Mechanical property-microstructure relationships of an X70 pipeline steel at elevated temperatures
Abstract not provided
Jacobs, T. R.↗
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Results for “microstructure property relationships”
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Microstructure-Property Relationships in Epitaxial Cu(In, Ga)Se2 Solar-Cell Absorbers
Epitaxially grown Cu(In,Ga)Se 2 (CIGS) absorber layers were analyzed by various techniques in scanning electron microscopy in order to reveal microstructure-property relationships in these thin films. Owing to their epitaxial nature, these CIGS absorber layers do not contain any grain boundaries, but only anti-phase domains (APDs) and dislocations. By combining electron channeling-contrast imaging, electron backscatter diffraction, and cathodoluminescence (CL), in some cases on identical specimen positions of polished cross-sections of CIGS/Mo/glass stacks, it was possible to correlate the presence and orientations of APDs and dislocations with the lateral distributions of the CL intensity and emission-peak energy. We studied CIGS layers with three different [Ga]/([Ga]+[In]) ratios as well as with and without NaF/KF treatments. Considerable differences between the CIGS layer properties in the microstructure-property relationships were found, depending on the growth parameters. Dislocations in the epitaxial CIGS layers do not tend to exhibit strong CL intensity decreases, which contrasts with the situation in numerous other semiconductor materials.
anti-phase domains↗
High-throughput, Ultra-fast Laser Sintering of Ceramics and AI Based Prediction on Processing-Microstructure-Property Relationships
We report high-throughput, ultra-fast laser sintering of alumina sample array and characterization of sample units’ microstructure and hardness, as a fast exploration of laser processing parameters, microstructure, and property. These experimental data were used to train machine-learning (ML) models. Accurate ML predictions were demonstrated for the processing-microstructure-property relationship, specifically in (1) prediction of the microstructure of alumina under arbitrary laser power and (2) prediction of the expected microstructure from the desired hardness. An independent neural network was developed and showed that ML-predicted microstructure had less than 10% error from real ones, in terms of projected hardness. To monitor the microstructure during laser sintering, we demonstrated an ML model that can instantaneously predict ceramic’s microstructure at the laser spot, based on the laser spot brightness. The ML model can generate more than 10 predictions per second, and the error in average grain size was less than 5% from the experimental observations.
36 MATERIALS SCIENCE↗
Understanding process-microstructure-property relationships in laser powder bed fusion of non-spherical Ti-6Al-4V powder
Powder feedstock is a major cost driver in metal additive manufacturing (AM). Replacing the spherical powder with the cost-efficient non-spherical one can reduce the feedstock cost up to 50% and attract more interest to adopt AM in production and new alloy development. Here, for this paper, a comprehensive study was conducted to understand process-microstructure-property relationships in laser powder bed fusion of hydride-dehydride Ti-6Al-4V powder. We demonstrated that variation of laser scan speed had a significant impact on the grain structure, pore evolution and properties compared to laser power. Dynamic X-ray radiography showed that with decreasing scan speed at a constant laser power, a transition from conduction to keyhole mode laser processing occurred, in which a deeper melt pool at lower scan speed intensified texture. In other words, an increase in laser scan speed resulted in formation of the refined prior β grains with shape factor of ~5, lowering the anisotropy. Furthermore, the degree of variant selection was evaluated based on the analyzed texture as a function of laser power and scan speed. With increasing laser scan speed, the dominant α/α boundary type was altered from type 2 to 4 and the degree of variant selection was noticeably decreased. On the other hand, increasing laser power left the morphology of prior β grains, their size, and the dominant α/α boundary (type 4) unchanged, while the texture and anisotropy were intensified, and the degree of variant selection was slightly decreased. Finally, dependency of surface roughness and microhardness were discussed as a function of laser processing parameters.
