About me

I am Yuksel, a data scientist specializing in materials science and engineering. I have over a decade of experience in applications of data analytics, statistics, machine learning and artificial intelligence to material science and engineering problems. I am interested in extending novel machine learning and AI appraoches to material science for advanced material design and development.

Education

Ph.D. in Mechanical Engineering, May, 2017
Georgia Institute of Technology, GA, USA

M.Sc. in Mechanical Engineering, December, 2008
Istanbul Technical University, Istanbul, Turkey

B.Sc. in Mechanical Engineering, June, 2006
Istanbul Technical University, Istanbul, Turkey

Employment

Research Staff Member, December 2021 - Continues
HRL Laboratories LLC, Malibu, CA, USA

Materials Data Scientist, April 2020 - December 2021
Multiscale Technologies LLC, Atlanta, GA, USA

Research Engineer I, November 2018 - April 2020
The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA

Postdoctoral Fellow, June 2017 - November 2018
The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA

Graduate Research Assistant, January 2013 - June 2017
The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA

Graduate Research Assistant, January 2012 - January 2013
Department of Mechanical Engineering and Mechanics, Drexel University, Philadelphia, PA, USA

Graduate Research Assistant, April 2008 - March 2009
School of Mechanical Engineering, Istanbul Technical University, Istanbul, Turkey

Publications (from most recent)

Book chapters

• Y.C. Yabansu, S.R. Kalidindi, Data analytics on phase-field simulation datasets. Big, Deep, and Smart Data in the Physical Sciences, World Scientific Publishers, 2018 [+abstract] [link]
  Phase-field method has become one of the broadly adopted approaches for studying details of microstructure evolution during various materials processing conditions. Although the phase-field method has demonstrated tremendous success in simulating the microstructure evolution under diverse processing conditions, these methods have not yet been successfully employed in multi-scale materials design explorations. Such explorations demand solutions in the inverse direction, where one aims to identify the specific set of process parameters that result in the desired microstructures. This chapter presents a recently developed data science framework called materials knowledge system (MKS) for addressing this challenge. At its core, the MKS employs analytics tools that can efficiently and accurately capture the salient details of the material microstructure in high-value low-dimensional forms that result in reliable and robust reduced-order process–structure–property (PSP) linkages that can be used effectively in multi-scale materials design explorations. The versatility of the MKS framework will be demonstrated through selected case studies..

