Search NASA⌕ Search

Engineering topics

Kalidindi, Surya R.

Publications and source records attributed to Kalidindi, Surya R..

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Active learning for the design of polycrystalline textures using conditional normalizing flows

Generative modeling has opened new avenues for solving previously intractable materials design problems. However, these new opportunities are accompanied by a drastic increase in the required amount of training data. This is in stark juxtaposition to the high expense and difficulty in curating such large materials datasets. In this work, we propose a novel framework for integrating generative models within an active learning loop. Further, this enables the training of generative models with datasets significantly smaller than what has previously been demonstrated, providing a direct route for their application in data constrained environments. The functionality of this framework is then demonstrated by addressing the challenge of designing polycrystalline textures associated with target anisotropic mechanical properties. The developed protocol exhibited a cost reduction between 14 to 18 times over a randomly sampled experimental design.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Gaussian process autoregressive model capturing microstructure evolution paths in a Ni–Mo–Nb alloy

Additive manufacturing is increasingly being employed to produce components of complex geometries in structural alloys because of the expected energy savings associated with the near-net-shape capability and the ability to build in novel internal features that are not possible with many conventional manufacturing approaches. However, because of the extreme thermal conditions encountered, the non-equilibrium microstructures produced during powder bed-based additive manufacturing processes must be subjected to custom post-heat treatment processes to recover the target mechanical properties. Phase-field models and simulation techniques have matured to a state where the microstructure evolution paths, and the morphologies of the resulting precipitate phases can be predicted reasonably accurately, considering alloy-specific thermodynamic and kinetic aspects of the nucleation and growth processes. However, phase-field simulations are computationally intensive, which precludes the ability to apply the simulations directly to the length scale of the entire component. Therefore, it is highly desirable to develop low-computational-cost surrogate models that effectively capture the physics at the microstructural length scale, while facilitating the design of optimized processing conditions resulting in location-specific targeted microstructures at the component scale. The work presented here demonstrates the application of the materials knowledge system framework to develop a surrogate model that effectively captures the microstructural path during annealing of a Ni–Mo–Nb alloy containing different Mo and Nb compositions known to segregate during solidification under additive manufacturing conditions. Specifically, the surrogate model built in this work is based on a Gaussian process autoregressive model informed by statistical representation of simulated microstructures using two-point correlations and dimensionality reduction through principal component analysis. In conclusion, this surrogate model is shown to capture the bifurcation of the microstructural path during precipitation, which yields a microstructure dominated by the $\gamma^{\prime\prime}$ phase at high Nb concentrations and the $\delta$ phase at low Nb concentrations.

36 MATERIALS SCIENCE↗
DOE OSTI2023

High throughput structure–property relationship for additively manufactured 316L/IN625 alloy mixtures leveraging 2-step Bayesian estimation

While the fabrication of graded materials by directed energy deposition (DED) has led to accelerated materials discovery, the ability to rapidly explore sufficiently large material composition spaces is limited due to the time-intensive nature of conventional materials characterization techniques. The present study investigates the viability of small punch test (SPT) protocols for rapidly evaluating DED-fabricated alloy mixtures of stainless steel 316L (316L) and Inconel 625 (IN625). The SPT protocols evaluated in this study include both the recently established two-step Bayesian estimation framework as well as the empirical relationships established in prior literature. It is shown that these protocols are capable of reliably and quantitatively tracking the changes in the mechanical properties of the alloy mixtures studied. Enhancement of mechanical properties was observed with the addition of IN625 to 316L, which is attributed to the austenite stabilization in the matrix and the formation of fine δ - Ni3Nb precipitates. It is shown that CALPHAD-based Scheil model simulations predicted the formation of different precipitate phases for each composition. The novel protocols presented in this paper open new avenues for high throughput material explorations for additive manufacturing.

2-step Bayesian estimation↗
DOE OSTI2022

An approach towards plastic scintillators from thermally activated delayed fluorescent dyes and cross-linkable bismuth compounds

