Cast Austenitic Stainless Steel Research Articles

Ultrasonic wave propagation analyses in centrifugally cast stainless steel using solidification grain structure models

Several components of nuclear power plants are made from cast austenitic stainless steel (CASS) because of its high corrosion resistance and strength. CASS that is centrifugally fabricated in a rotating mold is used in primary coolant piping. In-service inspection based on ultrasonic testing (UT) has to be conducted for primary coolant piping in accordance with fitness-for-service codes. However, a high-accuracy evaluation of flaws in CASS components through UT is difficult because the ultrasonic waves are scattered and attenuated by the coarse grains, and the ultrasonic beam is distorted by the anisotropy resulting from the grain orientations. Some works have attempted to improve UT capability for CASS components. For more improvement, it is essential to understand how ultrasonic waves propagate in the solidification structure of CASS. Numerical simulations are a useful and reasonable way for this purpose. The simulation model should account for a three-dimensional grain structure. In this study, we prepared three different models of solidification structures for centrifugally CASS, and we fed these structures into an explicit finite element model to simulate the propagation of ultrasonic waves. Afterward, we compared the simulated wave propagations with those measured by a laser Doppler vibrometer to determine an appropriate model that showed good agreement between the simulated and experimental results.

Generation and classification models of ultrasonic signals in aged cast austenitic stainless steel (CASS)

In this study, we attempted to generate new ultrasonic signals from thermally aged CASS specimens using a statistical-based model to augment the experimental data. A variational auto-encoder (VAE) model is adopted to generate the ultrasonic signals. To verify the quality of the generated data (signals), two ML algorithms (k-nearest neighbor and multi-layer perceptron algorithms) were used for classification with newly generated data for training and experimental data for testing. The results showed that new ultrasonic signals could be generated effectively with a small amount of experimental data, with the newly generated data showing features approximating those of the experimental data, indicating high classification accuracy rate for ultrasonic signals from aged CASS specimens. In addition, a sampling method for the generated model is suggested.

Effects of Nb addition on the microstructure and low-cycle fatigue properties of heat-resistant stainless steel

This study aims to improve the quality and durability of materials used in turbocharger housing, particularly to limit the degradation of automobile engine parts. As a result, niobium (Nb)-bearing heat-resistant cast austenitic stainless-steel samples have been explored. Two distinct steel samples, denoted as Alloy-A (with 0 wt.% Nb) and Alloy-B (with 1.5 wt.% Nb), were fabricated by centrifugal casting for investigating the effects of Nb content on the corresponding microstructure and low-cycle fatigue properties of these samples. The phases formed within these alloys were predicted by thermodynamic calculations using the Thermo-Calc software, with the results aligning well with the experimental results. The addition of Nb promoted the formation of niobium carbide, resulting in increased hardness and greater area fraction of the carbides present in the steel samples. Alloy-B displayed enhanced high-temperature strength over Alloy-A, while the low-cycle fatigue resistances of the two alloys were similar. Fatigue cracks predominantly initiated at the outer surface of the specimens, mainly in the carbide regions, due to the mismatch between the matrix and carbide hardness. This paper is the first to report the effect of Nb addition on the microstructure and low-cycle fatigue properties of heat-resistant cast austenitic stainless steel.

Phase-field simulation of the microstructure and properties evolution in cast austenitic stainless steels for nuclear power plants during thermal aging and annealing

The microstructural evolution of the Z3CN20.09M stainless steel used for primary pipes of the pressure water reactor (PWR) during thermal aging and annealing was discussed via the phase-field method. The evolution law of the amplitude and wavelength of the one-dimensional chromium (Cr) concentration curve representing the degree of spinodal decomposition and their connection regarding nanohardness was analyzed. Result shows that elemental concentration and temperature markedly affect the simulation. Cr elements demonstrate up-hill diffusion, that is, the ferrite decomposes into the Cr-enriched phase (α′ phase) and the Fe-enriched phase (α phase), when the initial Cr concentration is in the spinodal area. Meanwhile, The temperature increase within a certain temperature range (553 K–773 K) accelerates the progress of spinodal decomposition. The α′ phase redissolves into the matrix when the temperature reaches the critical value (823 K). The amplitude and the wavelength take on a growth trend from rapid to tardy during thermal aging. The amplitude rapidly decreases and the wavelength continuously increases during annealing. The relationship between amplitude–wavelength and nanohardness was established. The simulation using the phase-field method is consistent with the experiment of the material nanohardness.

