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Novel methods to increase performance of steels without increasing production costs are always sought after. In this study, a chemistry–process–structure–property paradigm was employed to modify rolled homogeneous armor (RHA) steels that contain no nickel (Ni) or chromium (Cr) and instead, microalloying with niobium (Nb). Characterization of 0.02 and 0.05 wt.% Nb additions was targeted in tandem with optimized heat treatments to improve performance without detrimentally increasing carbon equivalence. Designed alloys were cast in a vacuum induction melting furnace and thermo-mechanically processed. Optimal heat treatment conditions were determined with computational simulation software, JMatPro, for materials properties, which resulted in an exploration of three tempering temperatures, 400, 500, and 600 °C. Microstructures were investigated by optical and electron microscopy, where Nb additions were determined to reduce average grain area of ferrite, bainite, and martensite by as much as 75, 59, and 58%, respectively. Hardenability was characterized by Jominy end-quench testing and showed a slight increase in average hardness for Nb-bearing alloys due to the precipitation of NbC. Compression and tension tests revealed minimal strain rate sensitivity and a nearly 30% increase in strength in comparison with reference RHA materials. Impact tests showed a moderate increase in energy absorption of up 61%. Fractography of the failed specimens highlighted MnS precipitates as well as NbC and Mo2C carbides. Overall, results showed small (≤ 0.05 wt.%) additions of Nb accompanied by an optimized heat treatment resulted in a modified, less-expensive RHA with similar performance to typical Ni/Cr-RHA.

The ability to experimentally synthesise ceramic materials to incorporate nanotwinned microstructures can drastically affect the underlying deformation mechanisms and mechanics through the complex ...

Process optimization of Laser-Based Additive Manufacturing (LBAM) systems is often complicated by the tradeoff between different mechanical properties as well as the relative density window. For instance, parts with similar relative densities can have noticeably different tensile mechanical properties (e.g., elongation-to-failure, yield strength, ultimate tensile strength, Young’s modulus). This phenomenon can be attributed to the variation of size and distribution of fabrication-induced voids within the final parts. To overcome the aforementioned challenge, we applied an efficient sequential multi-objective process optimization framework to optimize the quality of LBAM-fabricated parts with respect to multiple non-correlated mechanical properties within the optimal relative density regime. The applied Multi-objective Accelerated Process Optimization (m-APO) method indirectly accounts for the effect of size and distribution of voids on the final parts’ mechanical properties. The m-APO decomposes the master multi-objective optimization problem into a sequence of single-objective sub-problems constructed from mathematically convex combination of individual unknown objective functions. At each step, the m-APO smartly maps the experimental data from previous sub-problems to the remaining sub-problems. Therefore, the information captured from previous sub-problems is leveraged to accelerate the master multi-objective process optimization problem. The m-APO exhibited capability to achieve a set of process parameter setups, resulting in the best trade-off between conflicting mechanical properties in the optimal window. The m-APO methodology is employed to maximize relative density and elongation-to-failure of Ti-6Al-4 V parts fabricated by Selective Laser Melting (SLM) system. The results show that the m-APO achieved the optimal process parameter setups while reducing the time-and cost-intensive experiments by 51.8%, compared with an extended full factorial design of experiments plan.

Magnesium alloys are good candidates for a number of applications due to their high strength-to-weight ratio, but other properties like corrosion resistance, formability, and creep are still a concern. In magnesium-aluminum alloys, M17Al12 phase precipitates at the grain boundaries (GBs) can have important implications on the mechanical and corrosion behavior. In order to better understand the effects, atomistic segregation of aluminum to GBs must be evaluated first. This study uses atomistic simulations to quantify aluminum segregation energetics for training a machine learning model. Aluminum atoms were iteratively placed at various atomic sites near 30 different 〈0001〉 symmetric tilt grain boundaries (STGBs) in magnesium. The results show how aluminum segregation is affected by GB structure and the local atomic environment. The ability to compute grain boundary physical properties of interest using machine learning techniques can have broad implications for the area of grain boundary science and engineering.

Sampling minimum energy grain boundary (GB) structures in the five-dimensional crystallographic phase space can provide much-needed insight into how GB crystallography affects various interfacial properties. However, the complexity and number of parameters involved often limits the extent of this exploration to a small set of interfaces. In this article, we present a fast Monte Carlo scheme for generating zero-Kelvin, low energy GB structures in the five-dimensional crystallographic phase space. The Monte Carlo trial moves include removal and insertion of atoms in the GB region, which create a diverse set of GB configurations and result in a rapid convergence to the low energy structure. We have validated the robustness of this approach by simulating over 1184 tilt, twist, and mixed character GBs in both fcc (Aluminum and Nickel) and bcc ( α -Iron) metallic systems.

An investigation into the general and pitting corrosion rates of rolled homogeneous armour (RHA) steel in immersion and salt-fog environments is presented. Although the mechanical properties of RHA...

Virtual X-ray diffraction (XRD) and atomistic simulations are used to probe the relationship between XRD phenomena and the strain accommodation methods associated with deformation. Twinned and untwinned nanocrystalline (NC) samples loaded in tension reveal distinct XRD responses. In initially untwinned samples, peak splitting occurs precisely as dislocation mediated deformation mechanisms initiate at approximately 2.9% strain. However, initially pre-twinned samples reveal less dislocation mediated deformation and no observable XRD peak splitting. XRD responses from control sets of ideal defect structures representing bulk and unloaded NC samples are analyzed. This study shows that the peak splitting during deformation of the initially untwinned NC sample can be traced to both the high density of planar defects and the complex internal strain state present under external load.