Peer-reviewed articles

• X. Gong, Y.C. Yabansu, P.C. Collins, S.R. Kalidindi, Evaluation of Ti–Mn Alloys for Additive Manufacturing Using High-Throughput Experimental Assays and Gaussian Process Regression. Materials, 2020. 13(20): p. 4641 [+abstract] [link] [pdf]
  Compositionally graded cylinders of Ti–Mn alloys were produced using the Laser Engineered Net Shaping (LENS™) technique, with Mn content varying from 0 to 12 wt.% along the cylinder axis. The cylinders were subjected to different post-build heat treatments to produce a large sample library of α–β microstructures. The microstructures in the sample library were studied using back-scattered electron (BSE) imaging in a scanning electron microscope (SEM), and their mechanical properties were evaluated using spherical indentation stress–strain protocols. These protocols revealed that the microstructures exhibited features with averaged chord lengths in the range of 0.17–1.78 μm, and beta content in the range of 20–83 vol.%. The estimated values of the Young’s moduli and tensile yield strengths from spherical indentation were found to vary in the ranges of 97–130 GPa and 828–1864 MPa, respectively. The combined use of the LENS technique along with the spherical indentation protocols was found to facilitate the rapid exploration of material and process spaces. Analyses of the correlations between the process conditions, several key microstructural features, and the measured material properties were performed via Gaussian process regression (GPR). These data-driven statistical models provided valuable insights into the underlying correlations between these variables.
• S. Parvinian, Y.C. Yabansu, A. Khosravani, H. Garmestani, S.R. Kalidindi, High-throughput exploration of the process space in 18% Ni(350) maraging steel via spherical indentation stress-strain protocols and Gaussian process models. Integrating Materials and Manufacturing Innovation, 2020. 9: p. 199-212 [+abstract] [link]
  Several challenges are encountered in the development of maraging steels with desired combinations of mechanical properties. These include the need to explore a large process design space, the time- and effort-consuming standardized testing protocols for property evaluations, and the lack of a formal design of experiments strategy that guides the selection of the process conditions for the next set of experiments based on a thorough analyses of the previously accumulated data. In this work, we explored the effect of the different combinations of the aging temperature and the aging time on the yield strength of 350-grade maraging steels using highthroughput protocols. For this purpose, a total of 21 small volumes of differently aged samples were produced and studied using spherical nanoindentation stress-strain protocols. Furthermore, the results of these tests were modeled using Gaussian process regression (GPR) to establish datadriven linkages between the yield strengths and the aging parameters. The predicted yield strengths were found to be in reasonable agreement with the experimentally measured ones. It is also demonstrated that the GPR model can provide objective guidance in the selection of the next set of experiments.
• Y.C. Yabansu, P. Altschuh, J. Hotzer, M. Selzer, B. Nestler, S.R. Kalidindi, A digital workflow for learning the reduced-order structure-property linkages for permeability of porous membranes. Acta Materialia, 2020. 195: p. 668-680 [+abstract] [link]
  Quantitative relationships between the complex porous structure of a membrane (henceforth simply referred to as microstructure) and its effective permeability are critical for improving the performance of membranes used in filtration and separation applications. This paper presents a digital workflow for learning the porous structure-permeability linkages in membranes. The presented workflow establishes the desired linkages by bringing together recent advances in (i) digital generators for three-dimensional representative volume elements (3-D RVEs) reflecting a large and diverse set of porous structures, (ii) numerical approaches for reliable evaluation of permeability of 3D-RVEs, (iii) low dimensional representation of material internal structure using the framework of 2-point spatial correlations and principal component analyses, and (iv) Gaussian process (GP) regression with input-dependent noise (i.e., heteroscedasticity). It is seen that the digital workflow presented in this study can systematically identify the salient features of the 3-D membrane microstructure and train reduced-order heteroscedastic GP models on the data generated using digital microstructure generators and physics-based permeability simulations. It will be shown that the structure-property linkages are able to make high fidelity predictions and assessment of uncertainties for new porous membrane structures at minimal computational cost.
• Y.C. Yabansu, V. Rehn, J. Hotzer, B. Nestler, S.R. Kalidindi, Application of Gaussian process autoregressive models for capturing the time evolution of microstructure statistics from phase-field simulations for sintering of polycrystalline ceramics. Modelling and Simulation in Material Science and Engineering, 2019. 27: p. 084006 [+abstract] [link]
