Iman Ghamarian

Additively manufactured materials are gaining wide attention owing to the manufacturing benefits as it results in near net shape components. It is well known that the manufacturing processes affects the performance of the components via microstructural features and the mechanical properties. There is an urgent need to understand the processing-structure-property-performance relationship for the materials manufactures via such innovative techniques. Strategies are needed to quantify and modify the mechanical properties. This study assists to design and tailor the process parameters based on the final properties required. Current work predicts the yield strength of additively manufactured Ti-6Al-4V with different post heat treatments. A thermal model predicted by ABAQUS is fed into an implementation of Langmuir equation that predicts the chemistry which is then used in a phenomenological equation predicting the yield strength. The model is confirmed via experiments showing less than 2...

The analysis of solute clustering in atom probe tomography (APT) has almost exclusively relied on a simple algorithm based on the simple friend-of-friend analysis where a threshold distance or maximum separation defines whether atoms are part of a cluster or part of the matrix. This method is however limited to very specific microstructures and is very sensitive to parameter selection. To expand the range and applicability of current APT analysis tools, we introduce new quantitative data analysis methods based on density-based hierarchical clustering algorithms and relevant to solute clustering and segregation. We demonstrate the methods' performance on the complex microstructure developing in a proton-irradiated Alloy 625, specifically focusing on the analyses of nanoscale Al clusters, Si clusters, and Si-decorated dislocation loops.

The quality of a Sn film deposited by the aerosol process is compared against the quality of Sn films deposited with traditional electroplating. Using the aerosol additive deposition technique, a Sn film was deposited on a brass substrate and exposed to room (25°C) temperature environments for 30 days, followed by a laser photosintering process. The film characteristics and content , formation of intermetallic compounds, residual stress distribution, grain texture, and the tendency of the film to grow Sn whiskers were analyzed. The preliminary results show a successful deposition of Sn film with an aerosol jet process and tensile residual stresses, whereas it was compressive in nature for electroplated Sn film. X-ray diffraction results also show the absence of intermetallic compound (IMC) formation in the aerosol jet-deposited film, while electroplated Sn film has a significant presence of IMC. The aerosol jet-deposited Sn film has the potential to resist nucleation of Sn whiskers under the operating conditions used in this study.

Effect of tensile loading on crystallisation behaviour of as-cast and laser thermal treated Fe–Si–B metallic glass foils was investigated. Tensile loading lacked any marked influence on the crystallisation behaviour of as-cast and structurally relaxed laser-treated metallic glass foils. Furthermore, the average crystallite/grain size in partially crystallised laser-treated metallic glass foil was nearly equal to the average crystallite/grain size in the region away from the fracture of the same partially crystallised laser-treated metallic glass foil after tensile loading. However, a significant crystallite/grain growth/coarsening of the order of two and half times was observed in the fractured region compared to the region around it for the laser-treated partially crystallised metallic glass foils. The simultaneous effects of stress generation and temperature rise during tensile loading were considered to play a key role in crystallite/grain growth/coarsening.

Mechanical properties of additively manufactured parts are sensitive to the presence of pores form during the manufacturing process. The impact of pores on the mechanical performance has been investigated extensively with respect to different parameters, such as pore volume fraction, shape, and size. However, statistical investigations focusing on the relationships between the spatial distribution of pores and process parameters; and consequently, the performance of the manufactured parts are scattered and limited. Also, these sparse investigations usually suffer from an ambiguous definition of terminologies. For instance, the required criteria to consider a point pattern as complete spatial randomness (CSR) are generally not clarified. Moreover, no numerical formalism is yet developed to show how much the observed spatial results are statistically significant. To address these shortcomings, the statistical definition of CSR in a point pattern and the procedure to quantitatively determine the deviation of a pattern from CSR were discussed. The explained statistical approach was used to investigate the effect of scanning speed parameter on the spatial distribution of gas pores in laser powder bed fusion manufactured stainless steel parts. Furthermore, and to highlight the impact of the spatial distribution of pores on mechanical properties, fatigue performances of parts with clustered and randomly distributed pores were simulated by finite element analysis. It is shown that by reducing the scanning speed, the spatial distribution of gas pores deviated more from CSR, and correspondingly fatigue performance deteriorated.