Additive manufacturing (AM) processes are subject to lower stability compared to their traditional counterparts. The process inconsistency leads to anomalies in the build, which hinders AM’s broader adoption to critical structural component manufacturing. Therefore, it is crucial to detect any process change/anomaly in a timely and accurate manner for potential corrective operations. Real-time thermal image streams captured from AM processes are regarded as most informative signatures of the process stability. Existing state-of-the-art studies on thermal image streams focus merely on in situ sensing, feature extraction, and their relationship with process setup parameters and material properties. The objective of this paper is to develop a statistical process control (SPC) approach to detect process changes as soon as it occurs based on predefined distribution of the monitoring statistics. There are two major challenges: 1) complex spatial interdependence exists in the thermal images and current engineering knowledge is not sufficient to describe all the variability, and 2) the thermal images suffer from a large data volume, a low signal-to-noise ratio, and an ill structure with missing data. To tackle these challenges, multilinear principal component analysis (MPCA) approach is used to extract low dimensional features and residuals. Subsequently, an online dual control charting system is proposed by leveraging multivariate T 2 and Q control charts to detect changes in extracted low dimensional features and residuals, respectively. A real-world case study of thin wall fabrication using a Laser Engineered Net Shaping (LENS) process is used to illustrate the effectiveness of the proposed approach, and the accuracy of process anomaly detection is validated based on X-ray computed tomography information collected from the final build offline.

We present a systematic approach to refine hyperdimensional interatomic potentials, which is showcased on the ReaxFF formulation. The objective of this research is to utilize the relationship between interatomic potential input variables and objective responses (e.g., cohesive energy) to identify and explore suitable parameterizations for the boron carbide (B–C) system. Through statistical data analytics, ReaxFF’s parametric complexity was overcome via dimensional reduction (55 parameters) while retaining enough degrees of freedom to capture most of the variability in responses. Two potentials were identified which improved on an existing parameterization for the objective set if interest, showcasing the framework’s capabilities.

Tailoring microstructure evolution with the use of external forces (magnetic, electric, mechanical, etc.) can expand the ability to control material properties and performance through microstructure engineering. In particular, crystalline materials are comprised of a distribution of grains of differing sizes, crystallographic orientations (texture), and topologies, which may evolve differently under the influence of various fields and lead to changes in material properties, bounded by movable grain boundaries. In this work, simulations are used to demonstrate the effect of an applied field, in this case a magnetic field, on the evolution of microstructure at the grain scale. The simulation results show that fields can be used to control microstructure geometry and texture. They also show that a dynamic field application of the field, through a changing field direction over time, can create new microstructure features even in a system that does not seem as sensitive to as static field, and that temperature can play a key role in this evolution as well. This work overall demonstrates how unconventional processing techniques on polycrystalline microstructures can be impacted by applying fields, and ultimately be used to improve properties and performance through engineered microstructures.

The mechanical properties of the weld regions of a 6061-T6 resistance spot welded lap joint are determined. The change in mechanical properties resulting from RSW are linked to the changes observed in the microstructure. Processing currents and strain rates are varied to probe the effects of processing temperature at strain rates from 10−3 to 103 s−1. Results show that material strength decreases within the heat affected zone (HAZ) and fusion zone due to precipitate dispersion. Further, decreased ductility results at quasi-static strain rates from accelerated crack growth arising near voids formed during weld formation, but the short time scale at higher strain rates limits the ability for crack growth from these voids allowing the material to exhibit higher ductility. Overall, significant changes in the mechanical behavior across the weld resulting from a change in microstructure congruent with precipitate dispersion are apparent for all processing conditions.

The objective of this study is to investigate the relationship between the melt pool characteristics and the defect occurrence in an as-built additive manufacturing part. One of the major detrimental microstructure properties associated with additive manufacturing (AM) is porosity within final parts. State-of-the-art porosity detection methods focus primarily on post-manufacturing approaches that are susceptible to high cost of process, longer process time, and are incapable of characterizing pores during fabrication. A real-time porosity prediction method is developed using morphological characteristics of the melt pool boundary (i.e., features obtained via functional principal component analysis (FPCA)). A thermal monitoring system is used to capture the time-varying melt pool signal, which are labeled as either pores or normal melt pools by X-ray tomography. Supervised learning methods are utilized to identify the patterns of melt pool images and build a black-box model for the probability distribution of class labels (namely, porosity) based on data characteristics of predictors (e.g., melt pool characteristics). The resultant model does not depend on specific design of specimens with varying material properties; and can be effectively developed as long as thermal-porosity data can be obtained. In the current study, multiple supervised machine learning approaches are used to classify melt pools to predict porosity in a part. Two different accuracy measures are used and numerical experiments show that among the classification approaches used (i.e., Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA)), KNN results in the highest rate of accurately classifying melt pools (98.44%). However, DT results in the lowest rate for incorrectly identifying normal melt pools as pores (0.03%). A comparative study is conducted that compares the performance of supervised learning methods leveraging the proposed morphological model and simple metrics of the melt pool. Numerical experiments show that the morphological model combined with supervised learning techniques vastly outperform the simple melt pool metrics combined with supervised learning techniques (approximately 250% better performance for correctly predicting abnormal melt pools). Our approach may potentially be applied to other AM processes that share similar energy-material interactions (e.g., powder bed fusion, electron beam melting).

Magnesium alloys are good candidates for a number of applications due to their high strength-to-weight ratio, but other properties like corrosion resistance, formability, and creep are still a concern. In magnesium-aluminum alloys, Mg17Al12 phase precipitates at the grain boundaries (GBs) can have important implications on the mechanical and corrosion behavior. In order to better understand the effects, atomistic segregation of aluminum to GBs must be evaluated first. This study uses atomistic simulations to quantify aluminum segregation energetics for training a machine learning model. Aluminum atoms were iteratively placed at various atomic sites near 30 different 〈 0001 〉 symmetric tilt grain boundaries (STGBs) in magnesium. The results show how aluminum segregation is affected by GB structure and the local atomic environment. The ability to compute grain boundary physical properties of interest using machine learning techniques can have broad implications for the area of grain boundary science and engineering.

