Dataset for machine learning of microstructures for 9% Cr steels
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Hybrid manufacturing is a combination of additive (deposition) and subtractive (machining) manufacturing in a single machine tool. Such a system can be used for near net shape manufacturing and component repair using either similar or dissimilar materials. Integrated into a single system, transition between additive and subtractive manufacturing can occur immediately and be leveraged to generate large components by alternating between the processes. In this investigation, we show how the interleaved capabilities can reduce overall cycle time by up to 68 %, improve average relative elongation to failure by 71 %, and reduce the average relative porosity fraction by 83 % when compared to traditional additive manufactured components. Results from this investigation builds the foundation needed for hybrid manufacturing to be applicable towards the manufacture of large complex components such as nosecones and marine propulsors.
Austenitic 347H stainless steel offers superior mechanical properties and corrosion resistance required for extreme operating conditions such as high temperature. The change in microstructure due to composition and process variations is expected to impact material properties. Identifying microstructural features such as grain boundaries thus becomes an important task in the process-microstructure-properties loop. Applying convolutional neural network (CNN)-based deep learning models is a powerful technique to detect features from material micrographs in an automated manner. In contrast to microstructural classification, supervised CNN models for segmentation tasks require pixel-wise annotation labels. However, manual labeling of the images for the segmentation task poses a major bottleneck for generating training data and labels in a reliable and reproducible way within a reasonable timeframe. Microstructural characterization especially needs to be expedited for faster material discovery by changing alloy compositions. Here, in this study, we attempt to overcome such limitations by utilizing multimodal microscopy to generate labels directly instead of manual labeling. We combine scanning electron microscopy images of 347H stainless steel as training data and electron backscatter diffraction micrographs as pixel-wise labels for grain boundary detection as a semantic segmentation task. The viability of our method is evaluated by considering a set of deep CNN architectures. We demonstrate that despite producing instrumentation drift during data collection between two modes of microscopy, this method performs comparably to similar segmentation tasks that used manual labeling. Additionally, we find that naïve pixel-wise segmentation results in small gaps and missing boundaries in the predicted grain boundary map. By incorporating topological information during model training, the connectivity of the grain boundary network and segmentation performance is improved. Finally, our approach is validated by accurate computation on downstream tasks of predicting the underlying grain morphology distributions which are the ultimate quantities of interest for microstructural characterization.
One parameter that has both economic and performance effects in the manufacturing of steel parts is the powder feedstock used. 316 L steel powder can be produced through water atomization or gas atomization, where both the powder size and the atomization process determine the cost of powder feedstock. Gas atomized powder is spherical and has a lower oxygen content than water atomized powder, making it the preferred choice for performance, but comes at a higher financial cost. Here, in this study, various 316 L powder batches are characterized to understand physical properties and microstructural variations. Density and flowability were determined using gas pycnometry and Hall flow tests. Powder morphology and porosity were qualitatively analyzed via optical microscopy and scanning electron microscopy, Finally, transmission electron microscopy was used to determine changes in phases present. Spherical gas atomized powder of 30–100 μm contained Mn$-$Si rich oxides both on the surface and within the matrix. Meanwhile, irregular water atomized powder 30 μm in diameter contained silicon oxides without manganese. All powder particles contained a nickel rich cellular structure which was shown to support the formation of additional phases. While literature has observed general microstructural features across 316 L powder particles, transmission electron microscopy (TEM) in this study has identified variations in precipitation and cell structures. Information regarding morphology, flowability, density, and phase distributions can be applied to understand variations in part consolidation, which will in turn show which powder aspects are significant for production.
Double soaking (DS) is a thermal processing route intended to produce austenite–martensite microstructures in steels containing austenite‐stabilizing additions and consists of intercritical annealing (primary soaking), followed by heating and brief isothermal holding at an increased temperature (secondary soaking), and quenching. Herein, experimental dilatometry during DS of a medium‐manganese (Mn) steel with nominally 7 wt% Mn and an ultralow residual carbon concentration, in combination with phase‐field simulations of austenite formation during secondary soaking, is presented. The feasibility of maintaining heterogeneous Mn distributions during DS is demonstrated and insight is provided on the effects of the secondary soaking temperature and prior Mn distribution on the ferrite‐to‐austenite phase transformation during the secondary soaking portion of the DS treatment.
