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Poster presentation at the 2024 FECM/NETL Spring R&D Project Review Meeting on Location-specific microstructures and properties of Haynes 282 with laser-wire DED processing.
Here this study investigated the effect of laser powder bed fusion process parameters on the microstructure and properties of maraging steel. The results show that the process parameters can be finetuned to achieve both a high build rate/low hardness and low build rate/high hardness during fabrication, enabling the co-design of components where property gradients are desirable such as an injection molding tool where surface hardness is desired to be higher than the bulk, which needs to have higher fracture toughness.
Ultrasonic techniques for mechanical property characterizations are reviewed and conceptual models are advanced for explaining and interpreting the empirically based results. At present, the technology is generally empirically based and is emerging from the research laboratory. Advancement of the technology will require establishment of theoretical foundations for the experimentally observed interrelations among ultrasonic measurements, mechanical properties, and microstructure. Conceptual models are applied to ultrasonic assessment of fracture toughness to illustrate an approach for predicting correlations found among ultrasonic measurements, microstructure, and mechanical properties.
Understanding the microstructure–property relationships of porous media is of great practical significance, based on which macroscopic physical properties can be directly derived from measurable microstructural informatics. However, establishing reliable microstructure–property mappings in an explicit manner is difficult, due to the intricacy, stochasticity, and heterogeneity of porous microstructures. In this paper, a data-driven computational framework is presented to investigate the inherent microstructure–permeability linkage for natural porous rocks, where multiple techniques are integrated together, including microscopy imaging, stochastic reconstruction, microstructural characterization, pore-scale simulation, feature selection, and data-driven modeling. A large number of 3D digital rocks with a wide porosity range are acquired from microscopy imaging and stochastic reconstruction techniques. A broad variety of morphological descriptors are used to quantitatively characterize pore microstructures from different perspectives, and they compose the raw feature pool for feature selection. Here high-fidelity lattice Boltzmann simulations are conducted to resolve fluid flow passing through porous media, from which reliable permeability references are obtained. The optimal feature set that best represents permeability is identified through a performance-oriented feature selection process, upon which a cost-effective surrogate model is rapidly fitted to approximate the microstructure-permeability mapping via data-driven modeling. This surrogate model exhibits great advantages over empirical/analytical formulas in terms of prediction accuracy and generalization capacity, which can predict reliable permeability values spanning four orders of magnitude. Besides, feature selection also greatly enhances the interpretability of the data-driven prediction model, from which new insights into the mechanism of how microstructural characteristics determine intrinsic permeability are obtained.
GRCop-84 was developed to meet the mechanical and thermal property requirements for advanced regeneratively cooled rocket engine main combustion chamber liners. It is a ternary Cu- Cr-Nb alloy having approximately 8 at% Cr and 4 at% Nb. The chromium and niobium constituents combine to form 14 vol% Cr2Nb, the strengthening phase. The alloy is made by producing GRCop-84 powder through gas atomization and consolidating the powder using extrusion, hot isostatic pressing (HIP) or vacuum plasma spraying (VPS). GRCop-84 has been selected by Rocketdyne, Ratt & Wlutney and Aerojet for use in their next generation of rocket engines. GRCop-84 demonstrates favorable mechanical and thermal properties at elevated temperatures. Compared to NARloy-Z, the currently used inaterial in the Space Shuttle, GRCop-84 has approximately twice the yield strength, 10-1000 times the creep life, and 1.5-2.5 times the low cycle fatigue life. The thermal expansion of GRCop-84 is 7515% less than NARloy-Z which minimizes thermally induced stresses. The thermal conductivity of the two alloys is comparable at low temperature but NARloy-Z has a 20-50 W/mK thermal conductivity advantage at typical rocket engine hot wall temperatures. GRCop-84 is also much more microstructurally stable than NARloy-Z which translates into better long term stability of mechanical properties. Previous research into metal alloys fabricated by means of powder metallurgy (PM), has demonstrated that initial powder size can affect the microstructural development and mechanical properties of such materials. Grain size, strength, ductility, size of second phases, etc., have all been shown to vary with starting powder size in PM-alloys. This work focuses on characterizing the effect of varying starting powder size on the microstructural evolution and mechanical properties of as- extruded GRCop-84. Tensile tests and constant load creep tests were performed on extrusions of four powder meshes: +140 mesh (great3er than l05 micron powder size), -140 mesh (less than or equal to 105 microns), -140 plus or minus 270 (53 - 105 microns), and - 270 mesh (less than or equal to 53 microns). Samples were tested in tension at room temperature and at 500 C (932 F). Creep tests were performed under vacuum at 500 C using a stress of 111 MPa (16.1 ksi). The fracture surfaces of selected samples from both tests were studied using a Scanning Electron Microscope (SEM). The as-extruded materials were also studied, using both optical microscopy and SEM analysis, to characterize changes within the microstructure.