Owing to its ability to incorporate Schmid’s law at each integration point, crystal plasticity has proven a powerful tool to simulate and predict the slip behavior at the grain level and the ensuing heterogeneous stress/strain localization and texture evolution at the macroscopic level. Unfortunately, notwithstanding substantial efforts during the last three decades, this remarkable capability has not been replicated for materials where twinning becomes a noticeable deformation mechanism, namely in the case of low-stacking fault energy cubic, orthorhombic, and hexagonal close-packed structures. The culprit lies in the widely adopted unphysical pseudo-slip approach for capturing twin formation. While the slip is diffuse, twinning is a localized event that occurs as a drastic burst of a confined number of partial twinning dislocations establishing an interface that pursues growth through a thread of perfect twinning dislocations in the sense of bicrystallography. Moreover, at earlier stages, twin nucleation may require atomic diffusion (Shuffling) and faceting, generally demanding higher stress levels not necessarily on the twin shear plane, while triaxiality at adequate sites might be needed or preferred such as lower grain boundary misorientations or other twin boundaries. Identifying a mathematical framework in the constitutive equations for capturing these twin formation sensitivities has been a daunting challenge for crystal plasticity modelers, which has stalled ameliorating the design of key hexagonal materials for futuristic climate change-related industries. This paper reviews existing approaches to incorporating twinning in crystal plasticity models, discusses their capabilities, addresses their limitations, and suggests prospective views to fill gaps. The incorporation of a new physics-based twin nucleation criterion in crystal plasticity models holds groundbreaking potential for substantial progress in the field of computational material science.

Molecular dynamics (MD) simulations are used to investigate the role of size and distribution of nanoscale Cu/Ta interfaces on the nucleation and evolution of defects during shock loading and spall failure of nanocrystalline (nc) Cu/Ta alloys. Cu/Ta interfaces are introduced through the embedding of Ta clusters in nc-Cu matrix. The phase stability of the embedded Ta clusters either as FCC or BCC clusters is first investigated and reveals that the FCC Ta clusters have a lower energy for diameters less than 4 nm, whereas the BCC Ta clusters have a lower energy for the larger diameters. The shock simulations are then carried out for Ta clusters with an average diameter of 1 and 3 nm and concentrations of 3.0, 6.3 and 10.0% to investigate the role of size and distribution of Cu/Ta interfaces (due to presence of clusters) on the nucleation and evolution of dislocations as well as the spall strength of the alloy. The MD simulations indicate that the Cu/Ta interfaces reduce the capability of nc-Cu to accommodate plasticity through nucleation of dislocations and create void nucleation sites during spallation. The MD simulations further reveal that the impact strengthening effects due to the presence of nanoscale Cu/Ta interfaces are strongly dependent upon the size and distribution of Ta clusters, as well as the grain size of Cu matrix. Smaller size of interfaces (cluster size), higher concentration of Ta (smaller spacing between interfaces) and larger matrix grain size render higher spall strengths of nc-Cu/Ta microstructures.

Capturing twin nucleation in full-field crystal plasticity is a long-standing problem in materials science modeling. Pronounced efforts have been deployed to understand the nucleation event at the atomic level. Yet, it remains very challenging to appropriately scale up the inherently discrete mechanisms to the continuum scale. However, of significant implications for modeling of hardening and damage is not the embryonic/lamellar nucleation of an individual twin per say, but the spacing of the twins and their concomitant interactions with each other and active defects in the lattice. Thus, knowing when and where the next twin would nucleate or grow is certainly of significant consideration to crystal plasticity and would have been perhaps the primary goal of any model which would capture early site-specific nucleation. In this study, we develop an analytical model based on a micromechanical formulation, which is able to pinpoint where a second twin nucleates in an idealistic scenario in a homogeneous material. The model was probed with a three-point bending boundary value problem and predictions were compared to in-situ bending tests of an AZ31 Mg alloy. The alloy was chosen because it exhibited sharp textures which tend to develop twin patterns similar to single crystals. The analytical expression for the stress field was obtained for an ellipsoidal twin in an isotropic half-space. We show that the twin spacing depends primarily upon the height of the twin band, and the stress relaxation from twinning depends primarily upon the thickness of the twin domain. The solution compared well to experimental results.

The physics of embrittlement is dictated by the various interactions between the impurities/defects and the local structure in polycrystalline material systems. In this study, a physically motivated model that describes the degree of interaction of hydrogen (H) defects on the segregation behavior to α-Fe grain boundaries (GBs) is developed. Molecular statics simulations were performed to quantify the segregation behavior of 1–2 H atoms at various interstitial sites around the , , , and symmetric tilt GBs. The results provide insights into the concentration profile of hydrogen defects along different GBs. Furthermore, the model accurately links the intrinsic GB character by quantifying the segregation length scale for the individual GBs based on the segregation behavior of defects. Finally, the metrics provided in this work are essential to comprehensively understanding the effect of hydrogen on the macroscopic behavior of α-Fe.

Predicting grain growth in nanocrystalline materials requires modeling approaches that incorporate grain boundary thermodynamics and kinetics. In this work, the thermokinetic model of Chen et al. (2012) for grain growth was applied to experimental X-ray diffraction measurements from a binary nanocrystalline alloy in an effort (1) to understand the influence of thermodynamic, kinetic, and material parameters in the model; and (2) to extend the thermokinetic model by incorporating temperature dependence. The model performs well for the grain boundary saturated case in the binary nanocrystalline alloy, where it is assumed that solute segregates to the grain boundaries and thermodynamically/kinetically reduces the driving force for grain growth. In this work, a sensitivity analysis of parameters (Monte Carlo global sensitivity analysis) identifies the important thermodynamic/kinetic parameters and their correlation with one another for the present model. This model was then extended to include the change in these independent thermodynamic/kinetic parameters as a function of temperature and to model the effect of initial grain size distribution. This research shows that the thermodynamic and kinetic contributions can describe grain growth in nanocrystalline materials and this extended model can be parameterized for grain size evolution and stabilization with temperature for nanocrystalline systems.