Atom probe tomography, and related methods, probe the composition and the three-dimensional architecture of materials. The software tools which microscopists use, and how these tools are connected into workflows, make a substantial contribution to the accuracy and precision of such material characterization experiments. Typically, we adapt methods from other communities like mathematics, data science, computational geometry, artificial intelligence, or scientific computing. We also realize that improving on research data management is a challenge when it comes to align with the FAIR data stewardship principles. Faced with this global challenge, we are convinced it is useful to join forces. Here, we report the results and challenges with an inter-laboratory call for developing test cases for several types of atom probe microscopy software tools. The results support why defining detailed recipes of software workflows and sharing these recipes is necessary and rewarding: Open source tools and (meta)data exchange can help to make our day-today data processing tasks become more efficient, the training of new users and knowledge transfer become easier, and assist us with automated quantification of uncertainties to gain access to substantiated results.

Alloy 800H is a high temperature and creep resistant alloy, and is considered as a candidate alloy for use in Generation IV nuclear reactor systems. To clarify the alloy's behavior under irradiation, samples were subjected to single ion irradiations up to 20 dpa at 440 °C, a dual ion/He irradiation to 17 dpa at 460 °C, and a neutron irradiation to 17 dpa at 385 °C. The irradiated microstructures were characterized using atom probe tomography to complement previously published transmission electron microscopy studies. After single ion irradiation, sparse fine Al and Ti clusters were observed after 1dpa, while high number densities of nanoscale Ni-Al-Ti clusters, Cr-Ti rich carbides, and Si-decorated dislocation loops developed after 10 and 20 dpa. The microstructure formed during single ion irradiation exhibited a strong depth dependence. No significant differences were observed between the single and dual ion irradiations. While Ni-Al-Ti clusters, Cr-Ti rich carbides, and Si-decorated dislocation loops were also observed after neutron irradiation, the neutron-irradiated microstructure differed from those found in the ion-irradiated samples.

The layer-wise deposition of the laser powder bed fusion (L-PBF) process offers a tailorable microstruc-ture with anisotropic properties. This research aimed to investigate the contribution of crystallographic texture on the anisotropic electrochemical/corrosion behavior of a L-BPF processed commercially pure nickel (Ni) cube along surfaces oriented parallel and perpendicular to the building direction. In order to exclusively assess the role of crystallographic texture on the corrosion behavior, the electrochemical measurements were performed on different surfaces of the L-PBF processed cube in alkaline and acidic chloride-free solutions. Orientation microscopy analysis showed that the morphology of grains along the parallel surface to the building direction was columnar, and their crystallographic orientation distribution was uniform. In contrast, the morphology of grains on the surface aligned normal to the building direction was equiaxed and highly oriented toward [110]. This bias in the orientation distribution led to the highest residual affinity for oxidation in comparison to the other planes in the FCC (Face Centered Cubic) crystal structure. In the case of 1 M NaOH solution, the hydroxide layer formed at the exposed surface did not effectively control the cation ejection from the surface. Also, surfaces oriented normal to the building direction, with lower surface atomic density, showed an elevated dissolution rate. Conversely, in the case of 1 M H 2 SO 4 solution, a more integrated and thicker oxide film formed in comparison to the surface aligned parallel to the building direction. This fact led to reduced surface dissolution. This study shows that engineering the topology and microstructure based on the desired properties can remarkably promote the performance of additively manufactured materials in extreme environments.

A scale-independent model for the interaction between multivariant phase transformations (PTs) and discrete shear bands is advanced and utilized to simulate plastic strain-induced PTs at high pressure. The model includes a scale-free phase-field theory for martensitic PTs. The localized shear bands are introduced via a contact problem formulation. That is, the continuous distribution of sliding displacements along the prescribed slip surfaces is modeled to reproduce the plastic-strain-induced stress concentrators necessary for nucleation of a high-pressure phase (HPP). The strain-induced PTs in the bi/polycrystalline samples subjected to compression and shear are studied. The simulations show a severe reduction in the PT pressure by the plastic shear in comparison to a hydrostatic condition, even below the phase equilibrium pressure, like in known experiments. Transformation kinetics versus shear strain for each martensitic variant and the volume fraction of the HPP in individual grains and the entire aggregate are determined. The stationary volume fraction of the HPP is the same for polycrystals consisting of 13 and 38 grains, and a further shearing does not cause PT. The local phase equilibrium condition based on the transformation-work criterion is satisfied at almost all stationary phase interfaces. A similar phase equilibrium condition in terms of stresses averaged over the entire polycrystal or HPP is fulfilled. These results are important for the development of the microscale kinetic equations and modeling the sample behavior in traditional and rotational diamond anvils during the high-pressure torsion, ball milling, friction, and other deformation-transformation processes.