Development and testing of coarse-grained models for ultrasonic simulations of cast austenitic stainless steel

Ultrasonic inspection of cast austenitic stainless steel (CASS) in the nuclear industry is particularly challenging because of sound field scatter and attenuation caused by the coarse-grained microstructure. Modeling and simulation are important tools in ultrasonic testing, as they can be used to help address key aspects of inspections, such as developing new probe designs, predicting inspection reliability, and testing phased-array focal laws. However, developing a useful and reliable CASS model is challenging due to the many grain interfaces and crystalline orientations that must be captured. We demonstrate a method of creating a realistic CASS model that is usable in CIVA, a commercially available modeling and simulation software platform. Using polished and chemically etched sections to highlight the grain structure and orientation, we generate models of a coarse-grained equiaxed specimen and a columnar specimen. We also test an alternative method of generating a coarse-grained model using Voronoi regions. We qualitatively compare sound field scatter and quantitatively compare sound field attenuation and beam partitioning in simulated sound fields to those of laboratory-measured sound fields. Results show that the Voronoi models perform as well as or better than the models based on actual grain morphology. We also show that model-to-model randomness in Voronoi grain structure can impact the magnitude of a simulated echo response by a factor of two or more. Although CASS models are potentially a good depiction of reality for a given scenario, they should not be considered representative since CASS morphology can change significantly from specimen to specimen or within the same specimen.

Predicting the creep-rupture lifetime of a cast austenitic stainless steel using Larson-Miller and Wilshire parametric approaches

An experimental dataset of just over 100 creep tests of a cast austenitic stainless steel, CF8C-Plus, was analyzed by two temperature-compensated parametric models (Larson-Miller, Wilshire et al.) to predict long-term lifetimes as functions of temperature and stress. The dataset and associated regression analyses showed greater scatter than typically found in recent similar studies of wrought Ni-based alloys by the same two models and was attributed to the microstructural inhomogeneity of the cast stainless steel. Qualitatively, the Larson-Miller formalism showed greater lifetime prediction accuracy than the Wilshire approach, with the latter model's predictive ability being particularly degraded by the presence of two very significant outlier results. This observation suggests that the Larson-Miller approach is more robust when treating rupture-time datasets that show particularly wide experimental scatter. Despite the differences in the overall predictive ability, both models yielded similar predictions of the applied stress at which CF8C-Plus would have a creep-limited lifetime of 100,000 h when loaded below the yield point.

Creep damage mechanism of 20Cr32Ni1Nb steel at high temperatures

In the petrochemical industry, the centrifugally cast austenitic 20Cr32Ni1Nb stainless steel used in steam reformer furnace's hot outlet manifold experiences creep failure and microstructural degradation, impacting its structural integrity. Understanding the causes of these issues is crucial for addressing the associated challenges. This study examines the creep deformation behaviour, damage mechanisms, and microstructural evolution of 20Cr32Ni1Nb steel under stresses of 22–50 MPa at temperatures of 1163 K (890 °C) and 1223 K (950 °C). Analysis of creep and creep strain rate curves revealed the presence of both primary and tertiary creep stages, with the power law relationship governing the stress-dependent minimum creep rate and rupture life. The variance of creep damage tolerance factor was also analysed, with results showing that in high-stress regimes, loss of section and necking along with cavity growth may initiate tertiary creep, while in low-stress regimes, microstructural degradation, specifically G-phase formation, may initiate tertiary creep. As the duration of creep exposure increases, microstructures were observed to degrade, with the growth and coarseness of NbC and M23C6 carbides. The structure of the space group for the Ni–Nb–Si enriched G-phase in 20Cr32Ni1Nb steel was found to be Fm3‾m, with a lattice parameter of about 1.14 nm.

Irregular microstructure-property linkage for cast alloys by a novel deep learning approach: Application on cast austenitic stainless steel

The conventional approach of extracting microstructure parameters is inaccessible to accurately describe the irregular microstructure in cast alloys. In the present paper, we have proposed a new model based on convolutional neural network (CNN) to directly link the irregular microstructure with micrographs and property relationship for such alloys. By taking 10 × 10 units through the adaptive average pooling layer after the best channel selection on the convolutional layer, the model has succeeded to establish the microstructure-toughness relationship for the cast austenitic steel solutioning treated at different temperatures. It shows a prediction accuracy with R2 of 0.84, and the predicated median error to actual testing impact toughness is as small as ± 2 J. In addition, intermediate representations in the model are visualized, showing that microstructure features such as volume fraction, morphology and distribution of irregular ferrite are captured in the 10 × 10 units.

