ICME 2023: Poster Session
Program Organizers: Charles Ward, AFRL/RXM; Heather Murdoch, U.S. Army Research Laboratory
Tuesday 4:45 PM
May 23, 2023
Room: Caribbean V
Location: Caribe Royale
A Framework for Multilevel Robust Co-design of Material and Product Systems: Mathew Baby1; Anand Balu Nellippallil1; 1Florida Institute of Technology
The design of products with targeted performance requires expert decision-making across the Processing-microStructure-Property-Performance levels of the materials design hierarchy. These multilevel decisions need to account for the uncertainties arising from incomplete and inaccurate information during design. Isolated decision-making across levels will not suffice in realizing targeted product performance, as it can result in design conflicts that adversely impact product performance. Hence, the interactions of multilevel decisions need to be considered. We recognize ‘co-design’, which involves collaborative decision-making across multilevel by considering their interactions, and ‘robustness’, as central to managing design conflicts and uncertainties.In this paper, we present a framework to support the multilevel, robust co-design of material and product systems. The framework facilitates systematic modeling of multilevel decisions and interactions, uncertainty management, and conflict detection and management. We illustrate the efficacy of the proposed framework using the Hot-rod rolling problem characterized by uncertain, multilevel decision-making across the Processing-microStructure-Property-Performance levels.
Ab-initio Modelling of Phonon Transport in 2D High Entropy MXene Layers: Prince Sharma1; Ganesh Balasubramanian1; 1Lehigh University
The concept of high entropy (HE) via the virtue of configurational disorder is exploited in all the classes of materials ranging from alloys, ceramics and metallic glasses. In this work we focus on 2D High Entropy MXene. MXenes are a promising class of 2D materials owing to high electronic conductivity and ease of cation intercalation making it a candidate for supercapacitors, battery material and sensors. However, there is absence of experimental or theoretical studies to evaluate thermal characteristics of these materials. In this work we study thermal properties of Ti2AlC (MAX phase), Ti2C (MXene) and (Cr0.5Nb0.5Ta0.5Ti0.5)C (HE-MXene) via first principal phonon calculation and solution of Boltzmann transport equations. The reduction in thermal conductivity is attributed to lower phonon life time and higher disorder in structure.
Decision Support System for Device Fabrication: Neelanshi Wadhwa1; Sapan Shah1; Deepak Jain1; Sreedhar Reddy1; Beena Rai1; 1Tata Consultancy Services
Devices like solar cells, batteries, LEDs etc. are now an integral part of our lives. Technological advancements in these devices are in vogue, either to increase their performance or to find new applications for them. These advancements often trickle down to the search for better materials or efficient processes. The space of potential materials, operations, operating conditions is vast, and selecting the right combination thereof to achieve the desired characteristics is a knowledge intensive activity. A large amount of such device fabrication knowledge is available in the form of publications, patents, company reports. We present a decision support system built on top of systematically extracted knowledge from materials science literature. The extracted knowledge is represented as knowledge graphs conforming to an ontology that can be queried to make informed decisions during device fabrication. We applied our system to the emerging class of devices- perovskite solar cells and achieved good results.
Deformation Behavior in Core-Shell Heterostructured Materials: Hyoung Seop Kim1; 1Pohang University of Science and Technology
Many studies have extensively investigated various microstructural features to understand heterogeneous microstructure effects with mechanical responses. In this study, a representative volume element modeling approach was used to control specific microstructure features accurately beyond the experimental difficulties. In particular, the deformation behavior of the core-shell structure was clearly visualized using the finite element method with the proposed dislocation-based constitutive model considering the evolution of geometrically necessary dislocations. From these findings, we suggest that the optimal heterogeneous microstructure should be designed based on the deformation behavior with respect to the geometry, size, and shape of soft and hard domains.