Basal slip and dynamic recovery of dislocations have been long assumed as the main mechanisms accounting for the sharp texturing of magnesium alloys upon dynamic recrystallization at high temperature. Basal slip allows the basal plane to mitigate stress by reorienting the 〈 c 〉 -axis normal to the loading direction, while dynamic recovery allows dislocations to rearrange into subgrains while maintaining the basal plane parallel to the main loading axis. These phenomena, though pertaining to unquestionable laws of plasticity and thermodynamics, do not fully explain the preferred selection of crystal orientation during nucleation and growth of dynamically recrystallized grains. For example, during extrusion, the basal plane reorients itself in one of two directions, where either the 〈 11 2 ¯ 0 〉 axis or the 〈 10 1 ¯ 0 〉 axis is parallel to the extrusion direction. Also, only a few (grain boundary) misorientation relationships are established between the recrystallized and the parent grains. In this paper, electron backscattered diffraction (EBSD) characterization on partially recrystallized microstructures, molecular dynamic simulations, and interfacial defect theory are used to uncover the mechanisms leading to the phenomena of texture formation during dynamic recrystallization. Simulations show that grain boundary energy and mobility emerge as the key governing effects in the experimentally-observed preferred orientation selection during nucleation and growth. Due to the low crystal symmetry inherent to the hexagonal close-packed structure of magnesium, certain grain boundaries possess both low interfacial energies and high mobilities relative to other boundaries, making them prime candidates for accommodating subgrain rotation. In particular, a new experimentally-observed { 13 4 ¯ 0 } twin boundary is associated with the texture formation; simulations and theory show that the twin boundary has a highly mobile b 2 / 2 disconnection with a wide dislocation core, presumably causing the affected grains to dominate the final texture.

The nucleation of helium (He) clusters and their growth in a monovacancy (V) eventually leads to the formation of He bubbles on grain boundaries and within the lattice for α-Fe, which can cause the macroscopic deterioration of material properties. The research objective herein is to model the nucleation and growth of small He clusters by calculating the formation/binding energetics of a single He atom to a HenV cluster (n = {1–7}) and by capturing how the grain boundary affects this behavior in bcc α-Fe. The formation energies for 1–8 He atoms in a monovacancy are calculated at all potential grain boundary sites within 15 A of ten select high angle grain boundaries. These results are combined with previously calculated vacancy formation energies and interstitial He formation energies to quantify how the local grain boundary structure impacts the binding of an interstitial He atom to a HenV cluster. We find that, despite the large range of different local environments within the grain boundaries, it is nearly always energetically favorable for a nearby interstitial He atom to combine with either a monovacancy or a HenV cluster to form a larger HenV cluster, with a binding energy that can be much greater (as much as 100% greater) than in the bulk crystal. Furthermore, a model is presented that captures the formation and binding energies of the various He–V clusters while capturing the subsequent binding energies of different clusters/defects in the presence of grain boundaries – both of which are important when accounting for the total energetics pertaining to He–V cluster growth in the presence of the high angle grain boundaries.

Additive manufacturing (AM) is a novel fabrication technique capable of producing highly complex parts. Nevertheless, a major challenge is the quality assurance of the AM fabricated parts. While there are several ways of approaching this problem, how to develop informative process signatures to detect part anomalies for quality control is still an open question. The objective of this study is to build a new layer-wise process signature model to characterize the thermal-defect relationship. Based on melt pool images, we propose novel layer-wise key process signatures, which are calculated using multilinear principal component analysis (MPCA) and are directly correlated with the layer-wise quality of the part. The resultant layer-wise quality features can be used to predict the overall defect distribution of a fabricated layer during the build. The proposed model is validated through a case study based on a direct laser deposition experiment, where the layer-wise quality of the part is predicted on the fly. The accuracy of prediction is calculated using three measures (i.e., recall, precision, and F-score), showing reasonable success of the proposed methodology in predicting layer-wise quality. The proposed quality prediction methodology enables online process correction to eliminate anomalies and to ultimately improve the quality of the fabricated parts.

Small-scale shear punch testing has been applied to a butt joint created by friction stir welding of two adjoining AA2139-T8 plates. Advantages of this technique include the ability to perform a large number of independent tests on a given volume of material and the ability to measure site-specific differences and variations in local material properties. As such, combined with a simultaneous evaluation of the weld morphology, a series of 144 shear punch tests were carried out in a 12×12 grid pattern on the retreating half of the weld. The overlay of the grid pattern onto the etched surface allowed a correlation of the microstructure and mechanical properties measured across the weld at each shear punch site. Two-dimensional color enhanced property maps were generated to provide a powerful site specific visualization of the unique or distinctive microstructural features and how they correlate with the local mechanical response across the weld. One of the more insightful discoveries was the weld nugget region undergoing 2.5 times more strain-hardening than the base plate material, while simultaneously experiencing the Portevin-LeChatelier effect. Aspects of the technique and results of our experiments are described.

The objective of this research is to demonstrate how similarity metrics can be used to quantify differences between sets of diffraction patterns. A set of 49 similarity metrics is implemented to analyze and quantify similarities between different Gaussian-based peak responses, as a surrogate for different characteristics in X-ray diffraction (XRD) patterns. A methodological approach was used to identify and demonstrate how sensitive these metrics are to expected peak features. By performing hierarchical clustering analysis, it is shown that most behaviors lead to unrelated metric responses. For instance, the results show that the Clark metric is consistently one of the most sensitive metrics to synthetic single peak changes. Furthermore, as an example of its utility, a framework is outlined for analyzing structural changes because of size convergence and isotropic straining, as calculated through the virtual XRD patterns.