The technique of atom probe tomography is often used to image solute clusters and solute atom segregation to dislocation lines in structural alloys. Quantitative analysis, however, remains a common challenge. To address this gap, we combined a cluster finding algorithm, a skeleton finder algorithm, and morphological classification of dense objects to distinguish solute clusters from solute-decorated dislocation lines, both being characterized by high solute atom densities. The proposed workflow is packaged into a graphical user interface available through GitHub. We illustrate its application on a synthetic dataset containing known objects and apply it to an experimental dataset obtained from a proton-irradiated Alloy 625 that contains high densities of Si-decorated dislocations and Si-rich clusters.

Atom probe tomography (APT) has enabled the direct visualization of solute clusters. However one of the main
analysis methods used by the APT community, i.e. the maximum separation method, often suffers from subjective
parametric selection and limited applicability. To address the need for more robust and versatile analysis
tools, a framework based on hierarchical density-based cluster analysis is implemented. Cluster analysis begins
with the HDBSCAN algorithm to conservatively segment the datasets into regions containing clusters and a
matrix or noise region. The stability of each cluster and the probability that an atom belongs to a cluster are
quantified. Each clustered region is further analyzed by the DeBaCl algorithm to separate and refine clusters
present in the sub-volumes. Finally, the k-nearest neighbor algorithm may be used to re-assign matrix atoms to
clusters, based on their probability values. Four mandatory parameters are required for this cluster analysis
approach. However, the selection of an appropriate value for only one of these parameters, i.e. a rough estimate
of the minimum cluster size, is essential. The improved performance of the method was evaluated by analyzing
four synthetic APT datasets and comparing the outcome with the commonly-used maximum separation method.
Codes and data are made available through GitHub.

A scale-free model for the coupled evolution of discrete dislocation bands and multivariant martensitic microstructure is developed. In contrast to previous phase field models, which are limited to nanoscale specimens, this model allows for treating the nucleation and evolution of martensite at evolving dislocation pileups, twin tips, and shear bands in a sample of an arbitrary size. The model is applied for finite element simulations of plastic strain-induced phase transformations (PTs) in a polycrystalline sample under compression and shear. The solution explains the one to two orders of magnitude reduction in PT pressure by plastic shear, the existence of incompletely transformed stationary state, and optimal shear strain for the strain-induced synthesis of high pressure phases.

A microscale phase field model developed in Levitas et al. (2004) and Idesman et al. (2005) is slightly advanced for different and anisotropic elastic moduli of phases and is employed for the study of the stress-induced cubic-monoclinic phase transition in NiTi single crystal involving all 12 martensitic variants. The model is scale-independent, without the gradient term, and it is applicable for any scale greater than 100 nm. This model includes strain softening and the corresponding transformation strain localization, and it reproduces a discrete martensitic microstructure. The model only tracks finite-width interfaces between austenite and the mixture of martensitic variants, and does not consider the interfaces between martensitic variants. The model is implemented as a UMAT subroutine in a commercial finite element (FE) package, ABAQUS. Multiple problems for a uniaxial cyclic loading are solved to study the effect of mesh, strain rate, crystal orientation, different numbers of pre-existing nuclei, and the magnitude of the athermal threshold on the stress-strain responses as well as the microstructure evolution. The obtained results exhibit that the microstructure and global stress-strain responses are practically independent of mesh discretization and the applied strain rate for relatively small strain rates. While the presence of the initial nuclei in the sample decreases the nucleation stress, it slightly increases the total energy dis-sipation. The observed microstructure, the sudden drop in the stress-strain curve after initiation of the martensitic transformation, and the absence of a similar jump for the reverse phase transformation are in qualitative agreement with known experiments. Changing the crystallographic orientation of the sample varies the entire behavior, namely, the variants which are involved in the phase transformation, the morphology of the associated microstructure, the stress-strain curve, and the total dissipation. Athermal threshold, in addition to the expected increase in the magnitude of hysteresis, leads to some strain hardening for the direct phase transformation.

