Microstructure Property Relationships Research Articles

Electrothermally actuated network metamaterials with reconfigurable bending deformation modes

Reconfigurable metamaterials with specific deformation modes show great promise in applications such as multifunctional antenna, stretchable electronic device, and reconfigurable soft robot, due to their ability to achieve multiple operational states within a single system. Previous researches on active metamaterials with bending deformation responses revealed two main issues: (1) achieving reconfigurable deformation within the same metamaterial is challenging due to the reliance on uniform external field actuation; and (2) there is a lack of in-depth studies on the microstructure-property relationships for the bending deformation responses of network metamaterials due to the lack of theoretical analysis. To address these issues, this study presents a mechanical design strategy for an electrothermally actuated network metamaterials to realize reconfigurable bending deformation. A theoretical model describing the electrothermally actuated bending deformation responses is developed through a three-level analysis, which offers a comprehensive understanding of the parameter-property relationships and accurately describes the bending deformation behaviors. The validity of these mechanical models is confirmed through finite element analyses (FEAs) and experimental results. These mechanical models provide analytical solutions for crucial mechanical quantities, including the electrothermally actuated bending angles and effective strains for bending deformation responses. The bending behaviors of the reconfigurable metamaterials under electrothermal actuation can be adjusted by the key nondimensional geometric parameters and the actuation strategies. Additionally, experimental results and FE calculations demonstrate that multiple bending responses can be realized within a single metamaterial by different actuation strategies. This study offers comprehensive guideline from theoretical predictions, FE calculations, and experimental demonstrations for future researched of reconfigurable metamaterials to realize required deformation behaviors.

Modeling complex polycrystalline alloys using a Generative Adversarial Network enabled computational platform

Creating statistically equivalent virtual microstructures (SEVM) for polycrystalline materials with complex microstructures that encompass multi-modal morphological and crystallographic distributions is a challenging enterprise. Cold spray-formed (CSF) AA7050 alloy containing coarse-grained prior particles and ultra-fine grains (UFG) and additively manufactured (AM) Ti64 alloys with alpha laths in beta substrates. The paper introduces an approach strategically integrating a Generative Adversarial Network (GAN) for multi-modal microstructures with a synthetic microstructure builder DREAM.3D for packing grains conforming to statistics in electron backscatter diffraction (EBSD) maps for generating SEVMs of CSF and AM alloy microstructures. A robust multiscale model is subsequently developed for self-consistent coupling of crystal plasticity finite element model (CPFEM) for coarse-grained crystals with an upscaled constitutive model for UFGs. Sub-volume elements are simulated for efficient computations and their responses are averaged for overall stress-strain response. The methods developed are important for image-based micromechanical modeling that is necessary for microstructure-property relations.

Unveiling the cellular microstructure–property relations in martensitic stainless steel via laser powder bed fusion

Unveiling the cellular microstructure–property relations in martensitic stainless steel via laser powder bed fusion

Crystallographic Study of Transformation Products of Heat-Affected Zone and Correlation with Properties of FH690 Heavy-Gauge Marine Steel by Multi-Pass Submerged Arc Welding

In this work, the microstructure–property relationship of the heat-affected zone (HAZ) of a FH690 ultra-heavy marine steel plate was investigated based on insight of microstructure and crystallographic features. After multi-pass welding with a heat input of ~30 kJ/cm, an ~8 mm wide HAZ was obtained with a coarse grain HAZ (CGHAZ) of ~3.8 mm, fine grain HAZ (FGHAZ) of ~3.4 mm, and intercritical HAZ (ICHAZ) of ~1 mm. High impact toughness values of ~120 and 140 J at -60 °C were obtained for coarse grain HAZ and fine grain HAZ, respectively. The microstructure of the CGHAZ and FGHAZ was fine lath bainite. Although the average prior austenite grain size for the CGHAZ was ~75 μm, which was five times that of the FGHAZ (15 μm), a high density of high-angle grain boundaries (HAGBs) with misorientation higher than 45° was obtained in the CGHAZ. This is the underlying reason for the excellent low-temperature toughness of the HAZ. Thermo-dynamic calculations indicated that the high density of HAGBs in the CGHAZ was attributed to the decreased bainitic transformation temperature due to the reduced phase transformation driving force via the high nickel addition, leading to weak variant selection. In addition, the high nickel addition offered high hardenability for high hardness in the FGHAZ. The outcome of this study could provide an experimental and fundamental basis for designing high-strength ultra-heavy steel plates with excellent weldability.

