Pinar Acar

Virginia Tech · Department of Mechanical Engineering

This work addresses a comprehensive review of the recent efforts for uncertainty quantification in small-scale materials science. Experimental and computational studies for analyzing and designing materials in small length-scales, such as atomistic, molecular, and meso levels, have emerged substantially over the last decade. With the advancement in...

The present work addresses gradient-based and machine learning (ML)-driven design optimization methods to enhance homogenized linear and nonlinear properties of cubic microstructures. The study computes the homogenized properties as a function of underlying microstructures by linking atomistic-scale and meso-scale models. Here, the microstructure i...

Cellular mechanical metamaterials (CMMs) are assemblies of periodic representative volume elements that can be engineered to exhibit unique mechanical properties. Recent advances in additive manufacturing (AM) have enabled us to fabricate sophisticated architected materials with high precision. To increase and diversify applications in both science...

This Perspective article aims to emphasize the crucial role of uncertainty quantification (UQ) in understanding magnetic phase transitions, which are pivotal in various applications, especially in the transportation and energy sectors [D. C. Jiles, Acta Mater. 51, 5907–5939 (2003) and Gutfleisch et al., Adv. Mater. 23, 821–842 (2011)]. Magnetic mat...

This study aims to model the phase transition of ferromagnetic materials using two- (2D) and three-dimensional (3D) Ising models, incorporating long-range magnetic spin-to-spin interactions and the influence of an external magnetic field. The 2D Ising model is investigated mainly for a $$4\times 4$$ 4 × 4 domain, while its extension to larger domai...

Microstructure-sensitive materials design has become popular among materials engineering researchers in the last decade because it allows the control of material performance through the design of microstructures. In this study, the microstructure is defined by an orientation distribution function. A physics-informed machine learning approach is int...

The geometrical arrangement of metamaterials controls their mechanical properties, such as Young’s modulus and the shear modulus. However, optimizing the geometrical arrangement for user-defined performance criteria leads to an inverse problem that is intractable when considering numerous combinations of properties and underlying geometries. Machin...

The geometrical arrangement of metamaterials controls their mechanical properties, such as Young's modulus and shear modulus. However, optimizing the geometrical arrangement for user-defined performance criteria leads to an inverse problem that is intractable when considering numerous combinations of properties and underlying geometries. Machine le...

This study presents a comprehensive investigation into the crystal plasticity behavior of the dual-phase Ti-6Al-4V alloy through the utilization of Crystal Plasticity Finite Element (CPFE) modeling and subsequent calibration. Employing a rate-independent, single-crystal constitutive model, the calibration process integrates an interior-point optimi...

The present work addresses uncertainty quantification within the application of Markov-Random Fields (MRF) on the multi-scale modeling of microstructures. The aleatoric uncertainty of experimental measurements, as well as the epistemic uncertainty arising from computational microstructure reconstruction, is explored. The study is performed on the e...

The objective of this study is to investigate the effects of uncertainty of temperature and crystallographic orientations on the homogenized stress–strain response of Ti-6Al-4V alloy. The dual-phase Ti-6Al-4V alloy is an exceptional candidate for various applications in the aerospace field owing to its remarkable specific strength and significant m...

Studying contact between engineering surfaces requires measuring their surface topography, which is time-consuming and requires the use of sophisticated equipment. Thus, to reduce the need for intricate measurements, researchers have implemented algorithms to numerically generate surface topography that, e.g., imitates the texture resulting from di...

Materials design aims to identify the material features that provide optimal properties for various engineering applications, such as aerospace, automotive, and naval. One of the important but challenging problems for materials design is to discover multiple polycrystalline microstructures with optimal properties. This paper proposes an end-to-end...

Cellular materials widely exist in natural biologic systems such as honeycombs, bones, and woods. With advances in additive manufacturing, research on cellular metamaterials is emerging due to their unique mechanical performance. However, the design of on-demand cellular metamaterials usually requires solving a challenging inverse design problem fo...

Computational experiments are exploited in finding a well-designed processing path to optimize material structures for desired properties. This requires understanding the interplay between the processing-(micro)structure-property linkages using a multi-scale approach that connects the macro-scale (process parameters) to meso (homogenized properties...

The present work uses Markov Random Field (MRF) algorithm to construct large-scale and statistically-equivalent samples from small-scale experimental data of metallic microstructures. While the MRF method can build such digital material representations in large computational domains, its algorithmic stochasticity (epistemic uncertainty) causes vari...

The present study addresses a multiscale and stochastic design approach for polycrystalline microstructures to achieve a zone of isotropic elastic properties to improve the predictability of the performance of aerospace components. The microstructures are modeled with Orientation Distribution Function (ODF), which is related to the volume densities...