  While phase-field models have been demonstrated to be highly versatile in performing physics-based simulations of a large variety of materials phenomena involving microstructure evolution (e.g. phase transformation, recrystallization, sintering), they are not practical for rapid exploration of the process design space due to their high demand for computational resources. The extraction of reliable and robust reduced-order models from the microstructure evolution datasets produced by such sophisticated physics-based models continues to be an unsolved problem. Recent advances in the fast computation of a comprehensive set of microstructure statistics and their data-driven low-dimensional representations using principal component analyses have resulted in the successful extraction of practically useful reduced-order models connecting the microstructure statistics and the effective properties exhibited by the material. In this paper, we explore for the first time, the viability of these low-dimensional representations of the microstructure statistics for establishing reduced-order models capable of learning the important details of the microstructure evolution predicted by the computationally expensive phase-field models. More specifically, we will explore the viability of applying the Gaussian process autoregressive models used in the fields of statistics and signal processing for problems in microstructure evolution. This will be accomplished using a specific case study dealing with the time evolution of porous microstructures in sintering of polycrystalline ceramics.
• Y.C. Yabansu, A. Iskakov, A. Kapustina, S. Rajagopalan, S.R. Kalidindi, Application of Gaussian process regression models for capturing the evolution of microstructure statistics in aging of Nickel-based superalloys. Acta Materialia, 2019. 178: p. 45-58 [+abstract] [link]
  Nickel-based superalloys, used extensively in advanced gas turbine engines, exhibit complex microstructures that evolve during exposure to high temperatures (i.e., aging treatments). In this work, we examine critically if the principal component (PC) representation of rotationally invariant 2-point spatial correlations can adequately capture the salient features of the microstructure evolution in the thermal aging of the superalloys. For this purpose, an experimental study involving microstructure characterization of 27 differently aged (i.e., different combinations of temperature and time of exposure) samples was designed and conducted. Of these, 23 samples were employed for training a Gaussian Process Regression (GPR) model that took the aging temperature and the aging time as inputs, and predicted the microstructure statistics as output. The viability of the approach described above was evaluated critically by comparing the predictions for the four samples that were not used in the training of the GPR model. Furthermore, a new strategy was developed and implemented to generate digital microstructures corresponding to the predicted microstructure statistics. The predicted microstructures were found to be in good agreement with the experimentally measured one, validating the novel framework presented in this work.
• P. Fernandez-Zelaia, Y.C. Yabansu, S.R. Kalidindi, A comparative study of the efficacy of local/global and parametric/nonparametric machine learning methods for establishing structure-property linkages in high contrast 3-D elastic composites. Integrating Materials and Manufacturing Innovation, 2019. 8: p. 67-81 [+abstract] [link]
  Reduced-order structure–property (S-P) linkages play a pivotal role in the tailored design of materials for advanced engineering components. There is a critical need to distill these from the simulation datasets aggregated using sophisticated, computationally expensive, physics-based numerical tools (e.g., finite element methods). The recent emergence of materials data science approaches has opened new avenues for addressing this challenge. In this paper, we critically compare the relative merits of the application of four distinct machine learning approaches for their efficacy in extracting microstructure-property linkages from the finite element simulation data aggregated on high-contrast elastic composites with different microstructures. The machine learning approaches selected for the study have included different combinations of local/global and parametric/nonparametric approaches. Furthermore, the nonparametric approaches selected for this study are based on Gaussian Process (GP) models that allow for a formal treatment of uncertainty quantification in the predicted values. The predictive performances of these different approaches have been compared against each other using rigorous cross-validation error metrics. Furthermore, their sensitivity to both the dataset size and dimensionality has been investigated.
• Z. Yang, Y.C. Yabansu, D. Jha, W. Liao, A.N. Choudhary, S.R. Kalidindi, A. Agrawal, Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches. Acta Materialia, 2019. 166: p. 335-345 [+abstract] [link]
  Data-driven methods are attracting growing attention in the field of materials science. In particular, it is now becoming clear that machine learning approaches offer a unique avenue for successfully mining practically useful process-structure-property (PSP) linkages from a variety of materials data. Most previous efforts in this direction have relied on feature design (i.e., the identification of the salient features of the material microstructure to be included in the PSP linkages). However due to the rich complexity of features in most heterogeneous materials systems, it has been difficult to identify a set of consistent features that are transferable from one material system to another. With flexible architecture and remarkable learning capability, the emergent deep learning approaches offer a new path forward that circumvents the feature design step. In this work, we demonstrate the implementation of a deep learning feature-engineering-free approach to the prediction of the microscale elastic strain field in a given three-dimensional voxel-based microstructure of a high-contrast two-phase composite. The results show that deep learning approaches can implicitly learn salient information about local neighborhood details, and significantly outperform state-of-the-art methods.
• A. Cecen, Y.C. Yabansu, S.R. Kalidindi, A new framework for defining and computing rotationally invariant two-point spatial correlations in microstructure datasets. Acta Materialia, 2018. 158: p. 53-64 [+abstract] [link]