variant selection↗
Establishing model credibility for process-microstructure-property relationships in additive manufacturing using exascale computing
Additive Manufacturing (AM) of alloys holds significant promise as a disruptive technology in various industries, yet its adoption is often hindered by challenges in achieving consistent part quality. These issues are primarily due to the complex process-microstructure-property (PSP) relationships inherent to AM. Computational models can greatly aid in understanding these relationships, but their widespread impact and adoption has been limited by a lack of validated, open-source, and computationally efficient PSP modeling frameworks and hardware limitations. Here, this study leverages the ExaAM software suite and data from the AMBench-2018 series of laser powder bed fusion (LPBF) benchmark experiments to perform a comprehensive model assessment, including verification, validation, sensitivity analysis, and uncertainty quantification. The RADICAL-EnTK workflow manager was used to perform an ensemble of heat transport, solidification, and mechanical response simulations on the exascale computer Frontier, considering uncertainties in critical model inputs such as laser spot size and nucleation parameters, and consisting of 125 explicit grain structure simulations and 7875 crystal plasticity simulations. For a selected location within the Inconel 625 AMBench-2018 test artifact, sensitivity analysis and uncertainty quantification were performed using the predicted distributions of grain structure and mechanical properties. Qualitative agreement was found between the predicted grain size and texture and the observed AMBench-2018 microstructure, the mean predicted yield stress was within 5% of the experimental measurement mean, and the mean predicted engineering stress at 5% strain was within 10% of the experimental measurement mean. The insights gained from development and validation of the ExaAM PSP modeling framework will help guide future directions for enhancing the credibility and reliability of PSP models in AM, thereby accelerating the adoption of AM technologies in various industries.
Additive manufacturing↗
Exploring the Microstructure-Property Relationship of Mg-Al-Mn alloys enhanced via Friction Stir Processing
Friction Stir Processing (FSP) is a solid-state thermomechanical processing technique that enables local grain refinement, densification, and homogenization of microstructure in metallic components. Here, FSP is applied to thin sheets of high-pressure die cast AM60 to address the porosity and heterogeneities inherent to the casting process. In this work, microstructure characterization is correlated to improvements in the static mechanical properties of the Mg-Al-Mn alloy system at various FSP processing conditions. Notably, there is a significant increase in the ductility and ultimate tensile stress of system following FSP. The dominant deformation mechanisms for the as-cast material and the FSP material are investigated. Finally, the fatigue life improvement is correlated to microstructure modifications that may lead to distinct behavior in crack initiation and crack propagation for the as-cast and FSP material.
Garcia, David↗
Quantitative Characterization of the Microstructure-Property Relationships in Ni and MIEC Nanocatalyst-Infiltrated Ni/YSZ Anodes
The performance of solid oxide fuel cell (SOFC) anodes can be improved by infiltration of nanoscale electrocatalysts, which increases triple phase boundary (TPB) density, but only when the added TPBs are active. Quantitative characterization of the microstructure of infiltrated nanocatalysts remains a critical challenge in understanding their role in improved performance. This paper explores the relationship between electrochemical performance and microstructure for Ni/YSZ anodes infiltrated with Ni, gadolinium-doped-ceria (GDC), and Ni/GDC nanocatalysts using a combination of electrochemical impedance spectroscopy (EIS), distribution of relaxation times (DRT) analysis, scanning electron microscopy (SEM) microstructural characterization, and three-dimensional (3-D) anode reconstruction. Estimated polarization resistance improvements as a result of nickel nanoparticle infiltration are discussed.
Rix, Jillian↗
Process-Microstructure-Property Relationships in Low Heat Input Wire-Arc Additive Manufacturing (WAAM) of Ni-Based Superalloy Haynes 282
Conference presentation on WAAM process optimization on Ni-based superalloy Haynes® 282®. This alloy is targeted for advanced power generation systems for its superior high temperature mechanical properties. Wire-arc additive manufacturing (WAAM) offers attractive cost and materials savings with high deposition rates. Based on initial build process optimization, WAAM blocks were made, and screened for defects using high-throughput CT scanning. The microstructural evolution in arc energy range of 250-700 J/mm was characterized in the as-built and aged conditions, using electron microscopy. Results showed self-consistent microstructure across conditions, with grain size dependence on arc energy, MC and M6C carbides in as-built, and blocky grain boundary M23C6 in as-heat-treated condition. Further reductions in as-built porosity were explored through 20+ combinations of H2 and CO2 additions to shielding gas. Initial results show improved wetting behavior, and bead overlap with up to 1% H2 and up to 0.25% CO2. Combination of experimentally determined datasets provided comprehensive understanding of process-structure-property relationships in WAAM Haynes 282.
Haynes 282↗
Process-microstructure-property relationships in additively manufactured Fe-Si-B-Nb-Cu soft magnetic alloy
Explore the source record for details and available documents.
Barrick, Erin Jenna↗
Property-Microstructure Relationships in Several Additively Manufactured Ni-based Superalloys
Oral Presentation at ASTM 8th International Conference on Advanced Manufacturing 2023.