Cross-linked polyvinyltoluene (PVT) based plastic scintillators loaded with dyes exhibiting thermally activated delayed fluorescence (TADF) and various amounts of (caproate)di-(methacrylate) bismuth (CMB) were developed. The co-polymerization of vinyl toluene monomer loaded with different weight ratios of CMB and 1 wt% of TADF dye yielded highly transparent cross-linked plastic scintillators with intense green photoluminescence. The thermal and mechanical properties of the scintillators were investigated using thermogravimetric analysis (TGA), differential scanning calorimetry (DSC) and micro indentation techniques. Here, their performance for radiation detection was assessed by performing gamma spectroscopy using a Cs-137 source and benchmarked to commercial plastic scintillators. The loading of CMB was guided by Monte Carlo N-Particle (MCNP) simulations. The results of this study show that the cross-linking approach and the use of TADF dyes yield scintillators with good mechanical properties with a uniaxial yield strength up to ~66 MPa for compositions loaded with 40 wt% of CMB and high optical quality. However, the light yield in gamma spectroscopy experiments is found to be reduced with the addition of CMB, suggesting a trade-off between mechanical yield strength and light yield in gamma spectroscopy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Digital Twins for Materials

Digital twins are emerging as powerful tools for supporting innovation as well as optimizing the in-service performance of a broad range of complex physical machines, devices, and components. A digital twin is generally designed to provide accurate in-silico representation of the form (i.e., appearance) and the functional response of a specified (unique) physical twin. This paper offers a new perspective on how the emerging concept of digital twins could be applied to accelerate materials innovation efforts. Specifically, it is argued that the material itself can be considered as a highly complex multiscale physical system whose form (i.e., details of the material structure over a hierarchy of material length) and function (i.e., response to external stimuli typically characterized through suitably defined material properties) can be captured suitably in a digital twin. Accordingly, the digital twin can represent the evolution of structure, process, and performance of the material over time, with regard to both process history and in-service environment. This paper establishes the foundational concepts and frameworks needed to formulate and continuously update both the form and function of the digital twin of a selected material physical twin. The form of the proposed material digital twin can be captured effectively using the broadly applicable framework of n-point spatial correlations, while its function at the different length scales can be captured using homogenization and localization process-structure-property surrogate models calibrated to collections of available experimental and physics-based simulation data.

36 MATERIALS SCIENCE↗
DOE OSTI2021

In Situ Transmission Electron Microscopy: Signal processing challenges and examples

Transmission electron microscopy (TEM) is a powerful tool for imaging material structure and characterizing material chemistry. Recent advances in data collection technology for TEM have enabled high-volume and high-resolution data collection at a microsecond frame rate. Here, taking advantage of these advances in data collection rates requires the development and application of data processing tools, including image analysis, feature extraction, and streaming data processing techniques. In this article, we highlight a few areas in materials science that have benefited from combining signal processing and statistical analysis with data collection capabilities in TEM and present a future outlook on opportunities of integrating signal processing with automated TEM data analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Gaussian-Process-Driven Adaptive Sampling for Reduced-Order Modeling of Texture Effects in Polycrystalline Alpha-Ti

Data-driven tools for finding structure–property (S–P) relations, such as the Materials Knowledge System (MKS) framework, can accelerate materials design, once the costly and technical calibration process has been completed. In this paper, a three-model method is proposed to reduce the expense of S–P relation model calibration: (1) direct simulations are performed as per (2) a Gaussian process-based data collection model, to calibrate (3) an MKS homogenization model in an application to α-Ti. The new methods are compared favorably with expert texture selection on the performance of the so-calibrated MKS models. Benefits for the development of new and improved materials are discussed.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

A Comparative Study of the Efficacy of Local/Global and Parametric/Nonparametric Machine Learning Methods for Establishing Structure–Property Linkages in High-Contrast 3D Elastic Composites

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.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Phonon transport properties of two-dimensional electride Ca 2 N—A first-principles study

We investigate phonon transport in dicalcium nitride (Ca 2 N), an electride with two-dimensional confined electron layers, using first-principles density functional theory and the phonon Boltzmann transport equation. The in-plane (κ[100]) and out-of-plane (κ[001]) lattice thermal conductivities at 300 K are found to be 11.72 W m -1 K -1 and 2.50 W m -1 K -1 , respectively. Spectral analysis of lattice thermal conductivity shows that ~85% of $κ_{[100]}$ and $κ_{[001]}$ is accumulated by phonons with frequencies less than 5.5 THz and 2.5 THz, respectively. Modal decomposition of lattice thermal conductivity further reveals that the optical phonons contribute to ~68% and ~55% of overall $κ_{[100]}$ and $κ_{[001]}$, respectively. Phonon dispersion suggests that the large optical phonon contribution is a result of low frequency optical phonons with high group velocities and the lack of phonon bandgap between the acoustic and optical phonon branches. We find that the optical phonons with frequencies below ~5.5 THz have similar three-phonon phase space and scattering rates as acoustic phonons. Comparison of the contributions from emission and absorption processes reveals that the three-phonon phase space and scattering rates of phonons—optical or acoustic—with frequencies below 5.5 THz are largely dominated by absorption processes. We conclude that the large contribution to lattice thermal conductivity by optical phonons is due to the presence of multiple low frequency optical phonon modes with high group velocities and similar phase space and scattering rates as the acoustic phonons. This study provides the frequency and temperature dependent lattice thermal conductivity and insights into phonon transport in Ca 2 N, both of which have important implications for the development of Ca 2 N based devices.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Reduced-order microstructure-sensitive protocols to rank-order the transition fatigue resistance of polycrystalline microstructures