Machine-learning-assisted deciphering of microstructural effects on ionic transport in composite materials: A case study of Li7La3Zr2O12-LiCoO2

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 Li7La3Zr2O12-LiCoO2 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. Our framework can also be extended for understanding generic microstructure-property relationships in other complex multiphase materials.

Surface hardening of high modulus steels through carburizing and nitriding: First insights into microstructure property relationships

High modulus steels are promising materials for future lightweight design solutions, as their embedded boride particles in a ductile steel matrix increase the stiffness/density ratio. One key requirement for maturing them towards industrial application is their suitability for improving the surface hardness. In this study we investigated the effects of low-pressure carburizing and plasma nitriding on the microstructure and mechanical properties of selected Fe-TiB2- and Fe-Cr-M2B-based high modulus steels. Nitriding resulted with the formation of expanded ferrite in the strongest hardness increase to about 1100 HV0.05 from the alloy system's base hardness of 240 and 500 HV0.05, respectively, albeit with different hardness depth profiles. The Fe-Ti-B alloy indicated deformation phenomena in the ferritic matrix after nitriding, whereas nitriding of Fe-Cr-B-C resulted in a diffusion-controlled particle transformation of M2B borides into CrN nitrides of lower stiffness. Carburizing on the other hand led to a slightly lower maximum hardness value of about 800 HV0.05 over an increased depth for Fe-Cr-B, as martensite and additional M23C6 carbides were formed in the surface zone. The surface hardness of the Fe-TiB2-based alloy could not be increased by the deployed carburization parameters, most likely due to excessive Ti dissolved in the matrix. Consequences for the transfer to engineering applications as well as the refinement of both, thermochemical processing parameters and designated alloy concepts, of high modulus steels are outlined and discussed.

Three-dimensional phase field modeling, orientation prediction and stress field analyses of twin-twin, twin-grain boundary reactions mediated by disclinations in hexagonal close-packed metals

Crystalline defects, such as dislocations, disclinations, twins and grain boundaries, play critical roles in determining the mechanical properties of metals and alloys. In particular, with multiple competitive deformation modes activated, the mechanical behaviors of hexagonal close-packed metals are strongly influenced by the interactions and reactions of various types of defects. Despite extensive studies on the elastic interactions of defects, a theoretical framework capturing crystallographic reactions, especially reaction products and associated local stress concentration, is still unavailable. Here we suggest a disclination-based method to quantify defect reactions. By using a combination of crystallographic calculations and phase field modeling/simulations, twin-twin and twin-grain boundary reactions in hexagonal close-packed metals have been quantitatively analyzed. It has been found that partial disclinations, accompanied with other defects (e.g., {112¯6} and {112¯2} high-index twins), can be generated by defect reactions as typical byproducts. The orientation change and stress fields caused by disclination formation have been systematically calculated, which offers a rigorous mathematical foundation to explore twin-twin, twin-grain boundary reactions. By quantitatively determining defect reactions and local stress fields, our work provides new insights into the deformation mechanism and microstructure-property relationship in metallic materials.