Sudbrack, Chantal↗
Machine learning insights into microstructural origins of transport and mechanical properties in porous microstructures
Multifunctional porous materials are increasingly needed across various fields, but their complex microstructures create significant challenges due to the intricate microstructure-property relationships. This complexity, combined with limitations of traditional analysis methods, hinders efforts to understand and optimize microstructure–property relationships. Here, to address this, we integrate physics-based mesoscale modeling with interpretable machine learning (ML) to uncover how microstructural features govern effective diffusivity and elastic modulus. At constant porosity, we show diffusivity varies by over 150 × and modulus by ∼50 ×, highlighting the power of microstructure engineering. Statistical analysis reveals bimodal behavior in diffusivity and unimodal in modulus. ML identifies connectivity as the dominant factor, while modulus is also sensitive to domain size and feature interactions. Controlled simulations further highlight domain shape as a critical feature for modulus. This framework enables efficient exploration of microstructure-property correlations, offering new insights to guide the design of advanced porous materials.
Bicontinuous microstructure↗
Using Artificial Microstructures to Understand Microstructure Property Relationship-Toughening Mechanisms in Metallic Glass (Final Report)
Metallic glasses are a new class of structural materials which exhibit exciting mechanical properties including high strength and elasticity. In terms of fracture toughness, the material class of metallic glasses spans a wide range; Some metallic glasses are extremely brittle and exhibit near ideal brittle behavior whereas others can be exceptional tough with values comparable to the toughest metals out there. Such large range of observed fracture toughness within the material class of metallic glasses is surprising as they have seemingly a very similar atomic structure. Therefore, we developed “artificial microstructures” which allows to decouple the various contribution of sample geometry, imperfection and structure. Specifically, we decouple variations in the alloys’ chemical composition and the atomic structure and quantified the resulting fracture toughness. Atomic structure of a metallic glass can be modified by the fictive temperature. The fictive temperature of a glass is the temperature at which the liquid metallic glass falls out of equilibrium upon colling and forms a glass. Upon further cooling the structure is maintained only thermal oscillations decrease due to a lower absolute temperature. We found that the effect of fictive temperature (same chemistry, different structural stages of the glass) is comparable to the variations of fracture toughness when the chemistry is varied. Hence, it appears that the subtle differences in the glass structure are responsible for the large range of fracture toughness’s observed. Our results reveal that fracture toughness within the material class of metallic glasses varies significantly and we found some example alloys with exceptional high resistance to fracture and others that are almost ideally brittle. Significant influences on the fracture toughness have the structure of the glass, its chemistry, and some imperfections in the structure.
36 MATERIALS SCIENCE↗
A macro-micro approach for identifying crystal plasticity parameters for necking and failure in nickel-based alloy haynes 282
Here, this work develops a two-scales macro-micro approach to address the challenge in calibrating crystal plasticity microstructural models when samples undergo necking prior to fracture. The crystal plasticity models are crucial for predicting the materials’ plastic deformation and failure at the microstructure level, identifying the materials’ intrinsic properties as well as investigating the microstructure-properties relationships. However, after necking occurs, the experimentally measured stress-strain curves fail to reflect the materials ‘true’ stress-strain behavior and cannot be directly fitted into crystal plasticity models. The proposed macro-micro approach employs a top-down strategy to address this challenge, which has been studied with experimental tests on precipitation-strengthened Ni-based superalloy Haynes® 282®. In this approach, a macro rate-dependent anisotropic plasticity model with Voce-type hardening and Rice-Tracey damage law is first utilized to model the deformation and failure of the tensile bar, and calibrated by matching the stress-strain curves, necking strain, and reduction of area. Especially, to match the testing results under different applied strain rates, the rate-sensitivity parameter m and saturation stress in the elasticity model are modified to incorporate dependence on the local strain rate. Then, the ‘true’ stress-strain behaviors are extracted from the necking zone of the macro-model, which are used to calibrate a micro-model with explicit microstructures and governed by an extended crystal plasticity law. The consistency between the micro-model and macro-model are enforced during calibration. The calibration outcomes from the crystal plasticity model elucidate the materials intrinsic properties for slip, hardening, and failure, which is vital for further investigations into the microstructure-properties relationship and for accurate prediction of the material behavior under various test and service conditions.
