite as a function of distance along the normal direction (ND), indicated by the arrow in Fig. 3(c). The area fraction varies along the ND direction; some portions have an area fraction 1.5 times higher than the average. This inhomogeneous distribution of retained austenite along the ND can be attributed to the microsegregation of Ni.²³⁾ High- and low-Ni regions formed during solidification were elongated along the RD during the rolling process. Consequently, the Ni concentration was inhomogeneously distributed along the ND.

Fig. 3. (a) SEM image, orientation map of (b) bcc and (c) fcc phases. Orientations parallel to the hot rolling direction (RD) are shown in (b) and (c). White dotted line in (c) is one of the prior austenite grains. (d) change in the area fraction of retained austenite along normal direction (ND) indicated by the arrow in Fig. 3(c). (Online version in color.)

Figure 4 shows (a) high-magnification SEM image and corresponding (b) band contrast, (c) phase and (d) orientation maps. The TFMA regions can be distinguished by their brighter contrast in the SEM image, as indicated by the black dotted lines in Fig. 4(a). In Fig. 4(b), the TFMA regions are observed as the region with a darker contrast, which indicates a low band contrast. A lower band contrast indicates the existence of a high density of lattice defects, such as dislocations and grain boundaries. The TFMA region with a higher dislocation density than that in the matrix appeared as a darker contrast region in the band contrast map (Fig. 4(b)). In the TFMA regions indicated by the black dotted lines in Fig. 4(c), the bcc and fcc phases coexisted, confirming the TFMA in the present 9Ni steel also consisted of tempered- and fresh-martensite and retained austenite, as previously reported.⁹⁾ The crystal orientation of the martensite in the TFMA region was identical to that in the matrix, as indicated by the arrows in Fig. 4(d). Because austenite memory effects occurred in the present 9Ni steel, as shown in Fig. 3(c), the austenites precipitated during the L and T processes should have the same crystal orientation as the prior austenite grains. These austenites transformed into martensite during water-cooling and finally formed TFMA regions. In the martensitic transformation, 24 variants of martensite are evenly formed from one prior austenite grain.²⁴⁾ Variant selection should occur in the martensitic transformation during the cooling of the L and T processes to avoid the formation of boundaries between the matrix and martensite, i.e., no interfacial energy is generated. Therefore, martensite in the TFMA region was formed with crystal orientations identical to those in the matrix. Similar variant selection was observed for TRIP steel.²⁵⁾

Fig. 4. (a) High-magnification SEM image. Corresponding (b) band contrast, (c) phase and (d) orientation maps. (Online version in color.)

Microstructural observations confirmed that the present 9Ni steel had a typical microstructure of QLT-processed 9Ni steels, consisting of a tempered martensite matrix and TFMA regions. However, tempered- and fresh-martensite in the TFMA region could not be distinguished in the SEM images and EBSD maps. The chemical concentration should differ between the tempered- and fresh-martensite in the TFMA region. In addition, the Ni segregation occurred, as shown in Fig. 3(d). Therefore, further characterization of the microstructure of the 9Ni steel using energy dispersive spectroscopy will be conducted in the future.

3.2. Tensile Properties at Room and Cryogenic Temperatures

Figure 5 shows (a) the nominal stress – elongation curves and (b) work-hardening rate curves of the specimens at RT and 77 K. For comparison, the true stress values are also shown in Fig. 5(b). The tensile tests were performed three times under each condition, and the representative data are shown in Figs. 5(a) and 5(b). The tensile properties shown in Fig. 5(a) are the average values. The strength increased significantly with decreasing temperature (Fig. 5(a)), and the total elongation ε_total also increased slightly. Consequently, the strength-elongation balance (σ_TS×ε_total) increased significantly at 77 K and was 1.5 times larger than that at RT. Here, σ_TS is the tensile strength. In addition, at 77 K, significantly higher work hardening occurred after the yield drop, resulting in a higher uniform elongation and true stress than those at RT.

Fig. 5. (a) Nominal stress – elongation curves and (b) work hardening curves with true stress at room temperature (RT) and 77 K. (Online version in color.)