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Additive manufacturing has the potential to revolutionize industrial hardware and unlock efficiency gains through the fabrication of geometries and architectures not possible by conventional processing. Currently most additive builds use a single set of process parameters (e.g. laser power and scan speed) which results in a part with a homogenous microstructure that provides a singular performance level. To move beyond this state, Raytheon Technologies Research Center worked to create a set of computational tools to track material evolution through each step of the additive process. Computational fluid dynamics and phase field models for microstructure evolution as a function of processing parameters, and crystal plasticity models fully coupling microstructure and mechanical properties for performance predictions were leveraged to establish a connection between additive parameters and the final microstructure. This framework was utilized to tailor spatially-varying mechanical properties in a part by appropriately controlling the microstructure evolution during the additive process. Specifically, a turbine blade was 3D printed from nickel superalloy IN718 using laser powder bed fusion with coarse grains in the airfoil section which experiences the highest temperatures and is creep limited while finer grains were printed in the root of the blade which experience higher stresses but at lower temperatures and is therefore fatigue limited. The benefit of being able to intentionally insert coarse grains in the high temperature region of the blade was showcased with a microstructure sensitive creep model that indicates longer creep life for coarser grains.
Opportunities exist to expand the application of Zircaloy-4 (Zry-4) into lower-temperature (<100°C) applications. Welded areas within these structures are potential failure points and post-weld heat treatments (PWHTs) are not yet standardized for application in this temperature range. Zry-4 tungsten inert gas (TIG) welds were given PWHTs from 450-900°C with hold times from 0.5-48 hours to investigate changes to tensile properties and microstructure. Treatment of 800°C for 1 hour was found to produce the most ductile material, measured by total elongation of the tensile bar (16.5%). Furthermore, both PWHT time and temperature were observed to increase total elongation, except in extreme (>800°C or >18hours) cases, but the effect of temperature was stronger. The second order cumulative annealing parameter shows a trend with tensile properties. Average grain sizes in the fusion zone and heat affected zone did not change under any of the PWHTs until the development of an undesirable blocky-alpha phase, which was only observed to form in the heat affected zones for hold times greater than 18hr at 800°C.
Hf–Ta based alloys have recently been investigated as potential candidates for high-temperature structural applications. While most attentions have been given to the properties above ~1200 °C where the alloys are mainly single-phase BCC structures and have excellent oxidation performance due to formation of super-oxides, structural properties at lower temperatures are equally important for applications in which an alloy may experience a range of temperatures. In the present work, microstructure, phase composition, mechanical properties and oxidation behavior of six Hf-25Ta-5Me alloys (Me is Mo (HTM alloy), W (HTW), 0.5Mo + 0.5 W (HTMW), Cr (HTC) or Zr (HTZ), the compositions are in at.%) are reported at temperatures below the eutectoid transformation. The alloys were prepared by arc melting followed by hot isostatic pressing for 3 h at 1400 °C and 207 MPa. All the alloys display coarse grains of partially or fully transformed (by a eutectoid reaction) high-temperature BCC phase. The eutectoid regions consist of fine lamellae of Hf-rich HCP and Ta-rich BCC phases. The ternary alloys containing W or Cr also contained small amounts of a cubic Laves phase. At 25 °C, the HTW alloy was the strongest (yield stress σ y = 1966 MPa) but brittle and HTZ was the weakest (σ y = 1120 MPa) but ductile among the studied alloys. Other alloys showed intermediate behaviors. In general, the room temperature ductility of the alloys increased with decreasing σy. All alloys maintained high strength up to 800 °C, but displayed a noticeable strength decrease at 1000 °C. At 1000 °C, HTMW was the strongest alloy (σ y = 468 MPa) and HTC was the weakest alloy (σ y = 368 MPa). All alloys had excellent deformability at 1000 °C. Oxidation behavior of the alloys was studied at 800 °C and 1000 °C and compared with that of Hf–27Ta binary alloy. Although the ternary additions improved oxidation resistance, overall oxidation performance of the studied alloys at 800 °C and 1000 °C was poor.