The study of sigma and carbide in cast austenitic stainless-steel grade HH after 24 years of high-temperature service

Cast austenitic stainless steel (CASS) has been widely used for long-term service periods in high-temperature applications. Nonetheless, the behavior of CASS after high-temperature and long-term service is insufficiently researched alongside the microstructural analysis for sigma phase and carbide. Here, intermediate pipe support made from CASS grade HH was investigated after 24 years of service at 700 °C and compared with the solution-treated specimen at 1100 °C for 2 h. The chemical composition was analyzed by optical emission spectroscopy (OES), while the microstructure was observed using optical microscopy and scanning electron microscope (SEM). The confirmation of phase composition and lattice parameters were further analyzed by energy-dispersive X-ray spectroscopy (EDS) and X-ray diffraction spectroscopy (XRD). The mechanical properties were assessed by tensile tests, hardness tests, and impact tests, whereas the corrosion property was analyzed using potentiodynamic polarization. Based on experiment and analysis, the effects of 24 years of exposure on the sigma phase are spheroidization and Cr depletion, and even partial transformation to ferrite. The sigma phase significantly increases the hardness, but Cr depletion slightly reduces the hardness to 96.86 HRB. Meanwhile, the carbide will increase in quantity and develop an irregular interface at long-term high-temperature exposure. Microstructure evolution of the sigma phase and carbide decreased tensile strength to 46%, elongation to 3%, and impact value to 1.6% of the minimum specifications, respectively, while the corrosion rate increased 10 times (about 7.35 μm per year).

Data Augmentation of Micrographs and Prediction of Impact Toughness for Cast Austenitic Steel by Machine Learning

The Material Genome Initiative has been driven by high-throughput calculations, experiments, characterizations, and machine learning, which has accelerated the efficiency of the discovery of novel materials. However, the precise quantification of the material microstructure features and the construction of microstructure–property models are still challenging in optimizing the performance of materials. In this study, we proposed a new model based on machine learning to enhance the power of the data augmentation of the micrographs and construct a microstructure–property linkage for cast austenitic steels. The developed model consists of two modules: the data augmentation module and microstructure–property linkage module. The data augmentation module used a multi-layer convolution neural network architecture with diverse size filter to extract the microstructure features from irregular micrographs and generate new augmented microstructure images. The microstructure–property linkage module used a modified VGG model to establish the relationship between the microstructure and material property. Taking cast austenitic stainless steels after solution treating in different temperatures as an example, the results showed that the prediction accuracy of the developed machine learning model had been improved. The coefficient R2 of the model was 0.965, and the medians were only ±2 J different with the measured impact toughness.

Homogenization temperature dependent microstructural evolution and mechanical properties in a Nb-stabilized cast austenitic stainless steel

A Nb-stabilized cast austenitic stainless steel is processed by homogenization treatments at 1050 °C, 1150 °C, and 1250 °C respectively to investigate microstructure dependent deformation behaviors and mechanical properties. Homogenization treatment at 1050 °C could not change the size of primary NbC, while significant dissolution of primary NbC destroys the network connectivity after homogenization treatments at temperatures above 1150 °C. Meanwhile, dissolution of NbC will increase C and Nb content in austenite matrix, leading to an increase in SFE. Nucleation of microcracks within NbC at the initial stage of deformation is caused by plastic strain incompatibility at the NbC/γ interface. Fast crack propagation along the continuous network of primary NbC leads to premature fracture at a lower stress level, and resultant lower ultimate tensile strength and elongation in as-cast and 1050-H samples. A higher SFE reduces the susceptibility to localization of deformation and crack propagation along the discrete primary NbC is suppressed, resulting in an obvious improvement in the ultimate tensile strength and elongation in 1150-H and 1250-H samples. • The dissolution and precipitation of NbC during homogenization treatments by different temperatures were revealed. • A higher SFE is responsible for the lower rate of deformation-induced α'-martensitic transformation by suppressing the planar slip. • A higher SFE reduces the susceptibility to localization deformation and the crack propagation along the primary NbC is suppressed. • A noticeable improvement in the ultimate tensile strength and elongation after homogenization treatment at temperatures above 1150 °C.

Detecting Thermally-Induced Spinodal Decomposition with Picosecond Ultrasonics in Cast Austenitic Stainless Steels

Destructive techniques to monitor nuclear reactor component health may not always be available during service, as they are time-consuming and often require pre-installed inspection coupons. Non-destructive evaluation (NDE) techniques can bridge this gap by rapidly identifying the state of mission-critical reactor components, via inference between NDE-measurable material properties and those of ultimate interest, such as ductility and toughness. Here, we demonstrate one such inference about the health of thermally aged cast austenitic stainless steels. Observations of surface acoustic wave peak (SAW) splitting correlate with spinodal decomposition-induced embrittlement as destructively measured by Charpy impact energy. Elastodynamic calculations and molecular dynamics simulations of the effects of spinodal decomposition on elastic moduli support that the new acoustic modes present are due to stiffening in the δ-ferrite domains. This discovery enables one to probe structure-property relationships in materials in a greatly accelerated manner, suggesting that similar inference methods can be used to determine material fitness-for-service, or to quickly uncover new structure-property relationships.