Development of a Fully Anisotropic Monte Carlo Potts Model to Study Grain Growth: Lin Yang1; Vishal Yadav1; Michael Tonks1; 1University of Florida
We have developed a Monte Carlo Potts model for anisotropic grain growth in SPPARKS to study the impact of misorientation and inclination on the grain boundary (GB). The GB inclination is determined using a smoothing method. We compare the grain growth behavior for three different GB energy functions: 1) energy is only a function of misorientation; 2) energy is only a function of inclination; 3) energy is fully dependent on five degrees of freedom, three from misorientation, two from inclination. In addition, we also compare the growth for different initial grain structures: 2D bicrystal with a circular grain, 2D tricrystal with a triple junction, 2D polycrystal, 3D bicrystal with a spherical grain, 3D tetracrystal with a quadruple junction, and 3D polycrystal.
Micromechanical Modeling of Cyclic Damage in Metallic Materials: Gururaj Gopal Rao1; Leslie T Mushongera1; 1University of Nevada, Reno
Fatigue is amongst the major physical processes associated with crystalline materials that lead to premature failure. Understanding how fatigue cracks initiate in crystalline materials is very challenging because the microstructural features around these cracks evolve continuously with cyclic loading. A phase-field model for heterogeneous microstructures is developed to study the evolution of phase, stress, and plastic strains in metallic material systems under cyclic loads. A small strain plasticity model based on the principles of continuum mechanics is integrated into the phase field model. Local momentum balance is solved on a staggered grid using the finite difference method to compute displacement fields. For calculations of plastic strains, a Prandtl-Reuss-type model consisting of an associated flow rule in combination with the von Mises yield criterion and a linear isotropic hardening approximation is implemented. Evolution of the stresses, plastic strains are obtained by establishing the consistency condition using a two-step return mapping algorithm.
Irradiance Simulation of Real World Field for PV Backsheets Degradation: Zelin Li1; Raymond Wieser1; Xuanji Yu1; Laura Bruckman1; 1Case Western Reserve University
Assessing the durability of photovoltaic (PV) module backsheet is critical to increasing module lifetime. Laboratory-based accelerated testing is inefficient in assessing large-scale failures of commercial polymeric materials. Additionally, there is growing concern that standard condition tests do not reflect non-uniformities in field exposure, and that certain modules experience more severe degradation due to their location. Anisotropy in field exposures is installation-dependent and reflects different levels of exposure to irradiance due to mounting geometry, ground surface albedo, and climatic zone. Bificial_Radiance, describes the amount of reflected irradiance on the backsheet using measured racking parameters. In our work, site specific weather data are gathered for the entire length of exposure for the modules. We then determined the total full-spectrum dose for the incident irradiance on the backsheet. The simulation results are integrated with historical field survey data to better understand outdoor exposures and allow us to better correlate with indoor accelerated tests.
Fluoroelastomer Crystallization Kinetics
Studied by Deep Learning Segmentation of Atomic Force Microscopy Images: Sameera Nalin Venkat1; Thomas Ciardi1; Jube Augustino1; Jayvic Jimenez1; Peter Schlueter1; Mingjian Lu1; Frank Ernst1; Yinghui Wu1; Roger French1; Laura Bruckman1; 1Case Western Reserve University
Atomic force microscopy (AFM) provides valuable insights into the crystallization of fluoroelastomers, which impacts performance in applications. Time-lapsed acquisition of AFM image sequences provides quantitative information on crystallization kinetics. However, corresponding datasets are large. Open-source software and tools are routinely used for data processing, but it can be time-consuming and challenging to process thousands of images. In this work, we integrate automated feature detection and segmentation algorithms, leveraging evaluation by convolutional neural networks. End-to-end frameworks, such as UNet image segmentation, allows for batch processing such as generating binarized masks which can be used to obtain image properties. This can help in quantifying the projected area fraction of the crystalline phase in each image. It can also track individual crystallites as a function of time when combined with an open-source software for AFM-image processing, which serves as the “ground truth” for comparison.