Unraveling impacts of polycrystalline microstructures on ionic conductivity of ceramic electrolytes by computational homogenization and machine learning

The ionic conductivity at the grain boundaries (GBs) in oxide ceramics is typically several orders of magnitude lower than that within the grain interior. This detrimental GB effect is the main bottleneck for designing high-performance ceramic electrolytes intended for use in solid-state lithium-ion batteries, fuel cells, and electrolyzer cells. The macroscopic ionic conductivity in oxide ceramics is essentially governed by the underlying polycrystalline microstructures where GBs and grain morphology go hand in hand. This provides the possibility to enhance the ion conductivity by microstructure engineering. To this end, a thorough understanding of microstructure–property correlation is highly desirable. In this work, we investigate numerous polycrystalline microstructure samples with varying grain and grain boundary features. Their macroscopic ionic conductivities are numerically evaluated by the finite element homogenization method, whereby the GB resistance is explicitly regarded. The influence of different microstructural features on the effective ionic conductivity is systematically studied. The microstructure–property relationships are revealed. Additionally, a graph neural network-based machine learning model is constructed and trained. It can accurately predict the effective ionic conductivity for a given polycrystalline microstructure. This work provides crucial quantitative guidelines for optimizing the ionic conducting performance of oxide ceramics by tailoring microstructures.

Additively manufactured Nb-Ti-Si based alloy: As-built and heat-treated conditions

This work aims to fully characterise the Nb-Ti-Si based alloy (Nb-26Ti-16Si-2.2Al-2Cr), processed by the electron-beam powder-bed-fusion in both as-built and heat-treated conditions, to elucidate the microstructure-property relationships. The as-built condition has [001]-oriented columnar grains of the Nb3Si phase with the Nbss phase dispersed throughout the microstructure. The microhardness is 645.2 ± 6.7 HV0.5, and the indentation fracture toughness shows distinct directionality: 7.7 MPa·m1/2 in the horizontal direction compared to 5.3 MPa·m1/2 in the vertical direction. Both properties are comparable to the cast version. The directionality is attributed to the underlying mechanisms such as crack bridging, arrest, and micro-crack formation. By contrast, in the heat-treated condition, the alloy exhibits a dual-phase microstructure (Nbss and Nb5Si3 phases) with near-equiaxed grain shape due to the Nb3Si phase decomposition. The fracture toughness increases to 12.1 MPa·m1/2, at the expense of a reduced microhardness of 564.4 ± 15.0 HV0.5.

Effect of grain morphology and interface on the toughness of nacre-like aluminas

Ceramic composites that are both tough and strong are needed for extreme environments, ranging from aerospace and nuclear, to medical applications. Bioinspired ceramic composites tackle this challenge by mimicking tough natural structures. Nacre-like aluminas (NLAs) are ceramics processed using advanced colloidal techniques to obtain a microstructure resembling the brick-and-mortar structure of nacre. NLAs can achieve flexural strengths up to 600 MPa and fracture toughnesses ranging from 5 MPa.m1/2 up to 16 MPa.m1/2 after crack propagation. These high values mainly rely on deflection mechanisms; these can operate at temperatures up to 1200°C and under impact testing. The fracture properties have been found to be highly dependent on the mortar composition and processing technique used. However, we still don't know how far we can extend the structural properties of NLAs by changing their mortar strength and brick morphology. In this study, we combine mode I wedge splitting testing and in situ synchrotron X-ray microtomography of mixed-mode SENB fracture testing along with a kinked crack analytical formulation to study the microstructure-properties relationship in NLAs having two mortar compositions/morphologies. We compare the toughness values obtained in mode I and II with deflection models and study the effect of mode-mixity on the NLA toughness. Our findings show the grain morphology strongly influences the fracture behaviour, with the longer grain material showing longer stable crack deflection which could be further optimised going forwards. By contrast the mortar properties which provide deflection and local toughening mechanisms may be nearing their optimal strength.

Revisiting Andrews method and grain boundary resistivity from a computational multiscale perspective

The effective material response as observed at a macro level is a manifestation of the material microstructure and lower scale processes. Due to their distinct atomic arrangement, compared to bulk material, grain boundaries significantly affect the electrical properties of metals. However, a scale-bridging understanding of the associated microstructure–property relation remains elusive so that phenomenological approaches such as the Andrews method are typically applied. In the present contribution we revisit Andrews method from a computational multiscale perspective to analyse its limits and drive concepts to go beyond. By making use of homogenisation techniques we provide a solid theoretical foundation to the Andrews method, discuss its applicability and tacit assumptions involved, and resolve its core limitations. To this end, simplistic analytical examples are discussed in a one-dimensional setting to show the fundamental relation between the Andrews method and homogenisation approaches. Building on this knowledge the importance of the underlying microscale morphology and associated morphology-induced anisotropies are in the focus of investigations based on simplified microstructures. Concluding the analysis, scaling laws for isotropic microstructures are derived and the transferability of the results to realistic, (quasi-)isotropic polycrystals is shown.