36 MATERIALS SCIENCE↗
Using post-processing heat treatments to elucidate precipitate strengthening of additively manufactured superalloy 718
The poor machinability and extensive work hardening of Ni-based superalloys makes additive manufacturing an attractive option for producing geometrically complex components with distinct microstructures. Although previous studies show recovery of high strength at room temperature, very few studies demonstrate successful properties at elevated temperatures required for industrial applications. Here, the objectives of this study are to present a post-build heat treatment for high strength across a wide temperature range, determine the strength contribution of nanoscale precipitating phases to the overall mechanical properties of superalloy 718, and from these, provide a comprehensive microstructure-property relationship for wrought and AM 718 to guide efforts to simulate the properties of AM components. Laser powder bed fusion–produced superalloy 718 was characterized at multiple length scales using scanning electron microscopy and transmission electron microscopy in the as-built condition and with multiple heat treatments designed to form combinations of γ', γ", and δ precipitates. Uniaxial tensile tests performed from room temperature to 600 °C on subsize specimens determined the yield strength, elastic modulus, ultimate tensile strength, fracture stress, and uniform elongation. Precipitates in this work proved to be weak barriers to dislocation motion through a dispersed barrier model, but they provided strength to the alloy through their consistent high density. The relative contribution to the yield strength from γ" remained consistent between 48% and 57% of the total strength up to 600 °C, the primary influence on the high temperature strength of superalloy 718. The strength factors for γ" and δ precipitates were found to trend inversely with tensile test temperature and may be attributable to the differences in precipitate coherency. A post-build heat treatment is recommended to maintain high strength at elevated temperatures. A quantitative microstructure-property relationship, dependent on precipitate size, density, and morphology, was derived and can estimate the yield strength across a wide temperature range applicable to the operational regimes for superalloy 718.
36 MATERIALS SCIENCE↗
Laser powder bed fusion of ODS Fe–Cr–Al (0.3Zr, 0.3Y 2 O 3 ): Unveiling processing-microstructure- mechanical property relationships
Here, this study investigates the fabrication of oxide dispersion strengthened (ODS) Fe-Cr-Al alloys via laser powder bed fusion (LPBF) with strategic additions of 0.3 wt% Zr and 0.3 wt% Y 2 O 3 for enhanced mechanical performance in nuclear applications. Systematic processing parameter optimization yielded three distinct conditions: one low-density product with significant defects and two near-full-density materials with improved consolidation. Comprehensive characterization confirmed single-phase α-ferrite matrix formation with successful incorporation of Y-, Zr-, O-, and C-rich precipitates characteristic of ODS alloys. However, precipitate density remained low (∼10 7 cm −3 ), resulting in sink strength values substantially below optimal levels for radiation resistance. Microhardness values (mid-200s HV) correlated inversely with grain size following the Hall-Petch relationship, indicating grain boundary strengthening as the dominant mechanism rather than precipitation strengthening. The optimized processing conditions achieved excellent mechanical properties with room temperature yield strength of approximately 500 MPa and 30 % elongation, demonstrating superior strength-ductility synergy compared to other additively manufactured ODS materials and performance consistent with literature values for LPBF-processed ODS-FeCrAl alloys. This investigation reveals both the potential and limitations of LPBF processing for ODS Fe-Cr-Al alloys. While successful defect-free fabrication was achieved, results highlight the critical need for systematic optimization of processing parameters and post-processing heat treatments to enhance precipitate density for effective dispersion strengthening and radiation resistance while maintaining additive manufacturing advantages.
Additive manufacturing↗
Machine-learning-assisted deciphering of microstructural effects on ionic transport in composite materials: A case study of Li 7 La 3 Zr 2 O 12 -LiCoO 2
The effective diffusivity of ionic species in multiphase materials is critical for the design and function of composite materials for electrochemical energy storage. In practice, effective diffusivity depends sensitively not only on the intrinsic diffusivities of constituting materials but also on their topological arrangement; nevertheless, these coupled contributions are oversimplified in most analytical models. Here, we combine atomistically informed mesoscale modeling and machine learning (ML) analysis to unravel how such features affect effective diffusivity in two-phase composites. Using the Li 7 La 3 Zr 2 O 12 -LiCoO 2 composite solid-state battery cathode as a model system, we compute effective diffusivity for 600 distinct dense polycrystalline microstructures with different topological configurations of grains, grain boundaries, and heterointerfaces. We verify that in addition to atomic-scale variabilities, microstructural feature diversity can significantly impact effective transport properties. Across the ensemble of test microstructures, this often results in bimodal distributions of effective diffusivity that encompass two qualitatively distinct operating mechanisms, which we identify via flux analysis. An ML approach reveals that the most critical determining factors for effective diffusivity are the connectivity of bulk phases and their heterointerfaces. The role of ionic mobility at the heterointerfaces is also discussed. These insights highlight the combined importance of microstructure and interface engineering in tuning the transport properties of ionic species in composite materials. In conclusion, our framework can also be extended for understanding generic microstructure-property relationships in other complex multiphase materials.
25 ENERGY STORAGE↗