A B S T R A C T Aluminum-magnesium alloys are ideal for a number of structural applications; however, alloys with magnesium content more than 3 wt% can become sensitized and susceptible to environmentally-assisted failure after exposure to moderately elevated temperatures for sufficient periods of time. Commercial Al-Mg alloys were tested in the as-received and sensitized conditions to investigate susceptibility to environmentally-enhanced cracking in fatigue crack growth. Slow strain rate testing (SSRT) of short-transverse (S-T) smooth tensile samples and fatigue crack growth testing of SEN bend specimens was also conducted in various loading orientations (L-T, L-S, S-L and S-T). Sensitization treatments included exposure to temperatures ranging from 60 °C – 175 °C for times ranging from 10 h to 20,000 h. Under specific loading conditions and the presence of a local hydrogen source, IGSCC manifested itself as ST and SL plane splits during the cyclic loading of sufficiently sensitized L-S and L-T test specimens. The regimes of grain boundary cracking have been determined. Initial findings using ESBD data is suggestive that the high angle grain boundaries most likely to suffer IGSCC are those with a sharp gradient in the Taylor factor. Remediation treatments were also conducted on sensitized Al-Mg alloys in an attempt to reverse sensitization and restore alloy properties. While thermal remediation was somewhat effective in reducing the environmentally-sensitive fracture, this condition occurred at the expense of a loss in strength and its long-term effectiveness remains questionable. SEM and EBSD were used to document the locus and orientation-dependence of grain boundary failure while TEM was used to determine species present at grain boundaries susceptible to environmentally-enhanced fracture. SSRT testing of S-T samples in various environments revealed different degrees of embrittlement depending on the environment and degree of sensitization. A change in alloy sensitization kinetics occurs at around 100 °C.

The quality of a Sn film deposited by the aerosol process is compared against the quality of Sn films deposited with traditional electroplating. Using the aerosol additive deposition technique, a Sn film was deposited on a brass substrate and exposed to room (25°C) temperature environments for 30 days, followed by a laser photosintering process. The film characteristics and content , formation of intermetallic compounds, residual stress distribution, grain texture, and the tendency of the film to grow Sn whiskers were analyzed. The preliminary results show a successful deposition of Sn film with an aerosol jet process and tensile residual stresses, whereas it was compressive in nature for electroplated Sn film. X-ray diffraction results also show the absence of intermetallic compound (IMC) formation in the aerosol jet-deposited film, while electroplated Sn film has a significant presence of IMC. The aerosol jet-deposited Sn film has the potential to resist nucleation of Sn whiskers under the operating conditions used in this study.

Advanced manufacturing approaches, including additive manufacturing (i.e., " 3D printing ") of metallic structures requires a change to qualification strategies. One approach, informed qualification, integrates modeling strategies to make predictions of material characteristics, including the prediction of tensile properties for given chemistries and microstructures. In this work, constitutive equations are developed and presented that can predict the yield strength of additively manufactured Ti-6Al-4V subjected to one of three different heat-treatments: a stress relief anneal in the aþb phase field; a hot isostatic press treatment in the aþb phase field; and a b-anneal. The equations are nominally identical, though different strengthening mechanisms are active according to subtle microstructural differences. To achieve an equation that can predict the yield strength of the material, it is also necessary to include an assessment of dramatic reduction in the tensile strength due to texture (i.e., a " knock-down " effect). This has been experimentally measured, and included in this paper. The resulting predictions of yield strength are generally within 5% of their experimentally measured values.

A constituent-based phenomenological equation to predict yield strength values from quantified measurements of the microstructure and composition of β processed Ti-6Al-4V alloy was developed via the integration of artificial neural networks and genetic algorithms. It is shown that the solid solution strengthening contributes the most to the yield strength (80% of the value), while the intrinsic yield strength of the two phases and microstructure have lower effects (10% for both terms). Similarities and differences between the proposed equation and the previously established phenomenological equation for the yield strength prediction of the αþβ processed Ti-6Al-4V alloys are discussed. While the two equations are very similar in terms of the intrinsic yield strength of the two constituent phases, the solid solution strengthening terms and the 'Hall-Petch'-like effect from the alpha lath, there is a pronounced difference in the role of the basketweave factor in strengthening. Finally, Monte Carlo simulations were applied to the proposed phenomenological equation to determine the effect of measurement uncertainties on the estimated yield strength values.

Fatigue crack growth experiments were conducted in humid air (RH~45%) at 25 °C on 29-mm-thick plate samples of an aluminium–magnesium (Al–Mg) 5083-H131 alloy in the long transverse (LT) direction. Samples were tested in both the as-received condition and after sensitization at 175 °C for 100 h.
Delamination along some grain boundaries was observed in the short transverse plane after fatigue testing of the sensitized material, depending upon the level of ΔK and Kmax. Orientation microscopy using electron backscattering diffraction and chemical analyses using transmission electron microscopy and energy dispersive spectroscopy of grain boundaries revealed that Mg segregation and the orientation of grains had key roles in the observed grain boundary delamination of the sensitized material.