  Quantification of the material internal structure (i.e., microstructure) is central to establishing the highly sought-after process-structure-property (PSP) relationships central to any materials design effort. In recent years, two-point spatial correlations (a subset of the n-point spatial correlations) have garnered significant attention because of their tremendous potential in arriving at practically useful PSP linkages. The central advantage of the two-point spatial correlations is that they capture an exceedingly large number of directionally resolved microstructure statistics. However, they are sensitive to the selection of the observer reference frame. In a number of practical applications, there is a critical need to establish the directionally resolved microstructure statistics, while attaining invariance to the observer reference frame (i.e., the statistics extracted are independent of the selection of the observer frame). A framework for defining and computing such observer-frame invariant two-point spatial correlations does not exist at the present time. This paper addresses this gap by introducing a new form of two-point spatial correlations, hereafter called rotationally invariant two-point spatial correlations. The theoretical framework for these new rotationally invariant two-point spatial correlations is introduced in this paper, and demonstrated through a comprehensive case study.
• Z. Yang, Y.C. Yabansu, R. Al-Bahrani, W. Liao, A.N. Choudhary, S.R. Kalidindi, A. Agrawal, Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets. Computational Materials Science, 2018. 151: p. 278-287 [+abstract] [link]
  Data-driven methods are emerging as an important toolset in the studies of multiscale, multiphysics, materials phenomena. More specifically, data mining and machine learning methods offer an efficient toolset for extracting and curating the important correlations controlling these multiscale materials phenomena in high-value reduced-order forms called process-structure-property (PSP) linkages. Traditional machine learning methods usually depend on intensive feature engineering, and have enjoyed some success in establishing the desired PSP linkages. In contrast, deep learning approaches provide a feature-engineering-free framework with high learning capability. In this work, a deep learning approach is designed and implemented to model an elastic homogenization structure-property linkage in a high contrast composite material system. More specifically, the proposed deep learning model is employed to capture the nonlinear mapping between the three-dimensional material microstructure and its macroscale (effective) stiffness. It is demonstrated that this end-to-end framework can predict the effective stiffness of high contrast elastic composites with a wide of range of microstructures, while exhibiting high accuracy and low computational cost for new evaluations.
• A. Cecen, H. Dai, Y.C. Yabansu, S.R. Kalidindi, L. Song, Material structure-property linkages using three-dimensional convolutional neural network. Acta Materialia, 2018. 146: p. 76-84 [+abstract] [link]
  The core materials knowledge needed in the accelerated design, development, and deployment of new and improved materials is most accessible when cast in the form of computationally low cost (reduced-order) and reliable process-structure-property (PSP) linkages. Quantification of the material structure (also referred as microstructure) is the core challenge in this task. Conventionally, microstructure quantification has been addressed using highly simplified measures suggested by the governing physics, with the list of measures often suitably augmented by the intuition of the materials expert. In this paper, we develop an objective (data-driven) approach to efficiently and accurately link a three-dimensional (3-D) microstructure to its effective (homogenized) properties. Our method employs a 3-D convolutional neural network (CNN) to learn the salient features of the material microstructures that lead to good predictive performance for the effective property of interest. We then utilize 3-D CNN learned features as estimators of higher-order spatial correlations, and formulate an integrated framework combining 3-D CNN features with 2-point spatial correlations. In this work, we created an extremely large microstructure-property benchmark dataset of 5900 microstructures, and demonstrated that our CNN based approach not only learns interpretable microstructure features, but also leads to improved accuracy in property predictions for new microstructures, while achieving a dramatic reduction in the computation time.
• A. Iskakov, Y.C. Yabansu, A. Kapustina, S. Rajagopalan, S.R. Kalidindi, Application of spherical indentation and the materials knowledge system framework to establish microstructure-yield strength linkages from carbon steel scoops excised from high-temperature exposed components. Acta Materialia, 2018. 144: p. 758-767 [+abstract] [link]
  Industrial power generation turbines operate at elevated temperatures for prolonged periods of time (around 100,000 h) which leads to significant microstructure evolution and mechanical property changes. Due to physical and structural constraints of operational turbines, only small scoop samples can be excised from heat-exposed steel components. Scoop samples at various service time intervals provide valuable information on microstructure changes, for example on graphite formation in carbon steels, with increasing service time. However, mechanical evaluation of such small material volumes poses significant challenges using conventional tests. A novel spherical microindentation technique is applied to evaluate a library of scoop samples ranging between 0 and 99,000 h of service. Furthermore, microstructure and yield strength data for the different exposure periods is used to construct a structure-property (S-P) linkage using the MKS homogenization approach that employs spatial correlations, principal component analysis, and regression techniques. The accuracy of the extracted S-P linkage was assessed on new samples that were not included in the calibration set.