Innovative construction of high-strength tungsten-copper joint through laser texture and nano-activation

Tungsten-copper (W-Cu) joints hold immense promise as plasma-facing materials in fusion reactors. However, the inherent immiscibility of W-Cu poses significant challenges in joint fabrication. Here, we introduce an innovative methodology that incorporates laser texture, W surface nano-activation, and subsequent diffusion bonding to fabricate W-Cu joints. Remarkably, the joints achieved exhibit unparalleled mechanical properties, with a peak tensile strength of 201 MPa and a shear strength of 141 MPa, surpassing previously reported W-Cu joints. To gain insights into the underlying mechanisms, we conducted a multiscale analysis utilizing scanning electron microscopy (SEM), high-resolution transmission electron microscopy (HRTEM), and density-functional theory (DFT) calculations. Our findings reveal a unique embedded structure and a metallurgically bonded interface at the W-Cu junction. Furthermore, the diffusion zone at the interface exhibits a fascinating hybrid crystal structure, maintaining a body-centered cubic (BCC) structure in certain regions while displaying a tetragonal crystal structure (with lattice parameters a=b=2.8617, c=3.44) in others. This tetragonal crystal structure formation within the W-Cu diffusion zone remains unexplored in previous literature. In summary, this novel W-Cu bonding approach not only offers a cutting-edge solution for modern manufacturing and fusion energy applications but also lays a solid theoretical foundation for understanding the intricate microstructure-property relationships in W-Cu systems.

An Explainable Deep Learning Model Based on Multi-scale Microstructure Information for Establishing Composition–Microstructure–Property Relationship of Aluminum Alloys

An Explainable Deep Learning Model Based on Multi-scale Microstructure Information for Establishing Composition–Microstructure–Property Relationship of Aluminum Alloys

Unravelling Effects of Microstructural Electrode Architecture on Cell Performance and Aging - a Combined Experimental and Simulation-Based Study on Commercial 21700 Lithium-Ion High-Energy Cells

In order to balance energy and power density of Li-ion batteries – and to ensure a long battery life – it is crucial to understand in detail how the composition and the microstructural design of the electrodes affects their capacity and kinetics.In our study we compare different commercial 21700 high-energy cells with very similar capacities (5 Ah) and energy densities (264 Wh/kg, 735 Wh/l) according to the supplier’s data sheets. However, in electrochemical tests, these cells showed significant differences with respect to rate capability and cyclic ageing. Detailed microstructural, chemical as well as electrochemical tests utilizing rebuilt half cells and symmetrical cells were done to identify microstructure-property relationships and the most critical microstructural parameters. In doing so, our study contributes to derive guidelines for optimized microstructural electrode design that combines energy with power and a long battery life.For material and microstructural analyses of fresh and aged cells, we used complementary methods such as optical and scanning electron microscopy, µCT- and FIB/SEM tomography, EDS and XRD to characterize the architecture of the electrodes at different length scales. We combined these results with results from detailed electrochemical studies such as OCV and half-cell tests with rebuilt cells as well as electrochemical impedance spectroscopy. Moreover, the 3D data from FIB/SEM tomography has been used as the input for microstructure-resolved simulations in the framework BEST, which allows for further investigation of the electrode microstructure and its effect on the electrochemical behaviour. In doing so, we gained a comprehensive dataset that allowed us to understand the different electrochemical behaviour of the different cells in the pristine state as well as their different behaviour during cyclic ageing.Some of the main findings are: (i) Different suppliers realize the same nominal capacity and energy densities of their cells by different electrode loadings; we observed differences of about 25 %; (ii) All suppliers strive to highly densify both, cathode and anode. Typical densities are 3.4 – 3.6 g/cm³ for the cathode and 1.5 – 1.6 g/cm³ for the anode. (iii) All investigated cells contain Si-rich phases within the anode, however, the chemical composition and the morphology is quite different and affects the electrochemical behaviour; (iv) We found significant differences in the binder-additive content; microstructure-resolved simulations indicate that this critically affects the electronic conductivity of the electrodes and thus, their electrochemical behaviour; (iv) The rate capability of the cells is significantly affected by the areal capacity of the electrodes (and presumably, their electronic conductivity); (vi) This in turn affects the cyclic aging behaviour of the cells. The investigated cells exhibit significant differences under various cycling protocols; we varied upper and lower cut-off voltage as well as the C-rate to investigate how microstructural features affect aging under different conditions.Within the talk, we will provide detailed insights into the results briefly outlined above and try to put the individual pieces of the puzzle together into an overall picture to elucidate how different microstructural features in combination determine the performance and cycling stability of the cells. Figure 1