After the fracturing at RT and 77 K, dimple fracture surfaces were observed on the specimens, as shown in Fig. 6. Therefore, ductile fracture occurred even at 77 K, although the present 9Ni steel was primarily composed of the bcc phase. The area reduction calculated from the cross-sectional area at the fracture was slightly larger at 77 K than at RT.

Fig. 6. Fracture surfaces of the specimens fractured at (a) room temperature and (b) 77 K.

The tensile test results confirmed that the 9Ni steel used in this study exhibited excellent low-temperature tensile properties, similar to previous studies;^4,5) the strength increased with decreasing temperature as bcc metals, but the elongation was constant as fcc metals.

3.3. Deformation Behavior at Low Temperature

Figure 7(a) shows the stress-elongation responses during interrupted tensile tests for DIC analysis at 77 K; the SS curve obtained from the tensile test at 77 K (Fig. 3(a)) is also shown. The yield stress increased slightly with each interruption. SEM images were obtained at each interruption step, and the observation time was approximately 3.6 ks. Therefore, the increase in yield stress can be attributed to strain aging effects. The effects of strain aging on the deformation behavior were neglected in this study because the strain distribution hardly changed with each interruption. Figure 7(b) shows the variation of the area fraction of retained austenite measured from EBSD phase maps against the applied elongation. Almost all the retained austenite transforms into deformation-induced martensite up to an elongation of 0.1. It was reported that the deformation-induced martensitic transformation affects the strain distribution in TRIP steel.²¹⁾ However, the amount of retained austenite in the present 9Ni steel is much smaller than that in the TRIP steel, and the strain distribution hardly changed even after the area fraction of retained austenite leveled off. Thus, the effect of deformation-induced martensitic transformation on strain distribution was also neglected in this study.

Fig. 7. Nominal stress – elongation responses during interrupted tensile tests at 77 K for the digital image correlation analysis. The nominal stress – elongation curve obtained from the conventional tensile test at 77 K (Fig. 3(a)) is also shown. (b) The variation of the area fraction of retained austenite measured from the phase maps against the applied elongation. (Online version in color.)

Strains of ε_xx and ε_yy were obtained using DIC analysis. Here, ε_xx and ε_yy are the strains parallel and perpendicular to the tensile direction, respectively. Figure 8 shows the ε_xx strain distribution in specimens deformed at (a) RT and (b) 77 K. The tensile direction is parallel to the horizontal direction of the figures. The average ε_xx strain (ε_avg) measured from Figs. 8(a) and 8(b) are 0.05 and 0.04, respectively. The color indicates the magnitude of the strain, and its maximum and minimum values were set to twice the ε_avg and 0, respectively. The strain was inhomogeneously distributed at both temperatures; the high- and low-strain regions were more than twice the ε_avg and approximately 0, respectively. The high-strain regions tended to elongate along 45 degrees to the tensile direction, i.e., the direction of maximum shear stress. Similar strain distributions have been observed in various metallic materials;^11,12,13,14) therefore, this is a general tendency. Furthermore, the strain distribution hardly changed with the progress of tensile deformation, similar to the results of a previous study.²¹⁾ Thus, more strain accumulated in the high-strain region by further tensile deformation. Figure 9 shows the relationship between the standard deviation (SD) of the ε_xx strain histogram and ε_avg. All the ε_xx strain distributions analyzed in this study were close to the Gaussian distribution, therefore, the SD indicates the inhomogeneity of the strain distribution. As shown in Fig. 9, SD is proportional to ε_avg, indicating that the strain difference between the high- and low-strain regions becomes more significant as the tensile deformation proceeds. Similar linear relationships have been reported for ferritic and TRIP steels.²¹⁾ As shown in Fig. 9, for the same ε_avg, the SDs at 77 K and RT are nearly the same. Therefore, the inhomogeneity of the strain distribution hardly changes with decreasing temperature, although the slip systems should be restricted to {110}<1-11> at low temperatures.²⁶⁾ This may be due to the restriction of the slip system that occurs even at RT owing to the martensite structure, as explained in the Discussion Section.

