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PUBLICATIONS

A Materials Data Framework and Dataset for Elastomeric Foam Impact Mitigating Materials

Published

Author(s)

Alexander Landauer, Orion Kafka, Newell Moser, Ian Foster, Ben Blaiszik, Aaron M. Forster

Abstract

The availability of materials data for impact-mitigating materials has lagged behind applications-based data. For example, data describing on-field helmeted impacts are available, whereas material behaviors for the constituent impact mitigating materials used in helmet designs lack a cohesive database. Here, we describe a FAIR (findable, accessible, interoperable, reusable) database with structure and property data for elastic impact protection foams. The unique continuum-scale constitutive behaviors of foams emerge from the interplay of polymer properties, internally contained gas, and geometric structure. Continuum behavior is rate and temperature sensitive, therefore, the data is necessarily multimodal. The database includes data from a variety of imaging (micro-computed tomography, digital image correlation) and force-sensing instruments (dynamic mechanical analysis, universal test system) under a wide range of experimental conditions. These data facilitate modeling efforts to capture foam response via, e.g., homogenization, direct numerical simulation, or phenomenological fitting. The database is implemented using data services and software provided by the Materials Data Facility of the NIST Center for Hierarchical Materials Design.
Citation
Scientific Data
Volume
10
Pub Type
Journals

Download Paper

https://doi.org/10.1038/s41597-023-02092-4
Local Download

Keywords

Foam, impact mitigating materials, impact protection, digital image correlation, dynamic mechanical analysis, micro computed tomography, material data, elastomer, vinyl nitrile
Polymers, Mechanical properties and Composition and structure

Citation

Landauer, A. , Kafka, O. , Moser, N. , Foster, I. , Blaiszik, B. and Forster, A. (2023), A Materials Data Framework and Dataset for Elastomeric Foam Impact Mitigating Materials, Scientific Data, [online], https://doi.org/10.1038/s41597-023-02092-4, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=935440 (Accessed December 11, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created June 5, 2023, Updated June 9, 2023