Geospatiotemporal Modeling of Near Subsurface Temperatures of the Continental United States for Assessment of Materials Degradation: Deepa Bhuvanagiri1; Hope Omodolor1; Erika Barcelos1; Vibha Mandayam1; Sameera Nalin Venkat1; R. Mohan Srivastava1; Roger French1; Jeffrey Yarus1; 1Case Western Reserve University
In this study, we assess the variation in temperature at selected subsurface depths between the surface of the earth extending to approximately 300m. It is a common rule of thumb that a 10 degree Celsius change in temperature doubles the degradation and failure rates of systems deployed. Thus, understanding temperature distribution both laterally and vertically in the subsurface is an important factor in understanding substructure materials degradation. We produced maps depicting subsurface temperature variation at selected shallow depths across the continental United States; respectively, 100m, 200m, and 300m using (ordinary) kriging. Additionally, Nevada, Michigan, and Ohio were selected for more detailed temperature mapping evaluation including a comparison of three different geostatistical methods; Ordinary Kriging, Fixed Rank Kriging, and Conditional Simulation. Results from all methods employed and at all depths analyzed confirmed heterogeneous temperature patterns across the United States and within the individual states.
Discriminative Object Tracking by Domain Contrast: Huayue Cai1; Xiang Zhang1; Long Lan1; Changcheng Xiao1; Chuanfu Xu1; Jie Liu1; Zhigang Luo1; 1National University of Defense Technology
Multi-domain tracking method improves object tracking by sharing domain information whilst learning private information. In that context, each video sequence as a specific domain serves for a domain-specific layer. We observe an finding that target features from different domains are highly confused with each other. To this end, we propose a domain interaction training paradigm called domain contrast to boost discriminative features by effectively using amounts of instances from all the domains in two novel aspects: 1) a memory-saving training algorithm is proposed to solve the "out-of-the-memory" problem, and 2) a composite class-balanced loss is explored to tackle a imbalanced problem, which not only involves the usual class imbalance problem but also accounts for the case of the totally mere negative instances. Experiments on multiple tracking benchmarks show that our mechanism consistently achieves the tracking performance gain of both base multi-domain tracker and its real-time variant.
Effects of Surface Segregations in Catalytic AgAuCuPdPt High Entropy Alloy: Chinmay Dahale1; Soumyadipta Maiti1; Sriram Srinivasan1; Beena Rai1; 1TCS Research, TRDDC
High-entropy alloys are emerging as a novel class of catalysts for chemical conversions like electrolysis for hydrogen production, CO2 electrochemical reduction, fuel cells etc. AuAgCuPdPt equimolar HEA was recently shown to be an active catalyst for CO2 reduction. In this work, we have used EAM molecular dynamics potential based Hybrid Monte Carlo/Molecular Dynamics (MC/MD) simulations to study surface segregation in AuAgCuPdPt FCC HEA. Simulations were carried out for spherical nanoparticles, cubical nanoparticles, and slabs of various crystallographic surface orientations to obtain detailed structural and concentration profiles normal to the surfaces. In all cases, Ag atoms were found to preferentially segregate to the surface while the subsurface layer mainly consisted of Au atoms. Hardly any Pt atoms were found on the surface layers. Detailed neighborhood analysis of surface sites revealed that the percentage of chemically unique sites were larger for elements with lower concentration at the surface.
Enhancement of Grain Refinement and Heat Resistance in Tib2-Reinforced Tial Matrix Composite Powder Manufactured by Spark Plasma Sintering: Ayodeji Afolabi1; Peter Olubambi1; 1University of Johannesburg
The microstructure and tensile deformation of a number of sintered TiB2-reinforced near-titanium aluminide matrix composites have been studied. When TiB2 was added to the microstructures via spark plasma sintering processing at values of 0.5 and 3.0 vol pct, it was contrasted with unreinforced TiAl. For every composition and reinforcement variety, the effects of temperature and time have been examined. Within the studied composites, a range of TiB2 sizes was measured. TiB2's elastic moduli, tensile strengths at ambient temperature, and overall strain-hardening response all rise as the volume fraction of TiB2 increases. According to the findings, both indirect and direct sources contribute to the strengthening and flow behaviour of these composite materials. Strengthening contributions are indirectly derived from the microstructural changes within the matrix of the composite.