Fig. 8. ε_xx strain distribution in the specimens deformed (a) until average ε_xx strain of 0.05 at room temperature and (b) until average ε_xx strain of 0.04 at 77 K. (Online version in color.)

Fig. 9. Relationship between the standard deviation (SD) of the ε_xx strain histogram and the average strain (ε_avg).

Figure 10 shows the orientation map of the specimen before deformation. The bcc and fcc phases are colored based on the crystal orientation along the tensile direction. The map was obtained from the same region as shown in Fig. 8(b). The regions indicated by the white and black lines are the high- and low-strain regions, where ε_xx strain is larger than 0.08 and smaller than 0.01, respectively, in Fig. 8(b). The high- and low-strain regions tend to be distributed with a block. Considering that the strain was distributed in a block unit in martensitic steel,¹⁹⁾ it is reasonable to assume that each block has a different deformability in the present 9Ni steel. Therefore, in the Discussion Section, the origins of the inhomogeneous strain distribution, i.e., the inhomogeneous deformability of the blocks, are discussed.

Fig. 10. Orientation map of the bcc and fcc phases in the specimen before deformation. The map was obtained from the same area as in Fig. 8(b). The color decoding was based on the crystal orientation along the tensile direction. (Online version in color.)

4. Discussion

The strain was inhomogeneously distributed in a unit of the block by tensile deformation in the present 9Ni steel (Fig. 8). A similar inhomogeneous strain distribution in a block unit has been reported for martensitic steel. Ishimoto et al. indicated that the Schmid factor of an in-lath plane slip system, wherein the slip directions lie on the habit plane, is related to block deformability.¹⁹⁾ Furthermore, in the case of martensitic steel with high carbon concentration, the Schmid factor of the habit plane slip system, wherein both the slip plane and slip directions are parallel to the habit plane, dominates block deformability.²⁰⁾ Figure 11(a) shows an enlarged SEM image of the high-strain block indicated by the white square in Fig. 8(b). Figures 11(b) and 11(c) represent ε_xx strain distribution and pole figure corresponding to Fig. 11(a), respectively. In 9Ni steel, the TFMA regions formed along the lath boundaries, as shown in Fig. 1; thus, the habit plane could be determined from the simple two-dimensional trace analysis of the TFMA region, as indicated by the dotted line in Fig. 11(a). The habit plane in the high-strain block was (110), as indicated by the arrow in Fig. 11(c). Figure 11(d) shows the values of the Schmid factors in the high-strain block of Fig. 11(a), calculated on the basis of the crystal orientation shown in Fig. 11(c). The Schmid factors of the in-lath plane and habit plane slip systems are shown in blue and red, respectively. The Schmid factors of the in-lath plane and habit plane slip systems exhibited high values in the analyzed block. The relationship between the average ε_xx strain within the block (ε_block) and the Schmid factor of the habit plane slip system (SF_habit) in the high- (ε_block > 0.08), intermediate- (0.06 > ε_block > 0.04), and low-strain (ε_block < 0.02) blocks is shown in Fig. 12 as box and whisker plot, where the number of data points in each strain level was 10, and ε_avg was 0.04 (Fig. 8(b)). A roughly positive correlation between the median of ε_block and SF_habit can be observed. For a more quantitative consideration, the coefficient of determination (R²) was obtained by the least-squares method and is shown in Fig. 12. The correlation coefficient (R) was calculated to be 0.66 from R². Because, in general, when R > 0.4, a positive correlation exists between the parameters, it can be judged that a positive correlation exists between SF_habit and ε_block. Thus, SF_habit is an influential factor in the deformability of the block in the present 9Ni steel. The migration distance of mobile dislocations in the habit plane slip system is the longest among the slip systems, and dislocations in the habit plane slip system can be easily activated in blocks with a high SF_habit, resulting in a high ε_avg. Similar results were reported for pearlitic steel where the gliding of dislocations in the ferrite phase was restricted by cementite plates,¹⁴⁾ that is, the slip systems parallel to the cementite plates strongly affected the deformability. The deformability of the block at RT also varied with SF_habit. Therefore, in the present 9Ni steel, the {110}<1-11> slip system is preferentially activated, even at RT, as a low-temperature deformation.²⁹⁾ Because the activated slip systems remained nearly unchanged with the change in temperature, the inhomogeneity of the strain distribution would be independent of the temperature (Fig. 9). Several low-strain blocks exhibited high SF_habit values (Fig. 12), indicating that other influential factors were related to the deformability of the blocks.