Deciding a gear pair’s macrogeometry parameters requires consideration of the factors such as cost of production, gear strength, and noise contributing parameters. A practical engineering problem is encountered when best possible gear geometry is needed for a fixed center distance to achieve the conflicting objectives. Further, constraints of pract...

The present work addresses multiscale modeling for grain topology of polycrystalline microstructures under the effects of the microstructural uncertainties. The special focus is on the titanium-7wt%-aluminum alloy (Ti-7Al), which is a candidate material for many aerospace systems owing to its outstanding mechanical performance in elevated temperatu...

View Video Presentation: https://doi.org/10.2514/6.2022-1438.vid The present study addresses a multi-scale and stochastic design approach for polycrystalline microstructures to achieve a zone of isotropic elastic properties to improve the predictability of the performance of aerospace components. The microstructures are modeled with Orientation Dis...

View Video Presentation: https://doi.org/10.2514/6.2022-1424.vid This work discusses new methodologies for identifying the grain boundaries in color imagesof metallic microstructures and the quantification of their grain topology. Grain boundarieshave a large impact on the macro-scale material properties. Particularly, this work employs theexperime...

View Video Presentation: https://doi.org/10.2514/6.2022-0811.vid Mechanical metamaterials are unique materials in that their properties are dependent on the internal microstructure of the material rather than the material it is constructed from. Mechanical materials have gained special attention due to the ability of designing their microstructures...

View Video Presentation: https://doi.org/10.2514/6.2022-1135.vid Cellular mechanical metamaterials (CMMs) obtain their unique mechanical properties mainly from their unique architectures. However, achieving the optimum structures corresponding to the given requirements can be challenging due to the complex relationship between physical structures a...

View Video Presentation: https://doi.org/10.2514/6.2022-1631.vid This manuscript summarizes recent efforts towards computing structural distortion and residual stresses during an additive friction stir deposition manufacturing process that consists of depositing layers of hot material on a substrate. Different strategies are considered to reduce th...

View Video Presentation: https://doi.org/10.2514/6.2022-0504.vid Ferromagnetic materials have been widely used in magneto-mechanical devices such as sensors, motors, generators, and transformers. However, these materials lose their magnetism during the ferromagnetic to the paramagnetic phase transition that occurs at the critical Curie temperature....

View Video Presentation: https://doi.org/10.2514/6.2022-2106.vid The present work addresses multi-scale modeling for grain topology of polycrystalline microstructures under the effects of the microstructural uncertainties. The special focus is on the Titanium-7wt\%-Aluminum alloy (Ti-7Al), which is a candidate material for many aerospace systems ow...

This study addresses a machine learning (ML)-reinforced strategy to build both linear and non-linear property closures for metallic materials. A property closure is a closed space of material properties that contains all possible values of the closure variables. The material properties of metals are significantly dependent on the underlying microst...

Uncertainty in the microstructures has a significant influence on the material properties. The microstructural uncertainty arises from the fluctuations that occur during thermomechanical processing and can alter the expected material properties and performance by propagating over multiple length scales. It can even lead to the material failure if t...

The present work addresses the microstructure reconstruction of forged and additively manufactured materials using Markov Random Field (MRF) approach and the principal of image moments. The MRF based reconstruction is performed for the experimental samples to predict the spatial evolution of the microstructures on a larger scale. To achieve a high-...

This paper presents a de-novo computational design method driven by deep reinforcement learning to achieve reliable predictions and optimum properties for periodic microstructures. With recent developments in 3-D printing, microstructures can have complex geometries and material phases fabricated to achieve targeted mechanical performance. These ma...

Uncertainty in the microstructures has a significant influence on the material properties. The microstructural uncertainty arises from the fluctuations that occur during thermomechanical processing and can alter the expected material properties and performance by propagating over multiple length-scales. It can even lead to the material failure if t...

This work addresses machine learning (ML) reinforced robust modeling of metallic microstructures under the effects of the uncertainties. The presented methodology is applied to explore the elasto-plastic deformation behavior of Titanium-7wt%Aluminum (Ti-7Al) alloy. Although it is a candidate aerospace material owing to the outstanding mechanical pe...

The present study addresses a computational design strategy to determine the optimum placement of 2D-microstructures in a component experiencing external forces. The advancement of the 3D-printing technologies has enabled the fabrication of microstructures having a large variety of geometries ranging from nano- to microscale sizes. This kind of tec...