Effects of standard heat treatment on micro-to nanostructure heterogeneities in a Rene 65 Ni-based superalloy billet

Advancements in the manufacturing of aeroengine components with superior mechanical properties rely on the microstructure homogeneity of industrially forged Ni-based superalloys. Billets often contain microstructural heterogeneities that can persist throughout the process, potentially compromising the integrity of the final product. Rene 65 is a relatively new superalloy with high-temperature mechanical properties superior to those of Alloy 718. However, systematic studies on its microstructure-property relationships are scarce. Here, we correlate the microstructural evolution with the hardness of a Rene 65 billet subjected to industrial processing and standard heat treatments. Multiscale microstructural heterogeneities, including grain size, geometrically necessary dislocations, twin boundary density, and the multimodal distribution of γʹ precipitates, are characterised. Grain growth occurs near the billet centre. Precipitation varies with radial position, showing up to a two-fold change in primary and secondary γʹ fractions. Tertiary γʹ precipitates exhibit variations in morphology and chemical composition, with Cr and Co enrichment near the billet surface. These heterogeneities arise from varying strain distributions and cooling rates during radial forging. Sub-solvus annealing and ageing are shown to mitigate these heterogeneities by promoting recrystallisation, recovery, and homogeneous precipitation of tertiary γʹ. A strengthening model elucidates the active mechanisms in as-received and heat-tread conditions, predicting hardness with a deviation of <8 %. Analyses of grain boundaries and γ/γʹ interfaces reveal strong segregation of Mo, W, Nb, C and B before and after heat treatment, with Mo and B showing the highest interfacial excess values. Insights into the microstructural evolution and its impact on properties could guide the development of next-generation aerospace parts.

Chloride transport in alkali-activated materials influenced by different reaction products: a review

Alkali-activated materials (AAMs) are regarded as a substitute for Portland cement. They have high chloride resistance and a low carbon dioxide footprint. The aim of this review is to provide a multi-scale perspective to understand material–product–microstructure–property relationships in terms of the chloride binding behaviour of AAMs. The physical and chemical chloride stability of different reaction products is summarised from nanostructure, to microstructure to macro properties. An analysis of studies in the literature gives an overview of recent progress in chloride transport in AAMs influenced by different reaction products. Results show that a higher calcium/silicon, aluminium/silicon molar ratios and alkali content increase the formation of amorphous phases, leading to a denser microstructure and lower chloride penetration in AAMs. Higher magnesium oxide and aluminium oxide contents result in increased formation of hydrotalcite. The enhanced physical and chemical absorption of chloride by hydrotalcite leads to higher resistance of chloride penetration in AAMs. Investigation of increasing chloride resistance could potentially focus on increasing gel and hydrotalcite formation.

Understanding Processing–Microstructure–Property Relationships of Structural Alloys

Although intensively investigated for centuries, structural alloys, especially steels, continue to attract a great deal of research interest [...]