In this study, we present the effects of input processing parameters and strut thickness (in square struts) on microstructure and properties in laser powder bed fusion additively manufactured stainless steel 316L lattice-emulating structures. Lattice-emulating X-structures with square cross-sections of 1.5, 1.0, and 0.5 mm were fabricated using three different parameter sets with varying power, speed, and therefore, linear energy density. Grain size and morphology were shown to be dictated by epitaxial growth, which was dependent on weld pool morphology. Additionally, grain size and morphology were shown to change across the thickness direction of the struts (from the bottom inclined surface to the top inclined surface). The spatial variation in grain size was reflected by changes in hardness through the thickness of each strut. The 0.5 mm struts exhibited more significant grain elongation in the strut direction and larger sub-grain solidification cell diameters than their thicker counterparts. The larger sub-grain solidification cell diameters in the 0.5 mm samples resulted in correspondingly lower hardness values when compared to samples of higher thicknesses.
Two flux thimble tubes (FTT) made of 15% cold-worked 316 stainless steel (SS) were harvested from Ringhals Pressurized Water Reactor (PWR) Unit 2, with peak damages of 76 and 100 displacements per atom (dpa) after 29 and 34 years’ service, respectively. Specimens sectioned from parent tubes were comprehensively characterized with nominal damage levels of ~0, ~41, ~74, 76, and 100 dpa at a nominal temperature range of 285-323 °C. Both FTTs contained helium and hydrogen gases as transmutation products. The helium follows a production rate of ~9.8 appm/dpa, while environmental factors complicate hydrogen production obscuring an exact H/dpa ratio. Irradiation-induced dislocation loops, nano-cavities, solute clusters, and microsegregation were all observed. The dislocation loops and nano-cavities indicated saturation at 41 dpa. The solute clusters continued to evolve with Ni-Si clusters formed at 41 dpa, and Ni-Si-Mn-P clusters formed at 74 and 100 dpa, but neither clusters exhibited distinct diffraction patterns at any damage levels. Solute clusters were observed to frequently be co-located with dislocation loops, but fully decorated loops were rarely detected. Significant radiation-induced segregation (RIS) was observed around grain boundaries at all damage levels. The modified inverse Kirkendall (MIK) model captured the RIS behavior of major elements. Large cavities within or around an Mn-S rich region were observed for the first time. Through all the damage levels, void swelling is always below 0.05%, making significant dimensional change unlikely in core internals when used at similar conditions. Meanwhile, the role of overwhelming nanocavities, presumably helium bubbles, should be considered in other potential degradation mechanisms, including irradiation-assisted stress corrosion cracking, embrittlement, and loss of fracture toughness, which remain the concerns for extended operation of nuclear power plants.
In the pursuit of lightweight, durable steel, we have successfully developed a multicomponent structure in AISI 9254 spring steel using a two-stage quenching and partitioning (Q&P) process. The primary objective of this process was to engineer an optimized microstructure consisting of nanobainite, martensite, and nano-carbides. Utilizing the insights gained from the results of advanced techniques such as X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and atom probe tomography (APT) performed on the as-received AISI 9254 spring steel, we refined the quenching and partitioning (Q&P) path, leading to the successful establishment of a bainitic transformation for superior mechanical properties. Our tensile tests revealed a high yield strength (≈ 1600 ± 25 MPa) and ultimate tensile strength (≈ 1850 ± 50 MPa), along with considerable elongation (≈ 11.15 ± 0.25%). We also identified that pre-formed martensite lath defects and high silicon content play crucial roles during the Q&P process, preventing carbide coalescence and increasing strain-hardening capacity. Finally, this study demonstrates the potential of a Q&P process to generate high-strength, ductile steel for automotive and aerospace applications.
Advancements were made in automating microscopy-based material characterization, particularly in studying irradiation effects on additively manufactured (AM) materials using machine learning (ML) and computer vision (CV). These automation efforts address the challenges of analyzing complex microstructures, accelerating the detection of irradiation-induced defects. Two CV models were developed at Argonne National Laboratory (ANL) to enhance transmission electron microscopy (TEM) analysis of irradiated AM 316 stainless steel. The first model focused on the detection of irradiation-induced dislocation loops, which contribute to material hardening and embrittlement. These loops, categorized as faulted or perfect, were automatically detected and classified using a Mask R-CNN model trained on TEM images from both in-situ and ex-situ ion irradiation experiments. The model achieved high accuracy, with precision, recall, and F1 scores of 0.839, 0.734, and 0.776, respectively, demonstrating its effectiveness in analyzing dislocation loops in irradiated AM materials. The second CV model was developed to analyze the size and wall thickness of dislocation cells in laser powder bed fusion (LPBF) 316 stainless steel. Using a U-Net++ architecture with EfficientNet as the encoder, the model was trained on TEM images to segment and measure cell size and wall thickness.
