Simulation-Assisted Design and Accelerated Insertion of Materials

Backman, Daniel

doi:10.1007/978-1-4419-0643-4_17

Cited by 8 publications

(3 citation statements)

References 64 publications

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“…Materials development and deployment efforts require evaluation of the properties of large sets of potential material internal structures (generically referred to as microstructures; this description also includes information regarding chemical composition at the relevant length scales) [1][2][3][4][5][6][7][8][9][10][11][12][13]. Currently, experiments and simulations are used for such analyses at a high cost, severely limiting the extent of the materials design spaces explored [14,15].…”

Section: Introductionmentioning

confidence: 99%

Reduced-order structure-property linkages for polycrystalline microstructures based on 2-point statistics

et al. 2017

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Computationally efficient structure-property (S-P) linkages (i.e., reduced order models) are a necessary key ingredient in accelerating the rate of development and deployment of structural materials. This need represents a major challenge for polycrystalline materials, which exhibit rich heterogeneous microstructure at multiple structure/length scales, and exhibit a wide range of properties. In this study, a novel framework is described for extracting S-P linkages in polycrystalline microstructures that are obtained using 2-point spatial correlations (also called 2-point statistics) to quantify the material's microstructure, and principal component analysis (PCA) to represent this information in a reduced dimensional space. Additionally, it is demonstrated that the use of generalized spherical harmonics (GSH) as a Fourier basis for functions defined on the orientation space leads to a compact and computationally efficient representation of the desired S-P linkages. In this study, these novel protocols are developed and demonstrated for elastic stiffness and yield strength predictions for α−Ti microstructures using a dataset produced through microscale finite element simulations.

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Section: Introductionmentioning

confidence: 99%

Reduced-order structure-property linkages for polycrystalline microstructures based on 2-point statistics

et al. 2017

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“…Examples on material modeling and designing can be found elsewhere [9][10][11][12][13][14]. Optimization methods have been used in most cases when solving such problems [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Material Design Methodology for Optimized Wear-Resistant Thermoplastic–Matrix Composites Based on Polyetheretherketone and Polyphenylene Sulfide

et al. 2020

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The main goal of this paper is to design and justify optimized compositions of thermoplastic–matrix wear-resistant composites based on polyetheretherketone (PEEK) and polyphenylene sulfide (PPS). Their mechanical and tribological properties have been specified in the form of bilateral and unilateral limits. For this purpose, a material design methodology has been developed. It has enabled to determine the optimal degrees of filling of the PEEK- and PPS-based composites with carbon microfibers and polytetrafluoroethylene particles. According to the results of tribological tests, the PEEK-based composites have been less damaged on the metal counterpart than the PPS-based samples having the same degree of filling. Most likely, this was due to more uniform permolecular structure and greater elasticity of the matrix. The described methodology is versatile and can be used to design various composites. Its implementation does not impose any limits on the specified properties of the material matrix or the reinforcing inclusions. The initial data on the operational characteristics can be obtained experimentally or numerically. The methodology enables to design the high-strength wear-resistant composites which are able to efficiently operate both in metal–polymer and ceramic–polymer friction units.

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“…Integrated computational materials engineering (ICME) is a bold, transformative initiative calling for a paradigm shift in the way that materials design and development are approached at a fundamental level [1]. Traditional material science practices have emphasized a sequential approach to materials development that spans discovery to deployment and commonly takes 10-20 years.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Enabled Uncertainty Quantification for Modeling Structure–Property Linkages for Fatigue Critical Engineering Alloys Using an ICME Workflow

Whelan

2020

Integr Mater Manuf Innov

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Integrated computational materials engineering (ICME) facilitates efficient approaches to new material discovery and design, as well as optimization of existing materials. Computational models provide a way to rapidly screen candidate material designs such that materials can be tailored for specific applications in the product design cycle. Uncertainty is ubiquitous in ICME process-structure-property workflows; it represents a major barrier to the effective use of modeling results for highconfidence decision support in materials design and development. This work addresses microstructure statistical uncertainties, and demonstrates an approach to quantify, reduce, and propagate these uncertainties through structure-property linkages to provide robust quantification of uncertainties in output properties of interest. Further, this work demonstrates the use of Gaussian process machine learning models to significantly decrease the computational cost of the aforementioned robust uncertainty quantification.

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Simulation-Assisted Design and Accelerated Insertion of Materials

Cited by 8 publications

References 64 publications

Reduced-order structure-property linkages for polycrystalline microstructures based on 2-point statistics

Reduced-order structure-property linkages for polycrystalline microstructures based on 2-point statistics

Material Design Methodology for Optimized Wear-Resistant Thermoplastic–Matrix Composites Based on Polyetheretherketone and Polyphenylene Sulfide

Machine Learning-Enabled Uncertainty Quantification for Modeling Structure–Property Linkages for Fatigue Critical Engineering Alloys Using an ICME Workflow

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