• P. Altschuh, Y.C. Yabansu, J. Hotzer, M. Selzer, B. Nestler, S.R. Kalidindi, Data science approaches for microstructure quantification and feature identification in porous membranes. Journal of Membrane Science, 2017. 540: p. 88-97 [+abstract] [link]
Rigorous quantification of porous microstructures exhibiting a wide variety of pore shapes, sizes, and their spatial distributions presents a significant challenge. In this work, novel data science approaches are used to characterize the complex microstructures in porous membranes, and to extract the salient features at the pore-scale. Towards this goal, a microstructure generator is developed and utilized to create a large ensemble of porous structures covering a substantial range in measures of features such as the stretched pore shapes (geometrical anisotropy), porosity, specific surface, and pore sizes. Additionally, the morphology of real porous membranes are obtained experimentally by high resolution X-ray tomography. The statistical representations for the simulated and real membrane microstructures are calculated and compared rigorously using novel data science approaches that are based on principal component analyses of the 2-point spatial correlations. This approach allows an objective measure of the difference between any two selected microstructures. The versatility and benefits of this approach for the quantification of microstructures in porous membranes are demonstrated in this paper.
• R. Liu, Y.C. Yabansu, A. Agrawal, S.R. Kalidindi, A. N. Choudhary, Context aware machine learning approaches for modeling elastic localization linkages in three dimensional composite microstructures. Integrating Materials and Manufacturing Innovation, 2017. 6: p. 160-171 [+abstract] [link]
  The response of a composite material is the result of a complex interplay between the prevailing mechanics and the heterogenous structure at disparate spatial and temporal scales. Understanding and capturing the multiscale phenomena is critical for materials modeling and can be pursued both by physical simulation-based modeling as well as data-driven machine learning-based modeling. In this work, we build machine learning-based data models as surrogate models for approximating the microscale elastic response as a function of the material microstructure (also called the elastic localization linkage). In building these surrogate models, we particularly focus on understanding the role of contexts, as a link to the higher scale information that most evidently influences and determines the microscale response. As a result of context modeling, we find that machine learning systems with context awareness not only outperform previous best results, but also extend the parallelism of model training so as to maximize the computational efficiency.
• Y.C. Yabansu, P. Steinmetz, J. Hotzer, S.R. Kalidindi, B. Nestler, Extraction of Reduced-Order Process-Structure Linkages from Phase-field simulations. Acta Materialia, 2017. 124: p. 182-194 [+abstract] [link]
  Phase-field simulations have achieved notable success in capturing characteristic details of microstructure evolution in directional solidification of ternary eutectic alloys. In spite of the impressive advances in high performance computations, phase-field simulations for most practical problems in materials design are resource intensive because of the need to incorporate multiple physical fields over large-scale three-dimensional domains. There is, therefore, a need to learn and capture the underlying materials knowledge embedded in the results produced by such expensive simulations, and facilitating an easy transferability to new problems of interest. This paper demonstrates the viability of extracting the salient process-structure linkages from phase-field simulations, while casting them in forms amenable for a rapid and efficient exploration of a relatively large process space. The presented framework is based on low dimensional representation of material structure obtained through principal component analysis (PCA) of 2-point spatial correlations.
• A. Choudhury, Y.C. Yabansu, A. Dennstedt, S.R. Kalidindi, Quantification and classification of microstructures in ternary alloys using 2-point spatial correlations and principal component analyses. Acta Materialia, 2016. 110: p. 131-141 [+abstract] [link]
  Eutectic solidification gives rise to a number of distinct microstructure patterns that might include lamella, rods and labyrinths in binary alloys. However, as the number of phases and components increases, the number of possible patterns that might be obtained during bulk solidification also become larger. While the morphological attributes of binary eutectic solidification have been fairly well understood, the same is not true for ternary and higher multicomponent alloys. In this paper, we study and quantify microstructures in ternary alloys as a function of two essential parameters, namely, the volume fraction of the solid phases and the surface energies of the interfaces (in particular the solid–liquid interfaces). For the selected ensemble of microstructures, quantification and classification were carried out using a recently developed data-driven (objective) approach based on principal component analyses of 2-point correlations. It is demonstrated that the method is capable of analyzing and quantifying the similarity/difference measures between the elements of the selected ensemble of microstructures.
• R. Liu, Y.C. Yabansu, A. Agrawal, S.R. Kalidindi, A. N. Choudhary, Machine learning approaches for elastic localization linkages in high contrast composite materials. Integrating Materials and Manufacturing Innovation, 2015. 4: p. 1-13 [+abstract] [link] [pdf]