Porous metals can be created through a wide variety of processing techniques, and the pore morphology resulting from these processes is equally diverse. The structural and functional properties of metal foams are directly dependent on the size, shape, interconnectedness and volume fraction of pores, so accurately quantifying the pore characteristics is of great importance. Methods for analyzing porous materials are presented here and applied to a copper-based metallic foam generated through solid state foaming via oxide reduction and expansion. This process results in large voids (10s of microns) between sintered particles and small pores (10 microns to less than 50 nm) within particles. Optical and electron microscopy were used to image the porosity over this wide range, and the pore characteristics were quantified using image segmentation and statistical analysis. Two-dimensional pore analysis was performed using the Chan-Vese method, and two-point correlation and lineal path functions were used to assess three-dimensional reconstructions from FIB tomography. Two-dimensional analysis reveals distinct size and morphological differences in porosity between particles and within them. Three-dimensional analysis adds further information on the high level interconnectedness of the porosity and irregular shape it takes, forming tortuous pathways rather than spherical cells. Mechanical polishing and optical microscopy allow large areas to be created and analyzed quickly, but methods such as focused ion beam (FIB) sectioning can provide additional insight about microstructural features. In particular, after FIB milling is used to create a flat surface, that surface can be analyzed for structural and compositional information.

Boron-based ceramics are appealing for lightweight applications in both vehicle and personnel protection, stemming from their combination of high hardness, high elastic modulus, and low density as compared to other ceramics and metal alloys. However, the performance of these ceramics and ceramic composites is lacking because of their inherent low fracture toughness and reduced strength under high-velocity threats. The objective of the present article is to briefly discuss both the challenges and the state of the art in experimental and computational approaches for engineering grain boundaries in boron-based armor ceramics, focusing mainly on boron carbide (B4C) and boron suboxide (B6O). The experimental challenges involve processing these ceramics at full density while trying to promote microstructure features such as intergranular films to improve toughness during shock. Many of the computational challenges for boron-based ceramics stem from their complex crystal structure which has hitherto complicated the exploration of grain boundaries and interfaces. However, bridging the gaps between experimental and computational studies at multiple scales to engineer grain boundaries in these boron-based ceramics may hold the key to maturing these material systems for lightweight defense applications.

The origin of texture components often associated with rare-earth element (REE) additions in wrought magnesium alloys is a long-standing problem in magnesium technology. While their influence on the texture is unquestionable, it is not yet clear why certain texture components, such as $$ \langle 11\bar{2}1 \rangle ||{\text{extrusion}}\;{\text{direction}}, $$ are favored over other components typically observed in traditional magnesium alloys. The objective of this research is to identify the mechanisms accountable for these RE textures during early stages of recrystallization. Electron backscattered diffraction and transmission electron microscopy analyses reveal that REEs in zinc-containing magnesium alloys corroborate discontinuous dynamic recrystallization. REEs promote isotropic growth for all nuclei generated through the bulging mechanism. During nucleation, the effect of REEs on orientation selection was explained by the diversified activity of both $$ \langle 10\bar{1}0 \rangle $$ and [0001] Taylor axes in the same grain with a marked preference for [0001] rotations to occur first. During nuclei growth, no growth preference was observed when sufficient REEs are added in the zinc-containing magnesium alloys, instead isotropic nuclei growth across all grain orientations occurs. This phenomenon is attributed to REEs segregating to grain boundaries (GBs), in agreement with prior computational and theoretical results (Barrett et al., Scripta Mater 146:46–50, 2018) that show a more isotropic GB energy and mobility after segregation.

A series of bulk nanostructured Fe–Ni–Zr oxide-dispersion-strengthened (ODS) alloys were synthesized using high energy mechanical alloying and consolidated using high temperature equal channel angular extrusion. The resultant microstructures are composed of nano/ultrafine or micrometer-sized grains with larger intermetallic precipitates and small Zr oxide clusters (

Symmetric and asymmetric tilt grain boundaries in Cu and Al were generated using molecular statics energy minimization in a classical molecular dynamics code with in-plane grain boundary translations and an atom deletion criterion. The following dataset (NIST repository, http://hdl.handle.net/11256/358) contains atomic coordinates for minimum energy grain boundaries in three-dimensional periodic simulation cells, facilitating their use in future simulations. This grain boundary dataset is used to show the relative transferability of grain boundary structures from one face-centered cubic system to another; in general, there is good agreement in terms of grain boundary energies (R2 > 0.99). Some potential applications and uses of this tilt grain boundary dataset in nanomechanics and materials science are discussed.

Darling et al. (2014) outlined a new model for nanocrystalline stability and applied it to a large number of systems to demonstrate its utility/applicability and how to visualize/select promising solutes in these nanocrystalline systems. The present article addresses some of the concerns raised by Lejcek and Hofmann (2015) and those that additional readers may have about this work. In some cases we have found that their concerns reflect ones that are shared by the community in developing and applying these sorts of models. For instance, in utilizing thermodynamic properties from such a large database of systems, researchers should use due diligence in extrapolating results to real systems – this model should be used as a guide for selecting prospective binary systems that may exhibit some degree of thermodynamic stability in nanocrystalline materials. Last, there was an oversight in terms of the phase transformation temperature for Fe and Ti, which negligibly changed the model results. In short, the present article is intended to bring to light and clarify some of the items raised by Lejcek and Hofmann.

Molecular simulations were performed to study the energetics and geometries of bond rupture in single alkane molecules using three reactive hydrocarbon potentials: (1) modified embedded-atom method (MEAM) for saturated hydrocarbons, (2) ReaxFF, and (3) second-generation REBO. The total energy/force versus strain, strain at fracture, and strain energy release were compared for a homologous series of normal alkanes (ethane to undecane) with generalization to polyethylene. The C C bond distances and C C C bond angles were quantified, and a fragment analysis was performed. Overall, the MEAM and ReaxFF potentials are in reasonable agreement with first-principles data with MEAM matching DFT-calculated lowest energy fragments.