SHINE Medical Technologies is developing a facility that will produce molybdenum-99 for medical isotope procedures. As part of this facility, a metal tank, called the target solution vessel (TSV), will hold a uranyl sulfite solution. The TSV must withstand neutron irradiation near room temperature and potential corrosion from the aqueous solution. The large cylindrical TSV will be fabricated by welding and will have numerous pipes and connections also attached via welding. The vessel and those pipes will have varying thicknesses so both gas tungsten arc welding (GTAW) and flux-cored arc welding (FCAW) will be necessary to fabricate different sections. The original material under consideration for the TSV was Zircaloy-4 (Zry-4), but it is known to form a hydride which can degrade its mechanical properties. Thus, some investigation here is focused on the effects of hydrogen uptake in the Zry-4. Additionally, the alternative material being considered is AISI 347, a stainless steel. Both materials have little existing data for their neutron irradiation behavior below 100°C, and both have open questions on the weld behavior under neutron irradiation. Testing has focused on characterizing their weld properties with tensile tests and performing neutron irradiation of samples in the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory (ORNL).
In this study, a thorough investigation of the microstructures and tensile properties of 316L stainless steel fabricated via laser powder bed fusion (L-PBF) was done. 316L stainless steel specimens with two different thicknesses of 1.5 mm and 4.0 mm fabricated under similar conditions were utilized. Microstructural characterization was performed using optical microscopy (OM) and scanning electron microscopy (SEM) equipped with electron backscatter diffraction (EBSD). Melt pools and cellular structures were observed using OM, whereas EBSD was utilized to obtain the grain size, grain boundary characteristics, and crystallographic texture. The 1.5 mm thick sample demonstrated a yield strength (YS) of 538.42 MPa, ultimate tensile strength (UTS) of 606.47 MPa, and elongation to failure of 69.88%, whereas the 4.0 mm thick sample had a YS of 551.21 MPa, UTS of 619.58 MPa, and elongation to failure of 73.66%. These results demonstrated a slight decrease in mechanical properties with decreasing thickness, with a 2.4% reduction in YS, 2.1% reduction in UTS, and 5.8% reduction in elongation to failure. In addition to other microstructural features, the cellular structures were observed to be the major contributors to the high mechanical properties. Using the inverse pole figure (IPF) maps, both thicknesses depicted a crystallographic texture of {001} <101> in their as-built state. However, when subjected to tensile loads, texture transitions to {111} <001> and {111} <011> were observed for the 1.5 mm and 4.0 mm samples, respectively. Additionally, EBSD analysis revealed the pre-existence of high-density dislocation networks and a high fraction of low-angle grain boundaries. Interestingly, twinning was observed, suggesting that the plastic deformation occurred through dislocation gliding and deformation twinning.
In this work, the effect of the nitriding process on microstructure and mechanical properties of additively manufactured (AM) 17-4PH stainless steel is investigated. The nitriding was performed at 530 °C, 560 °C, and 580 °C for 2 h. The nitriding process improves the hardness and surface roughness of the AM 17-4PH steel. Detailed microstructural characterizations of both as-built and nitride samples are performed using an optical microscope, scanning electron microscope (SEM) equipped with energy-dispersive X-ray spectroscopy (EDS), and X-ray diffraction technique. It reveals that the nitride layer thickness increases with nitriding temperature. A distinct transition layer between the substrate and nitride layer is observed in the 560 °C and 580 °C nitride samples. The nitriding process develops almost equiaxed grain microstructure with new secondary phase precipitates, whereas in the as-built material, the grains are primarily columnar along the AM process build direction. Specifically, the nitriding process introduces γ-Fe4N, ε-Fe3N, CrN, and Ni3N precipitates. The increase in Ni- and Cu-rich precipitates with the nitriding temperature explains the observed improvement in the hardness and surface roughness. Furthermore, the nitriding process does not alter the substrate's initial weak crystallographic texture.