We present that the transition fatigue regime between low cycle fatigue (LCF) and high cycle fatigue (HCF) is often addressed in the design and performance evaluation of load-bearing components used in many structural applications. Transition fatigue is characterized by elevated levels of local inelastic deformation in significant regions of the microstructure as compared to HCF. Typically, crystal plasticity finite element method (CPFEM) simulations are performed to model this phenomenon and to rank-order microstructures by their resistance to crack formation and early growth in the regime of transition fatigue. Unfortunately, these approaches require significant computational resources, inhibiting their use to explore novel materials for transition fatigue resistance. Reduced-order, microstructure-sensitive models are needed to accelerate the search for next-generation, fatigue-resistant materials. In a recent study, Paulson et al. (2018) extended the materials knowledge system (MKS) framework for rank-ordering the HCF resistance of polycrystalline microstructures. The efficacy of this approach lies in the reduced-dimensional representation of microstructures through 2-point spatial correlations and principal component analysis (PCA), in addition to the characterization of the HCF response with a small set of performance measures. In this work, these same protocols are critically evaluated for their applicability to rank-order the transition fatigue resistance of the same class of polycrystalline microstructures subjected to increased strain amplitudes. Success in this endeavor requires the formation of homogenization linkages that account for the significantly higher levels of local inelastic deformation and stress redistribution in transition fatigue as compared to HCF. Finally, a set of 12 α-titanium microstructures generated using the open access DREAM.3D software (Groeber and Jackson, 2014) are employed for this evaluation.

2-point correlations↗
DOE OSTI2018

Data-driven reduced-order models for rank-ordering the high cycle fatigue performance of polycrystalline microstructures

Computationally efficient estimation of the fatigue response of polycrystalline materials is critical for the development of next generation materials in application domains such as transportation, health, security, and energy industries. This is non-trivial for fatigue of polycrystalline metals since the initiation and growth of fatigue cracks depends strongly on attributes of the microstructure, such as the sizes, shapes, orientations, and neighbors of individual grains. Furthermore, regions of microstructure most likely to initiate cracks correspond to the tails of the distributions of the microstructure features. This requires the execution of large numbers of experiments or simulations to capture the response of the material in a statistically meaningful manner. In this work, a linkage is described to connect polycrystalline microstructures to the statistically signified driving forces controlling the high cycle fatigue (HCF) responses. This is achieved through protocols that quantify these microstructures using 2-pt spatial correlations and represent them in a reduced-dimensional space using principal component analysis. Reduced-order relationships are then constructed to link microstructures to performance characteristics related to their HCF responses. As a result, these protocols are demonstrated for α-titanium, which exhibits heterogeneous microstructure features along with significant elastic and inelastic anisotropies at both the microscale and the macroscale.

2-point correlations↗
DOE OSTI2017

Quantifying the mechanical effects of He, W and He + W ion irradiation on tungsten with spherical nanoindentation

Here, recent advances in spherical nanoindentation protocols have proven very useful for capturing the grain-scale mechanical response of different metals. This is achieved by converting the load–displacement response into an effective indentation stress–strain response which reveals latent information such as the elastic–plastic transition or indentation yield strength and work-hardening behavior and subsequently correlating the response with the material structure (e.g., crystal orientation) at the indentation site. Using these protocols, we systematically study and quantify the microscale mechanical effects of He, W, and He + W ion irradiation on commercially pure, polycrystalline tungsten. The indentation stress–strain response is correlated with the crystal orientation from electron backscatter diffraction, the defect structure from transmission electron microscopy micrographs, and the stopping range of ions in matter calculations of displacement damage and He concentration. He-implanted grains show a much higher indentation yield strength and saturation stress compared to W-ion-irradiated grains for the same displacement damage. There is also good agreement between the dispersed barrier hardening model with a barrier strength of 0.5–0.8 and void models (Bacon–Kochs–Scattergood and Osetsky–Bacon models) with the experimentally observed changes in indentation strength due to the presence of He bubbles. This finding indicates that a high density (~ 9 × 10 23 m –3 ) and concentration (~ 1.5 at.%) of small (~ 1 nm diameter) He bubbles can be moderate to strong barriers to dislocation slip in tungsten.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Probing nanoscale damage gradients in ion-irradiated metals using spherical nanoindentation