The formation of a metastable Cu–Ta solid solution in a mechanically alloyed Cu–10 at.%Ta alloy and its subsequent decomposition during annealing was investigated by atom probe tomography. During annealing, the as-milled Cu-rich alloy undergoes phase separation; Ta atoms diffuse out of the Cu lattice to form Ta clusters and particles along grain boundaries and within the Cu grains. The role of the Ta clusters and the nature of the solid solution as a potential strengthening mechanism for these alloys are discussed.

Virtual diffraction is a computational technique that enables a synergistic coupling between experiments and atomistic simulations, which can help to elucidate nanoscale structure–property relationships. The research objective herein is to highlight recent advances in the use of virtual diffraction as a method to study the geometry and structure of homophase grain boundaries and heterophase interfaces with direct experimental validation. Virtual selected area diffraction patterns for two types of boundaries—homophase Al twist grain boundaries and heterophase Al2O3/Al interfaces—are created without a priori assumption of the periodic interface structure by computing diffraction intensities across high-resolution, 3-D reciprocal space meshes. In this work, computed diffraction patterns clearly identify Al grain boundary misorientation angles, reveal subsidiary peaks created by the dislocation arrays within select Al grain boundaries, and allow experimental validation of the minimum energy orientation relationship for the Al2O3/Al interface. Due to its advanced implementation, virtual diffraction characterization used throughout this work can be easily extended providing routes for similar analysis and experimental validation of atomistic simulations.

Microstructure characterization continues to play an important bridge to understanding why particular processing routes or parameters affect the properties of materials. This statement certainly holds true in the case of directionally solidified dendritic microstructures, where characterizing the primary dendrite arm spacing is vital to developing the process–structure–property relationships that can lead to the design and optimization of processing routes for defined properties. In this work, four series of simulations were used to examine the capability of a few Voronoi-based techniques to capture local microstructure statistics (primary dendrite arm spacing and coordination number) in controlled (synthetically generated) microstructures. These simulations used both cubic and hexagonal microstructures with varying degrees of disorder (noise) to study the effects of length scale, base microstructure, microstructure variability, and technique parameters on the local PDAS distribution, local coordination number distribution, bulk PDAS, and bulk coordination number. The Voronoi tesselation technique with a polygon-side-length criterion correctly characterized the known synthetic microstructures. By systematically studying the different techniques for quantifying local primary dendrite arm spacings, we have evaluated their capability to capture this important microstructure feature in different dendritic microstructures, which can be an important step for experimentally correlating with both processing and properties in single crystal nickel-based superalloys.

Grain boundaries play an important role in the mechanical and physical properties of polycrystalline metals. While continuum macroscale simulations are appropriate for modeling grain boundaries in coarse-grained materials, only atomistic simulations provide access to the details of the grain boundary (GB) structure and energy. Hence, a multiscale description is required to capture these GB details. The objective of this paper is to consolidate various approaches for characterizing grain boundaries in an effort to develop a multiscale model of the initial GB structure and energy. The technical approach is detailed using various 〈 1 0 0 〉 , 〈 1 1 0 〉 and 〈 1 1 1 〉 symmetric tilt grain boundaries in copper and aluminum. Characteristic features are: (i) GB energies obtained from atomistic simulations and boundary period vectors from crystallography, (ii) structural unit and dislocation descriptions of the GB structure and (iii) the Frank–Bilby equation to determine the dislocation content. The proposed approach defines an intrinsic net defect density scalar that is used to accurately compute the GB energy for these GB systems. The significance of the present work is that the developed atomistic-to-continuum approach is suitable for realistically inserting the initial GB structure and energy into continuum level frameworks.

Although complex geometries are attainable with additive manufacturing (AM), a major barrier preventing its use in mission-critical applications is the lack of geometric accuracy of AM parts. Existing geometric dimensioning and tolerancing (GD&T) characteristics are defined based on simple landmark features, and thus, need to be customized to capture the subtle difference in parts with complex geometries. Hence, the objective of this work is to quantify the geometric deviations of additively manufactured parts from a large data set of laser-scanned coordinates using an unsupervised machine learning (ML) approach called the self-organizing map (SOM). The central hypothesis is that clusters recognized by the SOM correspond to specific types of geometric deviations, which in turn are linked to certain AM process conditions. This hypothesis is tested on parts made while varying process conditions in the fused filament fabrication (FFF) AM process. The outcomes of this research are as follows: (1) visualizing and quantifying the link between process conditions and geometric accuracy in FFF and (2) significantly reducing the amount of point cloud data required for characterizing of geometric accuracy. The significance of this research is that this unsupervised ML approach resulted in less than 3% of over 1 million data points being required to fully quantify the part geometric accuracy.

The research objective herein is to understand the relationships between the interatomic potential parameters and properties used in the training and validation of potentials, specifically using a recently developed modified embedded-atom method (MEAM) potential for saturated hydrocarbons (C–H system). This potential was parameterized to a training set that included bond distances, bond angles, and atomization energies at 0 K of a series of alkane structures from methane to n-octane. In this work, the parameters of the MEAM potential were explored through a fractional factorial design and a Latin hypercube design to better understand how individual MEAM parameters affected several properties of molecules (energy, bond distances, bond angles, and dihedral angles) and also to quantify the relationship/correlation between various molecules in terms of these properties. The generalized methodology presented shows quantitative approaches that can be used in selecting the appropriate parameters for the interatomic potential, selecting the bounds for these parameters (for constrained optimization), selecting the responses for the training set, selecting the weights for various responses in the objective function, and setting up the single/multi-objective optimization process itself. The significance of the approach applied in this study is not only the application to the C–H system but that the broader framework can also be easily applied to any number of systems to understand the significance of parameters, their relationships to properties, and the subsequent steps for designing interatomic potentials under uncertainty.