Вплив термічної обробки на механічні властивості модифікованих неіржавіючих сталей

The paper presents the results of the study of the influence of structure on the mechanical properties of microalloyed and modified austenitic stainless steels. It is shown that the mechanical properties of cast austenitic stainless steels with a probability of 95 % and an error of 0.46 to 13.2 % are determined by such structural parameters as austenite grain size, carbide phase and ferrite content in austenite after quenching, quantity, size and distance between oxide, titanium sulfides and carbonitrides particles. Mathematical models of the structure influence on the yield strength, reduction of area and toughness of stainless steels are built. The structure parameter effectiveness is shown and it is shown that to increase the efficiency of strength, reduction of area and toughness of stainless steels at normal temperature structural factors affect in the following sequence: austenite grain size, volume fraction, size and distribution of titanium carbonitrides and sulfides, the total quantity of non-metallic inclusions, carbides and ferrite in austenite after hardening of stainless steel, as well as the volume fraction, size and distribution of oxide inclusions. Keywords: steel, structure, mechanical properties, strength, ductility, toughness.

Cast Austenitic Stainless Steel Reinforced with WC Fabricated by Ex Situ Technique

In this study, the process of reinforcing austenitic stainless steel with tungsten carbide (WC) particles prepared by an ex situ technique was investigated. More specifically, the effect of microstructural features on the properties of the resulting WC-metal matrix composite (WC-MMC) was studied. For that purpose, porous Fe-WC preforms, prepared by the ex situ technique, were fixed in the mold cavity where they reacted with the molten steel. As confirmed by scanning electron microscopy with energy dispersive spectroscopy (SEM/EDS), the resulting composite showed a compositional and microstructural gradient in depth. The microstructure next to the surface is essentially martensite with large WC particles. From this region to the base metal, the dissolution of the original WC particles increased, being closely related to the formation of new carbides: (Fe,W,Cr)6C, (Fe,Cr,W)7C3, and (Fe,Cr,W)23C6. At the interface bonding, a sound microstructure free of discontinuities was achieved. Furthermore, the mechanical tests indicated that the WC-MMC is four times harder and more wear-resistant than the base metal.

Effect of microstructure evolution at the interphase boundary on the thermal ageing embrittlement of CF8M cast austenitic stainless steel

Thermal ageing embrittlement of a cast austenitic stainless steel, CF8M with 25% of δ-ferrite, was evaluated using various mechanical tests such as micro-hardness, small punch, tensile, and fracture toughness (J-R) tests. To simulate long-term service at operating temperature in light water reactors (LWRs), CF8M was thermally aged at 400 °C up to 10,000 h. After thermal ageing for 5000 h, phase separation into Fe-rich and Cr-rich phases by spinodal decomposition and G-phase precipitation were observed in δ-ferrite, resulting in significant hardening in δ-ferrite. As ageing duration increased to 10,000 h, additional changes in microstructure and micro-hardness in δ-ferrite were not significant. On the other hand, fracture properties, such as fracture toughness and small punch (SP) energy showed further reduction with ageing time, which could be related to elemental segregation at the austenite/ferrite interphase boundary. After reversion heat treatment at 550 °C for 1 h, spinodal decomposition was mostly disappeared, but G-phase precipitate in δ-ferrite and elemental segregation at the austenite/ferrite interphase boundary were not recovered, which could be responsible for the incomplete recovery of mechanical properties.

Tensile Behavior and Performance of Syntactic Steel Foams Prepared by Infiltration Casting

Syntactic steel foams (SSFs) were prepared by low-pressure infiltration of molten ASTM CF-8 cast austenitic stainless steel into randomly and densely packed Al2O3 hollow spheres. The microstructure of the SSFs was characterized by scanning electron microscopy and energy dispersive spectrometry. Using dumbbell-shaped specimens, the density of the as-cast SSFs is measured in the range from 3.33 to 3.64 g/cm3 and their ultimate tensile strength from 83.1 to 97.6 MPa. No significant chemical reaction was detected between the fillers and matrix. The quasi-static uniaxial tensile deformation of the syntactic foams underwent elastic deformation, plastic deformation, and then a failure stage, showing similar tensile behavior to plastic bulk metals but different behavior to common metal foams. From the good ductility of the metal matrix, a clear macroscopic plastic deformation was observed before the ductile fracture of the syntactic foams. A constitutive relationship of the SSFs under uniaxial tensile loads has been proposed.