We discuss and demonstrate the application of recently developed spherical nanoindentation stress-strain protocols in characterizing the mechanical behavior of tungsten polycrystalline samples with ion-irradiated surfaces. It is demonstrated that a simple variation of the indenter size (radius) can provide valuable insights into heterogeneous characteristics of the radiation-induced-damage zone. We have also studied the effect of irradiation for the different grain orientations in the same sample.

Science & Technology - Other Topics↗
DOE OSTI2017

Process-Structure Linkages Using a Data Science Approach: Application to Simulated Additive Manufacturing Data

A novel data science workflow is developed and demonstrated to extract process-structure linkages (i.e., reduced-order model) for microstructure evolution problems when the final microstructure depends on (simulation or experimental) processing parameters. Our workflow consists of four main steps: data pre-processing, microstructure quantification, dimensionality reduction, and extraction/validation of process-structure linkages. These methods that can be employed within each step vary based on the type and amount of available data. In this paper, this data-driven workflow is applied to a set of synthetic additive manufacturing microstructures obtained using the Potts-kinetic Monte Carlo (kMC) approach. Additive manufacturing techniques inherently produce complex microstructures that can vary significantly with processing conditions. Using the developed workflow, a low-dimensional data-driven model was established to correlate process parameters with the predicted final microstructure. In addition, the modular workflows developed and presented in this work facilitate easy dissemination and curation by the broader community.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Structure-processing correlations and mechanical properties in freeze-cast Ti-6Al-4V with highly aligned porosity and a lightweight Ti-6Al-4V-PMMA composite with excellent energy absorption capability

In contrast to freeze-cast ceramics and polymers, few freeze-cast metals have been described, to date. This systematic study on structure-processing correlations in freeze-cast Ti-6Al-4V scaffolds reports how processing parameters determine the architecture formed during the directional solidification of water-based metal slurries and after sintering. Additionally, sedimentation in the slurry during freezing and volume shrinkage during burnout and sintering were found to significantly affect both structure and properties of the scaffolds. In using two freezing rates, 1 and 10 °C min -1 , two water-based polymer solutions as binders (chitosan and carboxymethyl cellulose) and two different metal volume fractions in the slurry, 20 and 30 vol%, Ti-6Al-4V scaffolds could be prepared with pore length, width, and porosity ranging from 41 to 523 μm, 14.5–76.5 μm, and 65 to 34%, respectively. Their compressive strength, stiffness, and toughness (work to 20% strain) fall in the range of 83–412 MPa, 7–29 GPa, and 14–122 MJ m -3 , respectively. In order to improve the properties a select composition was infiltrated with poly(methyl methacrylate). This increased the average yield strength by a factor of 2.3 from 83 to 193 MPa and the average toughness (work to 50% strain) by a factor of 2.7 from 28.1 to 76.8 MJ m -3 .

36 MATERIALS SCIENCE↗
DOE OSTI2016

Mechanical characterization of Ti-6Al-4V titanium alloy at multiple length scales using spherical indentation stress-strain measurements

Recent advances in spherical indentation stress-strain protocols and analyses have demonstrated the capability for measuring reliably the local mechanical responses in polycrystalline metal samples at different length scales, ranging from sub-micron (regions within individual grains) to several hundreds of microns (regions covering several grains). These recent advances have now made it possible to study systematically the mechanical behavior of a single material system at different length scales, with tremendous potential to obtain new insights into the role of individual phases, interfaces, and other microscale constituents on the macroscale mechanical response of the material. In this paper, we report spherical indentation stress-strain measurements with different indenter sizes (microns to millimeters) on Ti-6Al-4V (Ti-64) which capture the mechanical response of single phase alpha-Ti-64, single colony (alpha-beta), few colonies, and many colonies of Ti-64. The results show that the average mechanical response (indentation modulus and yield strength) from multiple indentations remains relatively unchanged from single phase alpha to many colonies of Ti-64, while the variance in the response decreases with indenter size. In conclusion, the work-hardening response in indentation tests follows a similar behavior up to indentation zones of many colonies, which shows significantly higher work hardening rates.

36 MATERIALS SCIENCE↗