The goal of this work is to minimize geometric inaccuracies in parts printed using a fused filament fabrication (FFF) additive manufacturing (AM) process by optimizing the process parameters settings. This is a challenging proposition, because it is often difficult to satisfy the various specified geometric accuracy requirements by using the process parameters as the controlling factor. To overcome this challenge, the objective of this work is to develop and apply a multi-objective optimization approach to find the process parameters minimizing the overall geometric inaccuracies by balancing multiple requirements. The central hypothesis is that formulating such a multi-objective optimization problem as a series of simpler single-objective problems leads to optimal process conditions minimizing the overall geometric inaccuracy of AM parts with fewer trials compared to the traditional design of experiments (DOE) approaches. The proposed multi-objective accelerated process optimization (m-APO) method accelerates the optimization process by jointly solving the subproblems in a systematic manner. The m-APO maps and scales experimental data from previous subproblems to guide remaining subproblems that improve the solutions while reducing the number of experiments required. The presented hypothesis is tested with experimental data from the FFF AM process; the m-APO reduces the number of FFF trials by 20% for obtaining parts with the least geometric inaccuracies compared to full factorial DOE method. Furthermore, a series of studies conducted on synthetic responses affirmed the effectiveness of the proposed m-APO approach in more challenging scenarios evocative of large and nonconvex objective spaces. This outcome directly leads to minimization of expensive experimental trials in AM.

The microstructure and mechanical properties of Laser Based Additive Manufacturing (LBAM) are often inconsistent and unreliable for many industrial applications. One of the key technical challenges is the lack of understanding of the underlying process-structure-property relationship. The objective of the present research is to use the melt pool thermal profile to predict porosity within the LBAM process. Herein, we propose a novel porosity prediction method based on morphological features and the temperature distribution of the top surface of the melt pool as the LBAM part is being built. Self-organizing maps (SOM) are then used to further analyze the 2D melt pool dataset to identify similar and dissimilar melt pools. The performance of the proposed method of porosity prediction uses X-Ray tomography characterization, which identified porosity within the Ti-6Al-4V thin wall specimen. The experimentally identified porosity locations were compared to the porosity locations predicted based on the melt pool analysis. Results show that the proposed method is able to predict the location of porosity almost 85% of the time when the appropriate SOM model is selected. The significance of such a methodology is that this may lead the way towards in situ monitoring and on-the-fly modification of melt pool thermal profile to minimize or eliminate pores within LBAM parts.

Despite recent advances in improving mechanical properties of parts fabricated by Additive Manufacturing (AM) systems, optimizing geometry accuracy of AM parts is still a major challenge for pushing this cutting-edge technology into the mainstream. This work proposes a novel approach for improving geometry accuracy of AM parts in a systematic and efficient manner. Initial experimental data show that different part geometric features are not necessary positively correlated. Hence, it may not be possible to optimize them simultaneously. The proposed methodology formulates the geometry accuracy optimization problem as a multi-objective optimization problem. The developed method targeted minimizing deviations within part’s major Geometric Dimensioning and Tolerancing (GD&T) features (i.e., Flatness, Circularity, Cylindricity, Concentricity and Thickness). First, principal component analysis (PCA) is applied to extract key components within multi-geometric features of parts. Then, experiments are sequentially designed in an accelerated and integrated framework to achieve sets of process parameters resulting in acceptable level of deviations within principal components of multi-geometric features of parts. The efficiency of proposed method is validated using simulation studies coupled with a real world case study for geometry accuracy optimization of parts fabricated by fused filament fabrication (FFF) system. The results show that optimal designs are achieved by fewer numbers of experiments compared with existing methods.

Atomistic simulations have shown that the grain boundary (GB) structure affects a number of physical, mechanical, thermal, and chemical properties, which can have a profound effect on macroscopic properties of polycrystalline materials. The research objective herein is to use atomistic simulations to explore the role that GB structure and the adjacent crystallographic orientations have on the directional asymmetry of an intergranular crack (i.e. cleavage behavior is favored along one direction, while ductile behavior along the other direction of the interface) for aluminum grain boundaries. Simulation results from seven 〈110〉 symmetric tilt grain boundaries (STGBs) show that the GB structure and the associated free volume directly influence the stress–strain response, crack growth rate, and crack tip plasticity mechanisms for middle-tension (M(T)) crack propagation specimens. In particular, the structural units present within the GB promote whether a dislocation or twinning-based mechanism operates at the crack tip during intergranular fracture along certain GBs (e.g., the ‘E’ structural unit promotes twinning at the crack tip in Al). Furthermore, the crystallography of the adjacent grains, and therefore the available slip planes, can significantly affect the crack growth rates in both directions of the crack – this creates a strong directional asymmetry in the crack growth rate in the Σ 11 (113) and the Σ 27 (552) STGBs. Upon comparing these results with the theoretical Rice criterion, it was found that certain GBs in this study ( Σ 9 (221), Σ 11 (332) and Σ 33 (441)) show an absence of directional asymmetry in the observed crack growth behavior, in conflict with the Rice criterion. The significance of the present research is that it provides a physical basis for the role of GB character and crystallographic orientation on intergranular crack tip deformation behavior.