First-principles and Data-driven Discovery of High-entropy Alloys for Corrosion Protection: Andrew Neils1; Nathan Post1; Cheng Zeng1; Jack Lesko1; 1The Roux Institute at Northeastern University
Corrosion has a wide impact on society, causing catastrophically damage to structural engineered components. High-entropy alloys are emerging materials for superior corrosion performance. However, experimental search for corrosion-resistant materials is time consuming and expensive. Machine learning models trained on first-principles data holds the promise in acceleration of materials design and discovery by predicting materials properties at a low computational cost. In this work, we use first-principles calculations to identify thermodynamic and kinetic metrics for corrosion behaviors of metals. Based on those metrics, we then employ a data-driven approach to guide the autonomous discovery of high-entropy alloys for corrosion protection. Limitations and improvements of the proposed methods will be discussed.
Microstructure-based Modelling Approach to Determine Hydrogen Diffusion and Trapping in Steels: Maribel Arribas1; Ana Rosa Carrillo1; Ane Jimenez1; Jean Baptiste Jorcin1; Pello Uranga2; Nerea Isasti3; 1Tecnalia Research & Innovation; 2CEIT and TECNUN (University of Navarra); 3CEIT-BRTA and University of Navarra-Tecnun
In this work, a hydrogen diffusion and trapping model has been coupled with electrochemical permeation measurements to characterize de diffusion and trapping parameters associated with a pipeline steel. The obtention of hydrogen trapping parameters is not straightforward, especially for complicated microstructures with multiple trapping sites, and generally the existing models consider a high number of fitting parameters. In the present model, the number of fitting parameters has been reduced by defining new microstructure-based functions which give a physical meaning to parameters which have been typically treated as fitting parameters. As a result, a better estimation of the hydrogen diffusion and trapping parameters is obtained, providing an improved modelling framework for the definition of the appropriate microstructure features that result in a better performance of the pipeline steels in contact with hydrogen.
Phase Field Simulation of Heat Treatment Process for Single Crystal Ni-based Superalloy: Yeyuan Hu1; Qingyan Xu1; 1Tsinghua University
The heat treatment process is a necessary part after solidification in single crystal superalloy. The microstructure after heat treatment affects the service performance significantly. However, heat treatment experiments are time-consuming and costly. To investigate the microstructure evolution in Ni-based superalloys during heat treatment, a multicomponent multiphase-field model is established for the heat treatment process in Ni-based single crystal superalloys. The Thermo-Calc database is used to calculate kinetic and thermodynamic parameters in multicomponent superalloy. The microstructure evolution will be discussed according to the simulation results and the effect of cooling rate and heat treatment time will also be explored. Finally, the design of heat treatment process will be considered.
Development of an ML Interatomic Potential for SiC for Extreme Environments: Michael MacIsaac1; 1IIT Kanpur
Interatomic potentials are surrogate models that govern the physics of atomistic simulations and provide immense computational savings compared to ab initio molecular dynamics (MD). However, these reduced computational requirements may come with noticeable reductions in accuracy over ab-initio methods. Recently, there have been large advances in machine learning techniques, which have steadily been adopted by the material science community, ushering in the data-driven field of material informatics. Our goal is to develop a machine-learned interatomic potential (MLIP) for the silicon carbide (SiC) system, intended for simulating extreme environments (e.g., shock loading). We use an artificial neural network trained on ab initio calculations to fit an MLIP for the SiC system. The training data includes multiple polytypes as we aim to capture phase transformations observed under extreme loading conditions approaching ab initio accuracy with lower computational costs compared to current potentials. While MLIPs may not be as physically interpretable as their traditional counterparts, we expect that their flexible form will enable the identification of connections across length scales that are otherwise missed in more traditional approaches. Efforts for SiC MLIP development will be discussed, along with network architecture optimization.
