Digital image correlation and infrared thermography data for seven unique geometries of 304L stainless steel

Jones, E. M. C.; Reu, P. L.; Kramer, S. L. B.; Jones, A. R.; Carroll, J. D.; Karlson, K. N.; Seidl, D. T.; Turner, D. Z.

doi:10.1038/s41597-024-03949-y

Digital image correlation and infrared thermography data for seven unique geometries of 304L stainless steel

Journal Article · Wed Oct 09 00:00:00 EDT 2024 · Scientific Data

DOI:https://doi.org/10.1038/s41597-024-03949-y· OSTI ID:2477550

^[1]; Reu, P. L. ^[1]; ^[1]; Jones, A. R. ^[1]; Carroll, J. D. ^[1]; Karlson, K. N. ^[2]; ^[1]; Turner, D. Z. ^[1]

Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Sandia National Laboratories (SNL-NM), Livermore, NM (United States)

Material Testing 2.0 (MT2.0) is a paradigm that advocates for the use of rich, full-field data, such as from digital image correlation and infrared thermography, for material identification. By employing heterogeneous, multi-axial data in conjunction with sophisticated inverse calibration techniques such as finite element model updating and the virtual fields method, MT2.0 aims to reduce the number of specimens needed for material identification and to increase confidence in the calibration results. To support continued development, improvement, and validation of such inverse methods—specifically for rate-dependent, temperature-dependent, and anisotropic metal plasticity models—we provide here a thorough experimental data set for 304L stainless steel sheet metal. The data set includes full-field displacement, strain, and temperature data for seven unique specimen geometries tested at different strain rates and in different material orientations. Commensurate extensometer strain data from tensile dog bones is provided as well for comparison. We believe this complete data set will be a valuable contribution to the experimental and computational mechanics communities, supporting continued advances in material identification methods.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

Grant/Contract Number:: NA0003525

OSTI ID:: 2477550

Report Number(s):: SAND--2024-15563J

Journal Information:: Scientific Data, Journal Name: Scientific Data Journal Issue: 1 Vol. 11; ISSN 2052-4463

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

References (14)

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DIC Challenge 2.0: Developing Images and Guidelines for Evaluating Accuracy and Resolution of 2D Analyses Reu, P. L.; Blaysat, B.; Andó, E. Experimental Mechanics, Vol. 62, Issue 4 https://doi.org/10.1007/s11340-021-00806-6	journal	January 2022
On the Importance of Direct-Levelling for Constitutive Material Model Calibration using Digital Image Correlation and Finite Element Model Updating Fayad, S. S.; Jones, E. M. C.; Seidl, D. T. Experimental Mechanics, Vol. 63, Issue 3 https://doi.org/10.1007/s11340-022-00926-7	journal	December 2022
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Validation of finite‐element models using full‐field experimental data: Levelling finite‐element analysis data through a digital image correlation engine Lava, Pascal; Jones, Elizabeth M. C.; Wittevrongel, Lukas Strain, Vol. 56, Issue 4 https://doi.org/10.1111/str.12350	journal	April 2020
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Data set accompanying "Full-Field Digital Image Correlation (DIC) and Infrared Thermography (IR) Data for Seven Unique Geometries of 304L Stainless Steel Sheet Metal" Jones, Elizabeth; Reu, Phillip; Karmer, Sharlotte Figshare+ https://doi.org/10.25452/figshare.plus.25483534	dataset	January 2024
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