An Al–10Ce-8Mn (wt%) alloy was designed and fabricated by laser powder bed fusion additive manufacturing (AM). The rapid cooling rates of the AM process produced a refined microstructure with a large fraction of reinforcing intermetallic phases. The tensile properties of the alloy were characterized in the as-fabricated state and following thermal exposure. The properties of the as-fabricated microstructure showed exceptional high-temperature performance and strength retention at elevated temperatures up to 400 °C relative to benchmark wrought Al and AM Al alloy properties. Characterization of the microstructure and thermodynamic modeling of the ternary Al–Ce–Mn system rationalized the solidification and solid-state phase transformations. Finally, analysis of the relevant strengthening mechanisms for both the as-fabricated and thermally exposed conditions was performed.
In laser powder bed fusion (LPBF), the in-situ process signatures are known to have a direct correlation with the microstructural properties of the solidified melt pool (MP). It is known that the MP cooling and heating rates, and laser processing parameters can critically determine the grain structure and thereby affect the part properties. The objective of this work is to study the feasibility of using in-process, high-speed imaging pyrometry for evaluating the solidified MP properties “below” the surface, such as depth and microstructural properties. To accomplish this, we employ an in-house single camera-based two-wavelength imaging pyrometry (STWIP) system for monitoring the printing of single-scan tracks with Inconel 718 on a commercial LPBF printer (EOS M290). Further, the lab designed STWIP system is a coaxial high-speed (>10,000 fps) imaging system capable of monitoring MP temperature, morphology, and intensity profiles. The temperature measurements from STWIP are emissivity independent. The STWIP measured MP signatures of the printed tracks are correlated with the ex-situ microscopy characterized MP depth and the average grain lengths. From the data analysis, using support vector machine (SVM)-based regression models, we found that the MP temperature signatures are crucial for an accurate prediction of MP depth and the grain length, thus validating the novelty and necessity of the developed in-situ monitoring methods and analysis.
This study presents a comprehensive experimental investigation on the fracture properties of hardened alkali-activated slag/fly ash (AASF) pastes in relation to the microstructure formation and reaction product composition. The main reaction product in AASF is C-(N-)A-S-H gel along with minor hydrotalcite phase, with the polymerization of C-(N-)A-S-H gel substantially governed by its Ca/Si ratio. Strong positive correlations are identified between the Ca/Si ratios of C-(N-)A-S-H gel and the fracture properties K{sub Ic} (J{sub tip}), whereas, the compressive strength of AASF pastes is primarily determined by its capillary porosity (>0.01 μm). The disagreements between the Ca/Si ratios and corresponding intrinsic mechanical properties of C-(N-)A-S-H gel as proof by contradiction indicate that the fracture properties K{sub Ic} (J{sub tip}) of AASF pastes could be dominated by a cohesion/adhesion-based mechanism. These findings provide promising guidance for fine-tuning the fracture properties of AASF and also advise on the tailoring strategies for high-performance composite such as strain-hardening geopolymer composite.