Integrated Framework to Model Microstructure Evolution and Decipher the Microstructure-Property Relationship in Polymeric Porous Materials.

Unraveling the microstructure-property relationship is crucial for improving material performance and advancing the design of next-generation structural and functional materials. However, this is inherently challenging because it requires both the comprehensive quantification of microstructural features and the accurate assessment of corresponding properties. To meet these requirements, we developed an efficient and comprehensive integrated modeling framework, using polymeric porous materials as a representative model system. Our framework generates microstructures using a physics-based phase-field model, characterizes them using various average and localized microstructural features, and evaluates microstructure-aware properties, such as effective diffusivity, using an efficient Fourier-based perturbation numerical scheme. Additionally, the framework incorporates machine learning methods to decipher the intricate microstructure-property relationships. Our findings indicate that the connectivity of phase channels is the most critical microstructural descriptor for determining effective diffusivity, followed by the domain shape represented by curvature distribution, while the domain size has a minor impact. This comprehensive approach offers a novel framework for assessing microstructure-property relationships in polymer-based porous materials, paving the way for the development of advanced materials for diverse applications.

Computationally guided alloy design and microstructure-property relationships for non-equiatomic Ti–Zr–Nb–Ta–V–Cr alloys with tensile ductility made by laser powder bed fusion

Single-phase body-centered cubic refractory complex concentrated alloys (RCCAs), exhibit strength retention at temperatures above the melting points of conventional Ni-based superalloys. However, their lack of room temperature tensile ductility leads to cracks during processing and/or premature failure under mechanical loading when fabricated by laser-based metals additive manufacturing (AM). We present an alloy design framework for tensile ductility in non-equimolar RCCAs amenable to AM processing within the Ti–V–Cr–Nb–Zr–Ta system. First, density functional theory (DFT) and machine learning informed screening of alloy compositions were used to down-select ∼106 alloys with potential for tensile ductility. Next, Scheil solidification modeling was used to eliminate compositions most at risk for micro-segregation and solidification cracking based on the estimated freezing range of each alloy. This led to the design of two new, representative alloys: Ti0.4Zr0.4Nb0.1Ta0.1 and Ti0.486V0.375Ta0.028Cr0.111. These alloys were evaluated for rapid solidification defect formation using in-situ synchrotron melt pool imaging, fabricated via laser powder bed fusion (L-PBF) AM, and then characterized for processing defects, microstructure, and mechanical properties. Overall, both alloys achieved tensile yield strengths >800 MPa and failure strains >5 %. However, the occurrence of un-melted powders of high melting point elements likely resulted in premature failure during tensile straining. Considerations about mitigating this defect source are discussed and the role of local micro-segregation on ductility are elucidated. Overall, these results highlight the success of our framework, and show potential for laser-based AM-RCCAs with tensile ductility.

Demystifying ionic and electronic transport in prominent oxide ion and proton conducting ceramic electrolytes: Ce0.8Sm0.2O2 and BaCe0.7Zr0.1Y0.1Yb0.1O3-δ

ABSTRACT The development and performance of ceramic electrochemical cells (CECs) depend critically on choosing ceramic electrolytes with optimal ionic and electronic transport properties. In this study, we compare the structural, morphological, and transport characteristics of two representative ceramic electrolytes, oxide ion conducting Ce0.8Sm0.2O2 (SDC) and proton conducting BaCe0.7Zr0.1Y0.1Yb0.1O3-δ (BCZYYb). We demonstrate that SDC exhibits higher bulk conductivity but lower open-circuit voltage due to its higher electron density and larger crystallite size. In contrast, BCZYYb possesses higher proton conduction, lower activation energy (0.35–0.38 eV), and higher open-circuit voltage due to its lower electron density and smaller crystallite size with more grain boundaries. BCZYYb shows enhanced proton conductivity in humidified atmospheres due to the hydration of oxygen vacancies, making it more suitable for low-temperature CEC applications. These insights into ceramic electrolyte conduction mechanisms and microstructure-property relationships can guide the optimization of CECs for energy applications.