Fig. 11. (a) SEM image showing a high-strain block. Corresponding (b) ε_xx strain distribution and (c) pole figure. (d) Schmid factor in the high-strain block in (b). (Online version in color.)

Fig. 12. Relationship between average of ε_xx strain in the blocks (ε_block) and Schmid factor of habit plane slip system (SF_habit) in high-, intermediate- and low-strain blocks. (Online version in color.)

Figure 13 shows (a) an enlarged SEM image and (b) ε_xx strain distribution of the adjacent high-and low-strain blocks indicated by the black square in Fig. 8(b). The strain changes significantly at the block boundary. The area fraction of the TFMA in the low-strain block was considerably high, whereas that in the high-strain region was much lower. Thus, in addition to the SF_habit, the amount of TFMA in the blocks also influences their deformability. Figure 14 shows a box and whisker plot between ε_block and the area fraction of TFMA in the block (A_TFMA), where the blocks analyzed were same as in Fig. 12. The low-strain blocks tend to have high A_TFMA; however, the A_TFMA in the high-strain blocks are below average. The R² value in Fig. 14 was 0.47, and the value of R was calculated to be −0.69. Thus, a negative correlation between A_TFMA and ε_block was valid. Martensites in the TFMA region must be harder than the tempered martensite matrix because they have a higher dislocation density and carbon concentration than those in the matrix. Figure 15 shows (a) an enlarged SEM image of the region indicated by the black dotted square in Fig. 8(b) and its ε_xx strain distribution. The high-resolution ε_xx strain distribution demonstrates that the strain preferentially accumulates in the matrix regions. These results strongly suggest that the inhomogeneous TFMA distribution resulted in an inhomogeneous strain distribution in 9Ni steel. As shown in Figs. 3(c) and 3(d), the 9Ni steel has an inhomogeneous retained austenite (TFMA) distribution, which was likely caused by the inhomogeneous Ni distribution. Therefore, the controlling Ni segregation can control the TFMA distribution and resulting strain distribution.

Fig. 13. (a) High-magnification SEM image showing the region where the high- and low-strain blocks are neighboring in Fig. 8(b). (b) Corresponding ε_xx strain distribution. (Online version in color.)

Fig. 14. Relationship between average of the ε_xx strain in the blocks (ε_block) and the area fraction of tempered- and fresh-martensite and retained austenite (TFMA) in the block (A_TFMA). (Online version in color.)

Fig. 15. (a) SEM image showing the region indicated by black dotted line in Fig. 8(b). (b) Corresponding ε_xx strain distribution. (Online version in color.)

Figure 16 shows the relationship between A_TFMA and SF_habit in the high-, intermediate-, and low-strain blocks. The R value calculated from R² in Fig. 16 was 0.39. Thus, no correlation was observed between A_TFMA and SF_habit, and the two factors appeared to be independent. However, the high-strain blocks tended to have a high SF_habit above 0.35 and low A_TFMA below the average. The contributions of SF_habit and A_TFMA to ε_block were estimated using the least absolute shrinkage and selection operator (LASSO) regression in scikit-learn, a machine learning library in Python. The following relationship was assumed in the LASSO regression:

  

$${\epsilon }_{block}=w_{SF}S{F}_{habit}+w_{TFMA}{A}_{TFMA}+\lambda \left(\left|w_{SF}\right|+\left|w_{TFMA}\right|\right),$$(1)

where, w_SF and w_TFMA are the weights of SF_habit and A_TFMA, respectively; λ is a tuning parameter of the penalty term $\left(\left|w_{SF}\right|+\left|w_{TFMA}\right|\right)$ to avoid overtraining, which was assumed to be 1 in this analysis. Standardized data were used for learning, and the data were divided into training and testing datasets. Because the number of testing data was only 5, the learning was performed cyclically with randomly changing learning and testing data; the number of cycles was 5000. Table 2 shows the average mean square error (MSE) between the test data and predicted value, R², R, w_SF, and w_TFMA for 5000 cycles. Because MSE is low and R² is high, the accuracy of learning is comparatively high despite the small amount of data. Furthermore, R is more than 0.7, indicating a strong correlation between SF_habit and A_TFMA, and ε_block. The absolute values of w_SF and w_TFMA were approximately the same. Therefore, it can be concluded that the contributions of SF_habit and A_TFMA to ε_block are nearly identical, and both the factors dominate the deformability of the block.

Fig. 16. Relationship between the area fraction of tempered-and fresh-martensite and retained austenite (TFMA) of the block (A_TFMA) and Schmid factor of habit plane slip system (SF_habit) in high-, intermediate- and low-strain blocks. (Online version in color.)

Table 2. Average mean square error between the test data and predicted value, coefficient of determination, correlation coefficient, w_SF, and w_TFMA for 5000 cycles.

Mean square error	Coefficient of determination	Correlation coefficient	wSF	wMA
0.18±0.09	0.77±0.09	0.88±0.05	0.46±0.03	−0.48±0.04

In general, an inhomogeneous strain distribution introduced during deformation promotes the nucleation of voids and cracks,²⁷⁾ which deteriorates the toughness of the materials. Therefore, a reduction in the inhomogeneous deformation can improve the toughness of 9Ni steel at low temperatures. If a sharp texture can be developed in the matrix of 9Ni steel, it will reduce the inhomogeneity of deformation caused by the variety of SF_habit because the blocks in the textured matrix have similar SF_habit. Furthermore, as discussed in Section 3.1., Ni segregation is one of the reasons for the inhomogeneous distribution of TFMA. Thus, homogenization heat treatment should be effective in accommodating the inhomogeneous strain distribution caused by the nonuniform distribution of the TFMA. Therefore, based on the results obtained in this study, it is possible to improve the superior low-temperature mechanical properties of 9Ni steel through microstructural control.

5. Conclusion

The strain distribution introduced by tensile deformation at a cryogenic temperature (77 K) in a 9mass%Ni steel was visualized using the digital image correlation method, and the relationship between the strain distribution and the microstructure of the steel was systematically investigated. Based on the results obtained, the factors influencing the inhomogeneous strain distribution were quantitatively discussed. The key results are summarized as follows:

(1) The tempered- and fresh-martensite and retained austenite (TFMA) regions appeared as brighter contrast regions in scanning electron microscopy images, and the retained austenite was inhomogeneously distributed along the normal direction of hot-rolling.

(2) The strain was introduced inhomogeneously by tensile deformation at both RT and 77 K. High- and low-strain regions tended to be distributed in a unit of the block, suggesting that the deformability varies from block to block.

(3) The average ε_xx strain within a block (ε_block) correlated with the Schmid factor of the habit plane slip system (SF_habit) and area fraction of TFMA in a block (A_TFMA). The block with a high SF_habit above 0.35 and lower A_TFMA than the average value exhibited a high strain.

(4) The least absolute shrinkage and selection operator regression revealed that the contributions of SF_habit and A_TFMA to ε_block are nearly equal, and both the factors dominated the deformability of the block in the present 9mass%Ni steel.

The detailed strain distribution in 9mass%Ni steel during deformation was revealed for the first time, and the factors influencing the inhomogeneous strain distribution were quantitatively discussed. Based on the results obtained in this study, it is possible to improve the superior low-temperature mechanical properties through microstructure control.

Acknowledgements

The authors acknowledge the financial support of the 31st ISIJ Research Promotion Grant, JFE 21st Century Foundation, and Grant-in-Aid for Scientific Research (KAKENHI) Grant No. 20K14605. The authors are also grateful to Prof. Osamu Umezawa of Yokohama National University for providing the 9mass%Ni steel.

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