Overview -------------------------- This repository contains datasets from the manuscript **"Enhanced Generalizability to Deep-Learning Quantification of 3D Microstructural Characteristics through Microstructurally Aware Augmentation of Scarce Data"** (*William F. Kent, Rochan Bajpai, Rachel C. Kurchin, William K. Epting, Harry W. Abernathy, Paul A. Salvador. Submitted 2026*). The methods are also described in the dissertation **Data Intensive Analysis of Solid Oxide Cell Microstructures** (*Doctoral dissertation, Carnegie Mellon University, 2025*). The datasets here are trained convolutional neural network (CNN) models for predicting key microstructural properties of solid oxide cell (SOC) electrodes from low-res, 2-channel 3D images, as well as some helpful code. The parameters for input images are provided in the paper. Sample data is provided in the file `Combined_anode_aug_dual_1k_examples` - that particular data was used to train `anode_all_aug.pth` and will work most accurately with that model. Please familiarize yourself with all caveats on accuracy and applicability, as detailed in the associated paper. Usage -------------------------- The basic usage is as follows, assuming `model_fn` is the path to the .pth file, and `X` is 2-channel input image(s) of the proper dimensions (either one image of shape `[2,12,24,24]`, or a batch of N input images of shape `[N,2,12,24,24]`): from CNN_inferencer import load_model_for_inference model = load_model_for_inference(model_fn) y_predicted = model(X) The model object automatically handles input scaling and output de-scaling based on the way the models were trained - in other words, pass in a 2-channel micro-CT image, and it will output microstructural property values in real units. ## Other model object attributes Note that model has useful attributes other than its forward pass model(X). * `model.output_descaler` - returns the output descaler object. Model does the de-scaling when generating inferences, but you may want to re-use this de-scaler on other values to e.g. compare predictions to ground truth from already-scaled training data. * `model.prop_names` - Gives the property names of the predicted y values, in order. Only exists if there's an output scaler as part of the model object, which there will be in the models provided here. ## Usage with sample data Here is a short script to use with the included sample data. from CNN_inferencer import display_predictions, load_model_for_inference, calculate_mape, parity_plot import h5py import numpy as np model_fn = 'anode_all_aug.pth' data_fn = 'Combined_anode_aug_dual_1k_examples.h5' N_samples = 200 figure_outdir = '.' model = load_model_for_inference(model_fn) with h5py.File(data_fn,'r') as f: XX = f['X'] #These are the 2-channel 3D images yy = f['y'] #These are the ground-truth microstructural properties, but they have been scaled for training - need to de-scale below N = XX.shape[0] #How many images total in the input data file #Run inferences on N_samples random samples from XX. #Run in a batch, much more efficient than one at a time. ii = np.random.choice(N,N_samples,replace=False) ii.sort() y_pred = model(XX[ii]) #Get the original/true (but normalized/scaled) values from the training dataset... #Because they were normalized, they are not in real units yet. So let's also de-scale them using model.output_scaler. y_true = model.output_scaler.transform(yy[ii]) #Let's display actual values for just 5 random ones for i in np.random.choice(N_samples,5,replace=False): display_predictions(y_true[i], y_pred[i], model.prop_names) #Make parity plots for each property (ground truth vs predicted values) #Also label each plot with the mean abs. percent error (MAPE) of the predicted values for i,key in enumerate(model.prop_names): mape = calculate_mape(y_true[:,i], y_pred[:,i]) parity_plot(y_true[:,i], y_pred[:,i], figure_outdir, key, extra_title=f' ({mape:.2f}% MAPE)')
The room temperature physical and mechanical properties of silicon carbide fiber-reinforced reaction-bonded silicon nitride matrix composites (SiC/RBSN) were measured, and the composite microstructure was analyzed. The composites consist of nearly 24 vol% of aligned Hi-Nicalon SiC fiber yarns in a approx. 30 vol% porous silicon nitride matrix. The fiber yarns were coated by chemical vapor deposition with a 0.8 mm layer of boron nitride (BN) followed by a 0.2 mm layer of SiC. In the as-fabricated condition, both 1-D and 2-D composites exhibited high strength and graceful failure, and showed improved properties w en compared with unreinforced matrix of comparable density. No indication of reaction between the SiC fiber and BN coating was noticed, but the outer SiC layer reacted locally with the nitridation enhancing additive in the RBSN matrix. A comparison is made between the predicted and measured values of matrix cracking strength.
