Materials, Data, and Informatics

Kalidindi, Surya R.

doi:10.1016/b978-0-12-410394-8.00001-1

Cited by 18 publications

(25 citation statements)

References 71 publications

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“…Metallurgists can now make use of databases of experimental data surrounding structure-property relationships, loading-specific precipitation, coarsening, phase transformation and even complete-lifetime predictions 48,83,84 . Data-driven approaches can use machine learning methods [85][86][87][88][89] that can sometimes be computationally more tractable than simulation-based approaches that aim to avoid damage-susceptible microstructures (to reduce failure) 90 or to make predictions for when to apply repair treatments and how alloy compositions can be rendered more compatible for recycling.…”

Section: Longevity By Corrosion Protection Lifetime Extension and Reusementioning

confidence: 99%

Strategies for improving the sustainability of structural metals

Raabe

Taşan

Olivetti

2019

Nature

375

143

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Metagoods 16% Vehicles 13% Industrial equipment 16% Construction 55% Re ne Smelter Mine Fabrication Manufacture Use New Scrap Old Scrap Land ll Carbon scrap/nickel to other scrap markets Stock End-of-life products c Transportation 19% Electronics 5% Chemicals 1% Industrial machinery 30% Household appliances and metal goods 28% Building and infrastructure17% Mine Rutile Mill product Ti ingot Manufacturing scrap Mill scrap End-of-life products Use Ferrotitanium Ti sponge TiO 2 feed TiCl 4

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Section: Longevity By Corrosion Protection Lifetime Extension and Reusementioning

confidence: 99%

Strategies for improving the sustainability of structural metals

Raabe

Taşan

Olivetti

2019

Nature

375

143

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“…such as number, defect volume and cross-sectional area reductions due to defects, initial investigation of higher-order spatial statistics 43 using the two-point correlation function 44 was also pursued. Unfortunately, these investigations have not yet shown anything conclusive or additionally predictive in comparision to the first order metrics presented above.…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Corroborating tomographic defect metrics with mechanical response in an additively manufactured precipitation-hardened stainless steel

Madison

Underwood

Swiler

et al. 2018

AIP Conference Proceedings

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bhjared@sandia.gov, f) jmrodel@sandia.gov, g) bsalzbr@sandia.govAbstract. The intrinsic relation between structure and performance is a foundational tenant of most all materials science investigations. While the specific form of this relation is dictated by material system, processing route and performance metric of interest, it is widely agreed that appropriate characterization of a material allows for greater accuracy in understanding and/or predicting material response. However, in the context of additive manufacturing, prior models and expectations of material performance must be revisited as performance often diverges from traditional values, even among well explored material systems. This work utilizes micro-computed tomography to quantify porosity and lack of fusion defects in an additively manufactured stainless steel and relates these metrics to performance across a statistically significant population using high-throughput mechanical testing. The degree to which performance in additively manufactured stainless steel can and cannot be correlated to detectable porosity will be presented and suggestions for performing similar experiments will be provided.

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“…Data-driven Process-Structure-Property (PSP) linkages [26] provides a systemic, modular, and hierarchical framework for community engagement (i.e., several people making complementary or overlapping contributions to the overall curation of materials knowledge). Computationally cheap PSP linkages also communicate effectively the curated materials knowledge to design and manufacturing experts in highly accessible formats.…”

Section: Introductionmentioning

confidence: 99%

Materials Knowledge Systems in Python—a Data Science Framework for Accelerated Development of Hierarchical Materials

Brough

Wheeler

Kalidindi

2017

Integr Mater Manuf Innov

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There is a critical need for customized analytics that take into account the stochastic nature of the internal structure of materials at multiple length scales in order to extract relevant and transferable knowledge. Data driven Process-Structure-Property (PSP) linkages provide systemic, modular and hierarchical framework for community driven curation of materials knowledge, and its transference to design and manufacturing experts. The Materials Knowledge Systems in Python project (PyMKS) is the first open source materials data science framework that can be used to create high value PSP linkages for hierarchical materials that can be leveraged by experts in materials science and engineering, manufacturing, machine learning and data science communities. This paper describes the main functions available from this repository, along with illustrations of how these can be accessed, utilized, and potentially further refined by the broader community of researchers.

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Materials, Data, and Informatics

Cited by 18 publications

References 71 publications

Strategies for improving the sustainability of structural metals

Strategies for improving the sustainability of structural metals

Corroborating tomographic defect metrics with mechanical response in an additively manufactured precipitation-hardened stainless steel

Materials Knowledge Systems in Python—a Data Science Framework for Accelerated Development of Hierarchical Materials

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