  There has been a growing recognition of the opportunities afforded by advanced data science and informatics approaches in addressing the computational demands of modeling and simulation of multiscale materials science phenomena. More specifically, the mining of microstructure–property relationships by various methods in machine learning and data mining opens exciting new opportunities that can potentially result in a fast and efficient material design. This work explores and presents multiple viable approaches for computationally efficient predictions of the microscale elastic strain fields in a three-dimensional (3-D) voxel-based microstructure volume element (MVE). Advanced concepts in machine learning and data mining, including feature extraction, feature ranking and selection, and regression modeling, are explored as data experiments. Improvements are demonstrated in a gradually escalated fashion achieved by (1) feature descriptors introduced to represent voxel neighborhood characteristics, (2) a reduced set of descriptors with top importance, and (3) an ensemble-based regression technique.
• P. Steinmetz, Y.C. Yabansu, J. Hotzer, M. Jainta, B. Nestler, S.R. Kalidindi, Analytics for microstructure datasets produced by phase-field models. Acta Materialia, 2016. 103: p. 192-203 [+abstract] [link]
  Phase-field simulations have become valuable tools in explorations of the effects of the processing parameters on the internal structure evolution of materials in a broad range of advanced materials. Simulations conducted on high performance computers have enabled the resolution of relatively large material volumes and have generated big datasets. Although such computations have captured faithfully the many features observed in the corresponding experiments, there has not yet been a broadly adopted framework to quantitatively analyze the predicted material structures and compare them rigorously with experiments. This paper demonstrates that the recently developed framework for the quantification of the material structure, based on the concepts of 2-point spatial correlations and principal component analyses (PCA), can address this critical need. It is further demonstrated that the adoption of a rigorous framework for structure quantification can help to establish objectively many of the modeling parameters and choices made in the simulations.
• Y.C. Yabansu, S.R. Kalidindi, Representation and calibration of elastic localization kernels for a broad class of cubic polycrystals. Acta Materialia, 2015. 94: p. 26-35 [+abstract] [link]
  Localization kernels play an important role in the study of hierarchical material systems with well separated length scales. They allow for a computationally efficient communication of critical information between the constituent length scales. They are particularly well suited for capturing how an imposed variable (e.g., stress or strain) at the higher length scale is spatially distributed at the lower length scale (i.e., localization linkages). In recent work, our research group has presented a novel framework called Materials Knowledge Systems (MKS) for the representation and calibration of the localization kernels, and demonstrated the viability of this approach on selected individual material systems. In this work, we present and demonstrate an important extension to the MKS framework that allows representation and calibration of the localization kernels for an entire class of materials (e.g., a selected class of single phase cubic polycrystalline materials).
• Y.C. Yabansu, D.K. Patel, S.R. Kalidindi, Calibrated localization relationships for elastic response of polycrystalline aggregates. Acta Materialia, 2014. 81: p. 151-160 [+abstract] [link]
  In recent years, our research group has formulated a new framework called materials knowledge systems (MKS) for establishing highly accurate reduced-order (surrogate) models for localization (opposite of homogenization) linkages in hierarchical materials systems. These new computationally efficient linkages are designed to capture accurately the microscale spatial distribution of a response field of interest in the representative volume element (RVE) of a material, when subjected to an imposed macroscale loading condition. In prior work, the viability and computational advantages of the MKS approach were demonstrated in a number of case studies involving multiphase composites, where the local material state in each spatial bin of the RVE was permitted to be any one of a limited number of material phases (i.e. restricted to a set of discrete local states of the material). In this paper, we present a major extension to the MKS framework that allows a computationally efficient treatment of a significantly more complex local state of the material, i.e. crystal lattice orientation. This extension of the MKS framework is formulated by the use of suitable Fourier representation of the influence functions. This paper describes this new formulation and the associated calibration protocols, and demonstrates its viability with case studies comprising low and moderate contrast cubic and hexagonal polycrystals.
• S.R. Niezgoda, Y.C. Yabansu, S.R. Kalidindi, Understanding and visualizing microstructure and microstructure variance as a stochastic process. Acta Materialia, 2011: p.6387-6400 [+abstract] [link]
  The study of microstructure–property relationships is a defining concept in the field of materials science and engineering. Despite the paramount importance of microstructure to the field a rigorous systematic framework for the description of structural variance between samples of materials with the same processing history and between different materials classes has yet to be adopted. Here the authors utilize the formalism of stochastic processes to develop a statistical definition of microstructure and develop measures of structural variance in terms of the measured variance of estimators of higher order probability distributions. Principal component analysis (PCA) of higher order distributions is used to produce visualization of the space spanned by an ensemble of microstructure realizations and for quantification of the structural variance within the ensemble. The structural variance is correlated with the variance in properties and structure/property maps are produced in the PCA space.