Ontology-based Digital Representations of Materials Testing in the MaterialDigital Initiative: Hossein Beygi Nasrabadi1; Thomas Hanke2; Miriam Eisenbart3; Matthias Weber2; Roy Meissner4; Gordian Dziwis4; Yue Chen1; Birgit Skrotzki1; 1Bundesanstalt für Materialforschung und -prüfung (BAM); 2Fraunhofer-Institut für Werkstoffmechanik (IWM); 3Forschungsinstitut Edelmetalle + Metallchemie (fem); 4Institut für Angewandte Informatik (InfAI)
The MaterialDigital (PMD) platform has been funded by the German Federal Ministry of Education and Research (BMBF) by 2019. The platform aims to digitalize materials and processes including the provision of infrastructures to represent complete material lifecycles, considering the FAIR principles (discoverable, accessible, interoperable, reusable). The PMD-funded KupferDigital project is developing a data ecosystem for digital materials research. The fundament of the data ecosystem is ontology-based digital representations of copper materials. In the current research, we present the methodology and toolchains for the development of domain-level ontologies for materials testing that address the requirements of materials testing standards. The collection of the required terminology from the testing standard, the semantic representation of the process graphs, the conversion of the ontology files, their integration with the upper-level ontologies, and the data mapping processes were presented for the Brinell hardness testing use case. The data integration process was successfully validated by the SPARQL query of the mapped datasets.
Data-oriented Description of Microstructure-dependent Plastic Material Behavior: Jan Schmidt1; Alexander Hartmaier; 1Ruhr-University Bochum
Constitutive modelling of anisotropic plastic material behavior traditionally follows a deductive scheme, relying on empirical observations that are cast into analytic equations, the so-called phenomenological yield functions. Recently, data-driven constitutive modeling has emerged as an alternative to phenomenological models as it offers a more general way to describe the material behavior with no or fewer assumptions. In data-driven constitutive modeling, methods of statistical learning are applied to infer the yield function directly from a data set generated by experiments or numerical simulations. We present a new generic descriptor for crystallographic texture that allows an explicit consideration of the microstructure in data-driven constitutive modeling. We prove its ability to capture the structure-property relationship between a variety of textures and their anisotropic plastic behavior described with the yield function Yld2004-18p by applying methods of supervised machine learning. In the context of data-driven constitutive modeling,the descriptor enables consideration of microstructure evolution.
Irradiation Effects in MARMOT: Enhancing UO2 Grain Growth Modeling and Validation: Md Ali Muntaha1; Larry Aagesen2; Michael Tonks3; 1voestalpine Böhler Aerospace GmbH & Co KG; 2Idaho National Laboratory; 3University of Florida
MARMOT simulations extensively investigate grain boundary migration and grain growth in UO2. MARMOT, designed to model irradiated fuel and cladding at the mesoscale, currently only accounts for grain growth kinetics in fresh and unirradiated fuel in its UO2 grain growth model. This omission might significantly affect the accuracy of the model's predictions. In this research, we seek to expand MARMOT's simulation capabilities by including the impact of irradiation on UO2 grain growth. We have added these effects to MARMOT by considering thermal spikes and coupling them with a heat conduction simulation that features a random heat source. To validate our model, we will compare its predictions to in-situ TEM experimental results obtained under ion-irradiation conditions using nanocrystalline UO2 thin films. Once validated, we can use the refined model to assess the importance of this previously neglected mechanism in grain growth forLWR fuel pellets.
On the Origin of Dendrite Misorientation in Ni-based Single Crystal Superalloy: Huxiang Xia1; Qingyan Xu1; 1Tsinghua University
The dendrite misorientation is a typical defect in single crystal Ni-based superalloy castings. To date, there is still no explicit viewpoint on its generation mechanism. In order to trace the growth of dendrites, 30 layers of continuous slices were cut from a single crystal casting. The result shows that the growth direction of dendrite will continuously change during the solidification. A multicomponent phase-field simulation model was established to simulate the influence of solute convection on dendrite misorientation. The simulation result shows that, due to the existence of solute convection, the unstable distribution of solute can lead to the inhomogeneous driving force distribution at the tip of the dendrite, resulting in the orientation misorientation defect.