The present work addresses a new methodology for uncertainty quantification (UQ) of texturing and grain topology of metallic microstructures. Special focus is on the quantification of the uncertainties in grain shapes by utilizing the concept of shape moment invariants in physics. According to this concept, the shape of a physical object can be mat...

The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques. JARVIS is motivated by the Materials Genome Initiative (MGI) principles of developing open-a...

The present work addresses a stochastic computational solution to define the property closures of polycrystalline materials under uncertainty. The uncertainty in material systems arises from the natural stochasticity of the microstructures as a result of the fluctuations in deformation processes. The microstructural uncertainty impacts the performa...

The present work addresses the microstructural design of Galfenol and Alfenol, which are magnetostrictive materials used in bending mode energy harvesters. Galfenol and Alfenol have been attracting much interest during the last few decades as they provide bi-directional coupling between mechanical and magnetic properties. This magneto-mechanical co...

We present a new sampling method for the multi-scale design of polycrystalline materials, which improves the computational time efficiency compared to the existing computational approaches. The solution strategy aims to find microstructure designs that optimize component-scale mechanical properties. The microstructure is represented with a probabil...

This Paper addresses a two-step computational approach to building a robust modeling environment for the titanium–aluminum alloy, Ti-7Al, a candidate aerospace material owing to superior mechanical performance under high stresses. To be used in aerospace applications, the large deformation behavior of the alloy should be investigated with a high-fi...

The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques. JARVIS is motivated by the Materials Genome Initiative (MGI) principles of developing open-a...

The present work addresses the design of β-Titanium alloy, TNTZ, microstructure to be used in biomedical applications as implant materials. The TNTZ alloy has recently started to attract interest in the area of biomedical engineering as it can provide elastic modulus values that are comparable to the modulus of the human bone. Such a match between...

The present work addresses a stochastic computational solution to define the property closures of polycrystalline materials under uncertainty. The uncertainty in material systems arises from the natural stochasticity of the microstructures and the variations in deformation processes, and impacts the performance of engineering components by causing...

The present study addresses the integration of an analytical uncertainty quantification approach to multi-scale modeling of single-walled carbon nanotube (SWNT)-epoxy nanocomposites. The main highlight is the investigation of the stochasticity of nanotube orientations, and its effects on the homogenized properties. Even though the properties of SWN...

The present work addresses a machine learning approach to study the linkage between deformation processing and microstructural texture evolution. The texture evolution is represented with a one-point probability descriptor, Orientation Distribution Function (ODF) by employing a rate-independent single crystal plasticity model. The ODF relates to th...

The present study addresses an inverse problem for observing the microstructural stochasticity given the variations in the macro-scale material properties by developing an analytical uncertainty quantification (UQ) model called AUQLin. The uncertainty in the material property is modeled with the analytical algorithm, and then the uncertainty propag...

This work addresses various mathematical solution strategies adapted for design optimization of multi-phase materials. The goal is to improve the structural performance by optimizing the distribution of multiple phases that constitute the material. Examples include the optimization of multi-phase materials and composites with spatially varying fibe...

The present study addresses the multi-scale computational modeling of a lightweight Aluminum-Lithium (Al-Li) 2070 alloy. The Al-Li alloys display significant anisotropy in material properties because of their strong crystallographic texture. To understand the relationships between processing, microstructural textures at different material points an...

Microstructure sensitive design has a critical impact on the performance of engineering materials. The safety and performance requirements of critical components, as well as the cost of material and machining of Titanium components, make dovetailing of the microstructure imperative. This paper addresses the optimization of several microstructure de...

A novel problem in computational materials modeling is addressed: “Are the computational microstructure reconstruction techniques reliable enough to replace experiments?". Here, “reliable" computations are associated with producing “expected" reconstructions which are adequately close to the experimental data. The output of computational techniques...

A microstructure design approach utilizing a discrete adjoint sensitivity analysis scheme is addressed. The microstructure is modeled with a one-point probability descriptor, known as Orientation Distribution Function (ODF). The ODF measures the volume densities of the unique orientations in a polycrystalline material. It can be discretized within...

The present work addresses a Gaussian process-based multi-fidelity computational scheme to enable the crystal plasticity modeling of Ti-7Al alloy. The crystal plasticity simulations are performed by using computational techniques that lead to two different solution fidelities. The first technique involves the use of a one-point probability descript...

This work addresses the integration of an analytical uncertainty quantification approach to multi-scale modeling of singlewalled carbon nanotube (SWNT)-epoxy nanocomposites consisting of pristine systems. The computational modeling starts with the dendrimer growth approach, which is used to build an epoxy- SWNT network. Next, the molecular dynamics...