The critical secondary phases along with critical temperatures in regard to the creep resistance of Grade 91 steel alloy were evaluated in order to optimize the composition of the alloy to improve the creep resistance. The critical temperatures which are highlighted in this paper are Ac1 (the threshold temperature in which austinite begins to form), and Ac3 (the threshold temperature at which ferrite is fully transformed into austenite). Furthermore, optimization was presented through various Cr, V, Nb, and N concentrations in Gr.91 in relation to these critical temperatures and the mole fraction of M 23 C 6 , MX, and Z-Phase as critical secondary phases. Here, the results indicate that increasing V and Nb will increase MX stability, whereas decreasing Cr and N lead to a decrease in Z-Phase and M 23 C 6 stability, which may lead to an increased creep resistance of the material. Upon further analyzing the V, Nb, and N concentrations in Gr.91, results indicate that increasing Nb content in the steel can lead to an increased stability of Nb-rich MX2 carbide which can be used to replace Z-Phase in low N steels. Simulation results indicate that elimination of N with a dramatic increase in Nb concentrations result in the stability increase of only M 23 C 6 and MX2 phases at temperature regions between 600 °C and 1370 °C and a complete removal of Z-Phase precipitation. Overall, with the elimination of N, which destabilizes Z-Phase precipitation, and an increase in Nb for NbC carbide stability, a possible solution to increasing both long-term and short-term creep resistance for Gr.91 can be achieved, though further optimization and creep experiments need to be conducted for final conclusions.
The extreme thermal gradients in additive manufacturing (AM) result in residual strains. Subsequent machining operations can relieve the AM strains but can introduce additional strains. AM and machining on the same platform, also called interleaving, during hybrid manufacturing has been shown to improve productivity. However, the impacts of these operations on the resulting residual strains are not well understood. In this investigation, neutron diffraction was used to measure residual strains in hybrid manufactured 316L stainless steel where blown-powder laser directed energy deposition (DED), and machining operations were interleaved in 1-inch intervals. We observe that the dominant texture developed during AM, resulting from different spot sizes and deposition parameters, significantly affects residual strain evolution. A dominant <011> texture along the build direction does not show any strain localization during interleaved deposition and machining whereas a dominant <001> texture shows strong tensile strain localization consistent with the pauses for machining at the end of AM step. Using coolant during machining can further alter the residual strain evolution. Our work shows that a combination of AM parameters and machining strategy can be used to influence the nature and magnitude of residual strains in components fabricated using convergent manufacturing technologies.
For this work, properties and radiation responses of a metastable high alloy (HEA) exhibiting the transformation induced plasticity (TRIP) effect were studied. Innovative engineering used to manufacture this HEA has shown superior mechanical and corrosion properties in 3.5% NaCl than most advanced stainless steels. The microstructural evolution and corresponding mechanical response after irradiation have been evaluated using detailed transmission electron microscopy, and nanoindentation. The study shows a change in metastability of the alloy with irradiation via a recovery mechanism, where the irradiation-induced transformation is reversed by the temperature-induced transformation, thereby introducing the concept of self-healing, made possible due to the TRIP behavior of HEA.
Here we studied the creep strengthening nanoprecipitation in novel heat resistant steels strengthened by a high density of stable nanoprecipitates. The results show that the high number density of MX nanoprecipitates within the martensitic laths in these steels comes from the great amount of MX former elements present in their chemical compositions and the high dislocation density generated by the martensitic transformation that takes place during cooling after austenitization. These dislocations act as nucleation sites for these nanoprecipitates during the tempering applied after the austenitization and quenching. Atom probe tomography measurements show that these nanoprecipitates are rich in Nb, V, Cr and N for the HDSN1 and HDSN2 steels and V, Cr and N for the HDSN3 steel. The distribution of the nanoprecipitates within the martensitic laths suggests that the creep strengthening produced by the Nb, V, Cr and N nanoprecipitates in the HDSN1 and HDSN2 steels is more effective at pinning dislocations at high temperature than that obtained in the HDSN3 steel by the V, Cr and N nanoprecipitates. However, the high amount of W in the HDSN3 steel enhances the solid solution strengthening at high temperatures, resulting in a similar high temperature strength for all HDSN steels.