The room temperature physical and mechanical properties of silicon carbide fiber-reinforced reaction-bonded silicon nitride matrix composites (SiC/RBSN) were measured, and the composite microstructure was analyzed. The composites consist of nearly 24 vol% of aligned Hi-Nicalon SiC fiber yarns in a approx. 30 vo1% porous silicon nitride matrix. The fiber yarns were coated by chemical vapor deposition with a 0.8 micron layer of boron nitride (BN) followed by a 0.2 micron layer of SiC. In the as-fabricated condition, both 1-D and 2-D composites exhibited high strength and graceful failure, and showed improved properties when compared with unreinforced matrix of comparable density. No indication of reaction between the SiC fiber and BN coating was noticed, but the outer SiC layer reacted locally with the nitridation enhancing additive in the RBSN matrix. A comparison is made between the predicted and measured values of matrix cracking strength.
The purpose of this dissertation was the following: (1) to characterize the effect of pore fraction on a comprehensive set of electrical and magnetic properties for the yttrium-barium-copper-oxide (YBCO) high temperature ceramic superconductor; and (2) to determine the viability of using a room-temperature, nondestructive characterization method to aid in the prediction of superconducting (cryogenic) properties. The latter involved correlating ultrasonic velocity measurements at room temperature with property-affecting pore fraction and oxygen content variations. The use of ultrasonic velocity for estimating pore fraction in YBCO is presented, and other polycrystalline materials are reviewed, modeled, and statistically analyzed. This provides the basis for using ultrasonic velocity to interrogate microstructure. The effect of pore fraction (0.10-0.25) on superconductor properties of YBCO samples was characterized. Spatial (within-sample) variations in microstructure and superconductor properties were investigated, and the effect of oxygen content on elastic behavior was examined. Experimental methods used included a.c. susceptibility, electrical, and ultrasonic velocity measurements. Superconductor properties measured included transition temperature, magnetic transition width, transport and magnetic critical current density, magnetic shielding, a.c. loss, and sharpness of the voltage-current characteristics. An ultrasonic velocity image constructed from measurements at 1mm increments across a YBCO sample revealed microstructural variations that correlated with variations in magnetic shielding and a.c. loss behavior. Destructive examination using quantitative image analysis revealed pore fraction to be the varying microstructural feature.
This work explores technical feasibility in hot isostatic pressing (HIP) manufacturing of an integral bimetallic component using steel and Ni alloy powder for supercritical carbon dioxide (sCO2) turbomachinery. Lab-scale bimetallic HIP specimens using HAYNES® 282® and SS316L or SS415 powder are investigated in powder configuration, heat treatment, microstructure, and tensile properties up to 400 °C. Interdiffusion profiles at dissimilar alloy interfaces caused by HIP cycle is predicted by DICTRA simulations and validated by electron probe microanalysis (EPMA). The interdiffusion distance of most elements is around 100 μm, while C and N have a higher interdiffusion distance. Dense distribution of Ti-rich carbonitrides and alumina particles are found to decorate prior particle boundaries near joining interface on the 282 side, affecting tensile strength across interface as well as tensile failure location. A higher amount of excessive carbonitride formation near interface is observed in SS316L/282 than in SS415/282, which is consistent with the predicted greater degree of interdiffusion effect in SS316L/282. Typical HAYNES® 282® heat treatment condition is applicable to 282/SS316L and 282/SS415 combinations, resulting in a higher strength than cast CF8M and CA6NM. A pilot-scale bimetallic SS415/282 pipe is then demonstrated to show the promise of scaleup.
A large body of literature within the additive manufacturing (AM) community has focused on successfully creating stable tungsten (W) microstructures due to significant interest in their application for extreme environments. However, cracking and additional embrittling features at grain boundaries have resulted in poorly performing materials, stymying the application of AM as a manufacturing technique for W. Several alloying strategies, such as ceramic particles and ductile elements, have emerged with the promise to eliminate cracking while simultaneously enhancing stability against recrystallization. Here, in this work, we provide new insights regarding the defects and microstructural features that result from the introduction of ZrC for grain refinement and NiFe as a ductile reinforcement phase – in addition to the resulting thermophysical and mechanical properties. ZrC is shown to promote microstructural stability with increased hardness due to the formation of ZrO 2 dispersoids. Conversely, NiFe forms into micron-scale FCC phase regions within a BCC W matrix, producing enhanced toughness relative to pure AM W. A combination of these effects is realized in the WNiFe + ZrC system and demonstrates that complex chemical environments coupled with the tuning of AM microstructures provides an effective pathway for enabling laser AM W materials with enhanced stability and performance.