Conference Presentations

• Y.C. Yabansu, A. Iskakov, S. Rajagopalan, A. Kapustina, S.R. Kalidindi. Inverse solutions based on reduced-order process-structure-property linkages using Markov chain Monte Carlo sampling algorithms. TMS 149th Annual Meeting and Exhibition, February 2020, San Diego, CA. [+abstract]
  Deployment of advanced engineering materials in a commercial product can take multiple decades from the initial discovery. Process-structure-property (P-S-P) linkages play a critical role in designing the advanced engineering materials and they have been fairly well established since microstructure informatics tools became an integral part of building the linkages. P-S-P linkages where the flow of information occurs from process to property through material structural measures are deductive methodologies and are called forward P-S-P linkages. However, material design requires a goal/target oriented approach which aims to find the suitable processing/manufacturing conditions that correspond to tailored properties. Inverse solutions to P-S-P linkages depend on the accuracy and efficacy of the deducted information from forward P-S-P linkages. This study presents a novel framework that utilizes sampling algorithms to establish an inverse approach to forward P-S-P linkages for materials design.
• Y.C. Yabansu, V. Rehn, J. Hotzer, B. Nestler, S.R. Kalidindi. Building microstructure evolution linkages for sintering of polycrystalline ceramics. TMS 148th Annual Meeting and Exhibition, March 2019, San Antonio, TX. [+abstract]
  Sintering process plays a pivotal role in determining the properties of the polycrystalline ceramic materials. Phase-field method has been widely used to simulate the pore-grain growth interaction that occurs in sintering. However, phase-field method is a computationally expensive approach which limits the efficient exploration of the effect of pore size distribution on the microstructure evolution. In this study, a novel framework to build microstructure evolution linkages is presented to rapidly forecast the evolution of grain and pore size distributions in the form of chord length distributions. The evolution linkages are captured by autoregressive models which are calibrated on microstructure evolution time series accumulated through phase-field simulations. It will be shown through an extensive case study that the linkages can forecast the evolution of grain and pore size distributions with surpassing computational speeds and no significant losses in accuracy.
• Y.C. Yabansu, A. Cecen, S.R. Kalidindi. Formulation and calculation of rotationally invariant spatial correlation for microstructure datasets. TMS 148th Annual Meeting and Exhibition, March 2019, San Antonio, TX. [+abstract]
  Microstructure quantification plays a critical role in establishing process-structure-property linkages. The most complete method for microstructure quantification is n-point spatial correlations and they can capture the correlations between the distinct local states by accounting for both the distances and directionality. However, n-point correlations are sensitive to the observer rotations and this is a disadvantage in the cases where the sample frame is not known a priori. Pair correlation function removes the dependence to observer frame, however the valuable morphological anisotropy in the features are removed during the process. This study presents a novel framework that can effectively remove the dependence to observer frame without losing the critical morphological anisotropy of the features. The new framework is formulated for both 2-D and 3-D cases, and its viability is presented by comparing it to two-point spatial correlations and pair-correlation functions through a comprehensive case study.
• Y.C. Yabansu, A. Iskakov, S. Rajagopalan, A. Kapustina, S.R. Kalidindi. Bayesian Linear Regression and Kriging Methods for Uncertainty Quantification in Process-structure-property Linkages of Low Carbon Steels and Superalloys. TMS 147th Annual Meeting and Exhibition, March 2018, Phoenix, AZ. [+abstract]
  Process-structure-property (P-S-P) linkages play a critical role in advanced material design. However, there are uncertainties originating from variance in microstructure, processing conditions and property measurements. These uncertainties must be incorporated in the machine learning approaches to evaluate the performance of the linkages and their associated uncertainty. In this study, low dimensional representation of the microstructure was obtained through Materials Knowledge Systems (MKS). Then, Bayesian linear regression (BLR) and Kriging methods were employed to establish S-P linkages and P-S linkages, respectively. Bayesian linear regression approach was utilized to establish the linkages between the low carbon steel microstructures and their yield strength measurements obtained through instrumented microindentation. On the other hand, the Kriging approach was employed to link the processing parameters of stress and temperature to nickel based superalloy microstructures.
• Y.C. Yabansu, P. Altschuh, J. Hotzer, B. Nestler, S.R. Kalidindi. Structure-Property Linkages for Porous Membranes Using the Materials Knowledge System. TMS 147th Annual Meeting and Exhibition, March 2018, Phoenix, AZ. [+abstract]
  Porous membranes have been widely used in filtration applications of medical and environmental processes. The complex features of the porous structures in the membranes play a critical role in controlling their permeability and filtration characteristics. However, the porous membranes have so far been quantified mainly in terms of primitive structural features such as porosity. In this study, we demonstrate the benefits of the application of Materials Knowledge Systems (MKS) framework to quantify the membrane pore structures and extract the high value structure-property linkages. The data sets used included both experimental measurements obtained with micro-CT as well as synthetic structures generated with a novel membrane generator. The structures were quantified using 2-point spatial correlations and principal component analyses. The property considered was the effective permeability obtained through flow simulations.
• Y.C. Yabansu, L. Liang, L.Q. Chen, S.R. Kalidindi. Prediction of Microstructure Evolution in Phase-Field Simulations Through Data Analytics and Time Series. MS&T17 Technical Meeting and Exhibition, October 2017, Pittsburgh, PA. [+abstract]