The role that grain boundary (GB) structure plays on the plasticity of interfaces with preexisting cracks and on the interface crack dynamics was investigated using MD for both and aluminum STGBs. In simulations with a crack at the interface, this research shows how the maximum normal strength of the interface correlates with the respective GB energy, the GB misorientation, and the GB structural description. For instance, the normal interface strength for GBs containing D structural unit (SU) or stacking faults in the GB structural description ({\Sigma}13 (510) {\theta}=22.6{\deg} and {\Sigma}97 (940) {\theta}=47.9{\deg}) shows a noticeable decrease in interface strength, as compared to other evaluated GBs that contained favored SUs. In the case of interfaces, the presence of the E SU in the GB structure lowers the maximum normal interface strength by 35%. Further investigation of the deformation at the crack tip in GBs containing the E structure revealed that the E SU underwent atomic shuffling to accommodate intrinsic stacking faults (ISFs) along the interface, which in turn acts as a site for partial dislocation nucleation. Interestingly, regardless of GB misorientation, GB interfaces examined here containing the E structure in their structural period exhibited relatively small variation in maximum normal strength of interface. The GB volume ahead of the crack tip underwent structural rearrangement which, in turn, influenced the crack propagation mechanism. In most GBs, the crack propagation was due to alternating mechanisms of dislocation emission, followed by propagation of dislocation (blunting) and cleavage/crack advance. Moreover, the crack growth rates along the GB interface were strongly influenced by the initial free volume at the interface, i.e., faster crack growth was observed along interfaces with higher initial free volume.

It is a new beginning for innovative fundamental and applied science in nanocrystalline materials. Many of the processing and consolidation challenges that have haunted nanocrystalline materials are now more fully understood, opening the doors for bulk nanocrystalline materials and parts to be produced. While challenges remain, recent advances in experimental, computational, and theoretical capability have allowed for bulk specimens that have heretofore been pursued only on a limited basis. This article discusses the methodology for synthesis and consolidation of bulk nanocrystalline materials using mechanical alloying, the alloy development and synthesis process for stabilizing these materials at elevated temperatures, and the physical and mechanical properties of nanocrystalline materials with a focus throughout on nanocrystalline copper and a nanocrystalline Cu-Ta system, consolidated via equal channel angular extrusion, with properties rivaling that of nanocrystalline pure Ta. Moreover, modeling and simulation approaches as well as experimental results for grain growth, grain boundary processes, and deformation mechanisms in nanocrystalline copper are briefly reviewed and discussed. Integrating experiments and computational materials science for synthesizing bulk nanocrystalline materials can bring about the next generation of ultrahigh strength materials for defense and energy applications.

The objective of this research is to quantify the effects of resistance spot welding (RSW) parameters on different weld properties of a dual-phase steel. A finite element based model was used which accounted for the following required physical interactions: the interaction between (1) the electro-kinetics and heat transfer via the Joule effect, (2) the heat transfer and phase transformations through latent heat, and (3) the heat transfer, electro-kinetics, and mechanical behavior via the contact conditions. The effects of the RSW parameters on weld properties were investigated within a design of experiments framework by altering (1) the electrical current intensity, (2) the welding time, (3) the sheet thickness, (3) the electrode face radius, and (5) the squeeze force at multiple levels. The simulation results were analyzed using the analysis of variance (ANOVA) technique to show the effects of these parameters and their potential interactions, along with their significance. The current intensity was the most influential factor and resulted in an increased size of molten zone and the heat affected zone. The sheet thickness and welding time also showed significant contributions in changing the weld properties. The effects of the other parameters were less significant. The importance of this study is that finding the optimal process window for RSW parameters can help to engineer the desired weld properties.

Mitigating grain growth at high temperatures in binary nanocrystalline alloys is important for processing nanocrystalline alloy systems. The objective of this research is to develop a methodical design-based approach for selecting solutes in binary nanocrystalline alloys by revisiting grain boundary thermodynamics and the internal processes of grain growth and solute segregation in a closed system. In this work, the grain boundary energy is derived and systematically studied in terms of temperature, grain size, concentration, and solute segregation for binary systems of 44 solvents and 52 solutes, using readily-available elemental data, such as moduli and liquid enthalpy of mixing. It is shown that through solute segregation, the grain boundary energies of some binary systems can be reduced, resulting in thermodynamically stable grain structures and successful prediction of solutes that inhibit grain growth in some nanocrystalline alloys. Parametric studies reveal trends between equilibrium grain size, solute distribution and temperature for various binary systems culminating in the generation of nanocrystalline thermodynamic stability maps as a tool for solute selection in binary nanocrystalline alloys.

porosity has typically been limited to � 40%. These relatively low porosity levels (compared to liquid state processes) constrain the applications of metal foams produced via solid state foaming. To extend the capabilities of solid state foaming, we have developed an additive means of creating porosity by intraparticle expansion. The current limitation of solid-state expansion by gas entrapment is dictated by voids formed between solid particles during consolidation. In this model, the initial gas pressure and foaming temperature determine the resulting porosity. However, if the expanding gas is not limited to just that which is trapped between particles, but is also located within particles, solid state foaming may assume a character more akin to expandable polymers which foam from the constituent pellets. This concept is a paradigm shift in terms of the solid state foaming process and the achievable levels of porosity. On the basis of this simple, powder feedstock expansion, there is universal application to powder metallurgy methods for foaming. In this work, we examine the microstructure and morphology of a Cu–Sb alloy that expands to porosities of close to 40% within individual particles, resulting in absolute porosity of 69% in sintered samples. The Cu–Sb alloy powder was formed by mechanically alloying Cu and Sb (Alfa Aesar, 99.9 and 99.5%, respectively) at cryogenic temperature(� 196°C) for 4h using a modified SPEX 8000M Mixer/Mill. The elemental powders were combined to achieve 5 at% Sb in Cu. The as-milled powders contained no appreciable porosity. Ball milling was used as a means to intimately mix the elements and refine and distribute preexisting oxides. Although oxygen exposure was controlled during milling and storage of powders, the manufacturersupplied precursors did contain appreciable oxygen content. The importance of this will be detailed later. The alloyed powder was annealed at 600°C for a period of 1h under 3% H2 (bal. Ar). During annealing, the powders underwent pore formation and expansion. When annealing was done in the absence of H2, it did not produce any expansion. Microscopic examination of the loose powders was carried out using an FEI Nova Nano Lab 600 dual beam microscope using scanning electron microscopy (SEM). Cross-sectional analysis was performed using a focused ion beam (FIB). The grain size and grain orientations were measured using focused ion beam