Physics-constrained, Inverse Design of High-temperature Strength Printable Aluminum Alloys with Low Cost and CO2 Emissions for High Demand Industries: Ben Glaser1; S. Mohadeseh Taheri-Mousavi2; 1Florida Institute of Technology; 2Carnegie Mellon University
Printable high-temperature strength aluminum alloys are long standing goals in the structural material community for aerospace applications and can be leveraged for high-demand industries like automotive provided that their cost and sustainability metrics match the requirements of large-scale production. These metrics add additional constraints besides printability and typical mechanical performance for these alloys. We recently designed a record high-0temperature strength printable Al alloy from the Al-Ni-Er-Zr-Y-Yb system and experimentally validated its performance. To adapt this design for high-demand industries, on data from CALPHAD-based ICME calculations we applied various unsupervised learning techniques and Bayesian optimization to efficiently explore the space and optimize the complex objective functions We developed a design that enables cost reductions of 30-50 percent compared to the record alloy while maintaining 95 percent strength performance.
Predicting the Performance Degradation of Advanced Devices Exposed to Ionizing Radiation: Xiaoyu Guan1; Michael Tonks1; 1University of Florida
It is crucial to ensure the effectiveness and efficiency of semiconductor devices when they work in harsh environments. In this work we are developing a new novel simulation tool that predicts the impact of ionizing radiation on the performance of a semiconductor device. The approach will couple TCAD capability to predict device performance with detailed radiation models to predict charge carrier and lattice defect production. The tool is implemented using the open-source Multiphysics Object-Oriented Simulation Environment (MOOSE). We determine the performance of the devices by calculating local carrier concentration and local current density vs. DS voltage dynamically over time. The results show the relationship of performance degradation rate and irradiation. The final tool will be open source, have robust quality assurance practices, and be able to be referenced in designing more radiation hard devices.
The Effects of Orientation and Temperature on Deformation Mechanisms in Single-crystalline CrCoNi: Charles Matlock1; Ning Zhang1; 1Baylor University
In this work, we examine the effects of crystallographic orientation and temperature on the mechanical performance of a single crystalline face-centered cubic (FCC) CoCrNi MEA. Uniaxial tensile loadings were applied on the MEA plates oriented along the [100], [110], [111], and [112] directions at 77 K and 298 K. Our simulation results reveal a strong orientation and temperature effect on the stiffness, strength, and ductility of the MEA. The [111]-oriented plate exhibited the highest elastic modulus, yield stress, and modulus of toughness, demonstrating a strong strain-hardening response; while the [100]-oriented plate produced the lowest corresponding values among the tested cases. At 77K, deformation twinning was the dominant deformation mechanism in the [100] and [110]-oriented MEA plates, and for the [111] and [112]-oriented MEA plates, dislocation slip was dominant. At 298K, dislocation slip was the dominant deformation mechanism in all orientations.
A Physics-based Correlation Study of Hot Cracking Phenomenon in the Processes of Additive Manufacturing: Guannan Tang1; Anthony Rollett1; 1Carnegie Mellon University
The occurrence of hot cracking in the additive manufacturing process involves a variety of factors from different aspects. This leaves the effort trying to quantify hot cracking phenomena often lacking generality. Thus, unifying models that take account of processing parameters, thermodynamics, and mechanical properties remain a big gap in modeling the hot cracking phenomenon. Our current study intends to evaluate variables in different aspects but related to the hot cracking phenomenon. The topmost relevant variables will be identified and correlated with the occurrence of hot cracking through machine learning algorithms. To this end, synchrotron-based high-speed techniques together with a melt pool simulation model will be used to generate the training data. The end goal is to build up a unifying model that can predict hot cracking susceptibility based on information at different scales and aspects.
Modelling of Carbides in Irradiated Steel Microstructure: Andris Freimanis1; Matti Lindroos1; Anssi Laukkanen1; Sicong Ren1; 1VTT Technical research center of Finland
This paper explore ICME workflow that enables the virtual design of radiation resistant materials. Carbides, solute clusters, and dislocation loops are a critical microstructure/material features of reactor pressure vessel steel. Irradiation generates these defects, which contribute to reduction of ductility and the ductile-to-brittle transformation of metals used in fission or future fusion power plants. In this paper, authors' consider the fracture in unirradiated and irradiated microstructures of reactor pressure vessel steel. Finite-element simulation results are compared with peridynamic theory’s results to address key features like defect size effect. Several cases of intragranular and intergranular fractures are presented.