Since composition and microstructure considerably affect the mechanical properties of metallic materials, it is highly desirable to be able to predict mechanical properties based upon these features. However, due to the complexity of real, multi-component, multi-phase engineering alloys, it is extremely difficult to develop constituent-based phenomenological equations. An accepted solution to the problem is to use Neural Networks. Unfortunately, while the developed model is quantitative it is not phenomenological. Thus, we propose that one must use new approaches in parallel with neural networks to derive the phenomenological equations. This talk will highlight a new method based upon the integration of three separate modeling approaches, specifically Artificial Neural Networks, Genetic Algorithms, and the Monte Carlo method to derive phenomenological equations with a simultaneous analysis of uncertainty. This approach has been applied to derive a phenomenological equation for the p...

The development of tools to predict the mechanical properties based upon compositional and microstructural inputs in multi-component, multi-phase Ti-based alloys represents a significant challenge. One such solution is the development of high-fidelity databases and the subsequent application of non-linear modeling tools such as neural networks based upon a Bayesian framework to extract the underlying composition-microsructure-property relationships. This approach has resulted in successful tools for the prediction of properties but often is based upon complex equations that do not appear to be phenomenological. Thus, one must use new approaches in parallel with neural networks to derive the phenomenological equations. This talk will highlight the development of such rules-based models for the prediction of the tensile and fracture toughness properties of Ti6Al4V at room temperature. These models have been successfully used to isolate the influence of the individual microstructural f...

ABSTRACT Since composition and microstructure considerably affect the mechanical properties of metallic materials, it is highly desirable to be able to predict mechanical properties based upon these features. However, due to the complexity of real, multi-component, multi-phase engineering alloys, it is extremely difficult to develop constituent-based phenomenological equations. An accepted solution to the problem is to use Neural Networks. Unfortunately, while the developed model is quantitative it is not phenomenological. Thus, we propose that one must use new approaches in parallel with neural networks to derive the phenomenological equations. This talk will highlight a new method based upon the integration of three separate modeling approaches, specifically Artificial Neural Networks, Genetic Algorithms, and the Monte Carlo method to derive phenomenological equations with a simultaneous analysis of uncertainty. This approach has been applied to derive a phenomenological equation for the prediction of tensile strength in a variety of Ti-6-4 microstructures from databases containing compositional and microstructural features of the alloy.

The increased spatial resolution of a new characterization technique, precession electron diffraction (PED), makes possible the very accurate and automated quantitative characterization of technically interesting materials that historically have been difficult to analyze due to their dimensions and/or degree of deformation, including specifically ultrafine-grained metallic structures with high dislocation densities. PED, when coupled with the novel post-processing techniques that have been rigorously developed and presented for the first time in this paper, such as applying a Kuwahara filter to improve the angular resolution of the technique, makes it possible to determine grain size, texture, the density and spatial distribution of geometrically necessary dislocations, crystal orientation gradients, and the character of grain boundaries at the relevant length scale (the nanoscale) for such ultrafine-grained materials. The methods detailed in this paper place the determination of key microstructural features on a quantitative, rather than qualitative footing. These techniques have been applied to a hexagonal close-packed a-titanium. The results include the correlation between defect structure and microstructure with a nanometer resolution, the identification of regions containing few geometrically necessary dislocations, the quantification of dislocation densities in cell walls, and the quantification of deformation type in a statistically meaningful fashion.

Poor oxidation performance of Ti-based alloys is an important life-limiting factor for high temperature
applications. In this paper, a combinatorial approach is used to investigate systematically the influence
of composition and time on the oxidation of Ti–Cr system. A compositionally graded Ti–xCr specimen
(0<=x<=40 wt%) was prepared and oxidized at 650 C. The structure and composition of the oxide and
near-surface region were studied and a critical composition of 20 wt% Cr was identified above which
the oxidation resistance is enhanced. Below the critical composition transition to a rapid breakaway oxidation
was observed for extended exposure times.

One key aspect of any integrated computational materials engineering approach is the integration of experiments that provide critical information for the modeling activities. This article describes, using case studies, three examples of critical experiments that have been conducted in an integrated fashion with modeling activities for titanium alloys, providing valuable information in an accelerated manner. The first has been used to identify key microstructural features associated with fracture toughness in Ti-6Al-4V and integrates artificial neural networks and various experimental techniques. The second is associated with defect accumulation in highly constrained titanium structures and integrates a highly innovative characterization technique (precession electron diffraction) and dislocation dynamics. The third is a high-throughput combinatorial technique to understand the oxidation behavior of titanium alloys and couples the experimental effort with the CALPHAD approach.