Microstructure design can have a substantial effect on the performance of critical components in numerous aerospace applications. However, the stochastic nature of metallic mi-crostructures leads to deviations in material properties from the design point and alters the performance of these critical components. In this paper, we have developed a nov...

Microstructure design has been traditionally addressed as a deterministic optimization problem. However, the microstructures are inherently stochastic, and their stochastic nature can lead to deviations in material properties. The current state of the art focuses on the direct uncertainty quantification (UQ) problem such that the effect of microstr...

The present work addresses representation of texture evolution in face-centered cubic (FCC) microstructures in cubochoric orientation space. The microstructure is quantified with Orientation Distribution Function (ODF), which models volume density in the fundamental region of crystallographic space. The ODF is discretized using a finite element sch...

Microstructures are stochastic by their nature. These aleatoric uncertainties can alter the expected material performance substantially and thus they must be considered when designing materials. One safe approach would be assuming the worst case scenario of uncertainties in design. However, design under the worst case conditions can lead to over-co...

Microstructures significantly impact the performance of sensitively engineered components, such as wireless impact detectors used in military vehicles or sensors used in aircrafts. These components can operate safely only within a certain range of frequencies, and frequencies outside that range can lead to instability because of resonance. This pap...

An orientation distribution function based model is used for micromechanical modeling of the titanium-aluminum alloys, Ti-0 wt % Al and Ti-7 wt % Al, which are in demand for many aerospace applications. This probability descriptor based modeling approach is different than crystal plasticity finite element techniques since it computes the averaged m...

Microstructure design can have a substantial effect on the performance of critical components in numerous aerospace applications. However, the stochastic nature of metallic microstructures leads to deviations in material properties from the design point, and alters the performance of these critical components. In this work, an inverse stochastic de...

Electron backscatter diffraction (EBSD) scans are an important experimental input for microstructure generation and homogenization. Multiple EBSD scans can be used to sample the uncertainty in orientation distribution function (ODF), both point-to-point within a specimen as well as across multiple specimens that originate from the same manufacturin...

Experimental pole figures are an important input for microstructure homogenization models. In this paper, we derive an exact analytical formulation to quantify the uncertainties in homogenized properties due to uncertainty in the experimentally measured pole figures. The pole figures are acquired from a set of Ti-7Al alloy samples. These samples we...

Microstructure design can have a substantial effect on the performance of critical components in numerous aerospace applications. However, the stochastic nature of metallic microstructures leads to deviations in material properties from the design point, and alters the performance of these critical components. In this work, an inverse stochastic de...

Electron backscatter di�ffraction (EBSD) scans are an important experimental input for microstructure generation and homogenization. Multiple EBSD scans can be used to sample the uncertainty in orientation distribution function (ODF), both point-to-point within a specimen as well as across multiple specimens that originate from the same manufacturi...

This paper addresses a two step linear solution scheme to find an optimum metallic microstructure satisfying performance needs and manufacturability constraints. The mi-crostructure is quantified using the orientation distribution function (ODF), which determines the volume densities of crystals that make up the polycrystal microstructure. The ODF...

The following problem is addressed: 'Can one synthesize microstructure evolution over a large area given experimental movies measured over smaller regions?' Our input is a movie of microstructure evolution over a small sample window. A Markov Random Field (MRF) algorithm is developed that uses this data to estimate the evolution of microstructure o...

Automated fiber placement technology has pushed for the need to explore nonconventional fiber paths in laminated composites. This paper investigates optimal spatially varying fiber paths in a symmetric linear orthotropic laminate, which could increase the critical buckling temperature under uniform applied thermal loads. The key idea here is to ach...

Microstructures have a significant effect on the performance of critical components in numerous aerospace metallic material applications. Examples include panels in airframes that are exposed to high temperatures and sensors used for vibration tuning. This paper addresses the techniques to optimize the microstructure design for polycrystalline meta...

Automated fiber placement (AFP) technology has pushed for the need to explore non-conventional fiber paths in laminated composites. This paper investigates optimal spatially varying fiber paths in a symmetric linear orthotropic laminate which could increase the critical buckling temperature under uniform applied thermal loads. The key idea here is...

Microstructures have a significant effect on the performance of critical components in numerous aerospace metallic material applications. Examples include panels in airframes that are exposed to high temperatures and sensors used for vibration tuning. This paper addresses the techniques to optimize the microstructure design for polycrystalline meta...

In this study, we focus on static and dynamic aeroelastic analyses of the HIRENASD wing based on reference experimental data for two different flight conditions for Aeroelastic Prediction Workshop-1. The major anticipations from the HIRENASD project are to improve the knowledge about aero-structural dynamics, and to get experimental and computation...