Effect of Ni, Mo and Mn content on spinodal decomposition kinetics and G-phase precipitation of aged model cast austenitic stainless steels

• Effect of Ni, Mo and Mn content on ferrite decomposition. • Thermal ageing of cast austenitic stainless steels (CASS) and model alloy. • Spinodal decomposition kinetics and G-phase precipitation. During ageing, the ferritic phase of Cast Austenitic Stainless Steels (CASS) undergoes decomposition by spinodal decomposition and G-phase precipitation, which leads to increasing hardness and decreasing impact toughness. Mo-bearing steels are more prone to ageing than Mo-free steels. To study the effects of Ni, Mo, and Mn, duplex model alloys were thermally aged at 350 °C and 400 °C. The microstructure evolution during ageing was characterized by atom probe tomography. Only Ni additions seem to affect the spinodal decomposition kinetics. Ni accelerates the spinodal decomposition kinetics regardless of the presence of Mo or Mn. Additions of Mo and Mn promote the precipitation of G-phase.

Classification of ultrasonic signals of thermally aged cast austenitic stainless steel (CASS) using machine learning (ML) models

Cast austenitic stainless steels (CASSs) are widely used as structural materials in the nuclear industry. The main drawback of CASSs is the reduction in fracture toughness due to long-term exposure to operating environment. Even though ultrasonic non-destructive testing has been conducted in major nuclear components and pipes, the detection of cracks is difficult due to the scattering and attenuation of ultrasonic waves by the coarse grains and the inhomogeneity of CASS materials. In this study, the ultrasonic signals measured in thermally aged CASS were discriminated for the first time with the simple ultrasonic technique (UT) and machine learning (ML) models. Several different ML models, specifically the K-nearest neighbors (KNN), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP) models, were used to classify the ultrasonic signals as thermal aging condition of CASS specimens. We identified that the ML models can predict the category of ultrasonic signals effectively according to the aging condition.

A comprehensive study of the effects of long-term thermal aging on the fracture resistance of cast austenitic stainless steels

Loss of fracture resistance due to thermal aging degradation is a potential limiting factor affecting the long-term (80+ year) viability of nuclear reactors. To evaluate the effects of decades of aging in a practical time frame, accelerated aging must be employed prior to mechanical characterization. In this study, a variety of chemically and microstructurally diverse austenitic stainless steels were aged between 0 and 30,000 h at 290–400 °C to simulate 0–80+ years of operation. Over 600 static fracture tests were carried out between room temperature and 400 °C. The results presented include selected J-R curves of each material as well as K0.2mm fracture toughness values mapped against aging condition and ferrite content in order to display any trends related to those variables. Results regarding differences in processing, optimal ferrite content under light aging, and the relationship between test temperature and Mo content were observed. Overall, it was found that both the ferrite volume fraction and molybdenum content had significant effects on thermal degradation susceptibility. It was determined that materials with >25 vol% ferrite are unlikely to be viable for 80 years, particularly if they have high Mo contents (>2 wt%), while materials less than 15 vol% ferrite are viable regardless of Mo content.

Transformation of the δ-Ferrite in SS2343 Austenitic Stainless Steel upon Annealing at 1050 °C, 1150 °C and 1250 °C

The solidification behaviors of laboratory cast austenitic SS2343 stainless steel in terms of the volume fraction of δ-ferrite in the as-cast state and its transformation after subsequent annealing were investigated. Monitoring of morphological transformations of δ-ferrite in the microstructure show the progress of δ-ferrite dissolution. Annealing tests were conducted at 1050 °C, 1150 °C and 1250 °C with soaking times of 5 and 40 min. The thermodynamic prediction and metallographic identification of δ-ferrite are presented. The ferrite fractions were measured using a magnetic method and determined to be in the range between 10.7% and 14.6%. The volume share of δ-ferrite decreased with an increase in temperature and the time of annealing. About 50–55% the δ-ferrite was effectively transformed. The δ-ferrite phase, originally present in a dendritic morphology, tends to break up and spheroidize. The morphology varies from vermicular, lacy and acicular shapes to globular for higher temperatures and for longer exposure times. In the δ-ferrite after annealing, concentrations of Cr and Mo decrease, and conversely the concentration of Ni increase, all by small, but significant, amounts. The observed changes in the solute concentration can be explained in terms of the transformation of ferrite into austenite and sigma phases.