  Phase-field simulations have proven to be successful in modeling and capturing the salient features of microstructures and their evolution through a processing history for several material systems and phenomena. However, the conventional phase-field simulations are computationally effort-intensive resulting in a slow and expensive exploration of microstructure space for the process-structure-property relationships. For the first time, a templated workflow is implemented for learning the physics involved in microstructure evolution in phase-field simulations through data analytics and time series. This study depicts the reduced order statistical representation of entire microstructure evolution in phase-field simulations through two-point statistics and principal-component analysis (PCA). It will be shown that the reduced order representation of microstructure can be effectively and efficiently used in a time-series approach to capture the physics of microstructure evolution in a compact metamodel with high reusability for future microstructures.
• Y.C. Yabansu, P. Steinmetz, J. Hotzer, M. Jainta, B. Nestler, S.R. Kalidindi. Evaluation of Phase-Field Models through Stochastic Quantification of Microstructure and Data Analytics. TMS 145th Annual Meeting & Exhibition, February 2016, Nashville, TN. [+abstract]
  Phase-field simulations have been conducted to explore interface interactions and phase transformations in the simulation of solidification process in ternary eutectic alloys. The evaluation of the accuracy and performance of phase field models are based on qualitative observations and very primitive quantitative measures that give inadequate information on the microstructure. An objective, rigorous and low dimensional representation of microstructure has yet to be developed to evaluate the effect of simulation parameters on microstructure. For this manner, 2-point spatial correlations and principal component analysis (PCA) are employed as a data-driven framework to track the microstructure evolution in a low dimensional space for the evaluation of simulation parameters. The viability of the application of data-driven framework will be demonstrated with a case study involving an ensemble of microstructure evolution histories for a selected set of processing parameters, and the accuracy of the phase field model is visualized by comparing the microstructures from simulations to results from experiments.
• Y.C. Yabansu, S.R. Kalidindi. Calibrated Localization Relationships for Polycrystalline Aggregates by using Materials Knowledge System. 3rd World Congress on Integrated Computational Materials Engineering, June 2015, Colorado Springs, CO. [+abstract]
  Multiscale modeling of material systems demands novel solution strategies to simulating physical phenomena that occur in a hierarchy of length scales. Majority of the current approaches involve one way coupling such that the information is transferred from a lower length scale to a higher length scale. To enable bi-directional scale-bridging, a new data-driven framework called Materials Knowledge System (MKS) has been developed recently. The remarkable advantages of MKS in establishing computationally efficient localization linkages (e.g., spatial distribution of a field in lower length scale for an imposed loading condition in higher length scale) have been demonstrated in prior work. In these prior MKS studies, the effort was focused on composite materials that had a finite number of discrete local states. As a major extension, in this work, the MKS framework has been extended for polycrystalline aggregates which need to incorporate crystal lattice orientation as a continuous local state. This extension of the MKS framework for elastic deformation of polycrystals is achieved by employing compact Fourier representations of functions defined in the crystal orientation space. The viability of this new formulation will be presented for several case studies involving single and multi-phase polycrystals.
• Y.C. Yabansu, S.R. Kalidindi. Strategies for Multiscale Materials Simulations Using Calibrated Metamodels. TMS 144th Annual Meeting & Exhibition, March 2015, Orlando, FL.
• Y.C. Yabansu, D.K. Patel, S.R. Kalidindi. Localization relationships for polycrystalline aggregates using Materials Knowledge System approach. 17th U.S. National Congress on Theoretical & Applied Mechanics, June 2014. [+abstract]
  In recent years, our research group has formulated a new framework called Materials Knowledge Systems (MKS) for establishing highly accurate metamodels for localization (opposite of homogenization) linkages in hierarchical materials systems. These computationally efficient linkages are designed to capture accurately the microscale spatial distribution of a response field of interest in the representative volume element (RVE) of a material, when subjected to an imposed macroscale loading condition. In prior work, the viability and computational advantages of the MKS approach were demonstrated in a number of case studies involving multiphase composites, where the local material state in each spatial bin of the RVE was permitted to be any one of a limited number of material phases (i.e., restricted to a set of discrete local states of the material). In this study, we present a major extension to the MKS framework that allows a computationally efficient treatment of significantly more complex local states of the material. In this study, we present an important extension of the MKS approach that permits calibration of the influence kernels of the localization linkages for an entire class of low to moderate contrast material systems as opposed to the prior protocols that addressed one material system at a time. For high contrast single phase and multi-phase polycrystals, the MKS series include higher order terms. These major advances in the MKS framework are facilitated by the use of suitable Fourier representations of the influence functions. This study describes this new formulation and the associated calibration protocols, and demonstrates its viability with case studies of different material systems.
• Y.C. Yabansu, D.K. Patel, J.B. Shaffer & S.R. Kalidindi. Localization Relationships for Elastic Deformation of Cubic Polycrystalline Aggregates. Society of Engineering Science, 49th Annual Conference, October 2012.

Research goes here !