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PUBLICATIONS

Opportunities for Inverse Analysis in Dynamic Tensile Testing

Published

Author(s)

Steven P. Mates, Fadi Abu-Farha

Abstract

Dynamic tensile testing using Kolsky Bar methods are used to assess crashworthiness of new structural materials needed for lightweight automotive design. High speed Digital Image Correlation (DIC) measurements routinely show that the strain experienced by dynamic tensile specimens deviates markedly from what is expected from the original strain wave analysis techniques used in tensile Kolsky bar metrology. Deviations can be manifest either by different average strain values over the gage section, or by departures from strain uniformity, or both. The former can be attributed to plastic yielding in the specimen outside the gage section, while the latter concerns specimen geometry and material hardening effects. These issues are sometimes difficult to eliminate through simple modifications of the sample or the test design. Finally, it is of interest to make use of the data beyond necking, where the strain state departs significantly from ideal conditions. These metrology issues lend themselves to solution by inverse methods, where full field strain measurements and global load measurement data are available. In this paper we describe typical measurement data and explore methods to identify the constitutive response from dynamic tensile tests.
Proceedings Title
Proceeding of the Society for Experimental Mechanics 2016 Annual Meeting
Conference Dates
June 6-9, 2016
Conference Location
Orlando, FL
Pub Type
Conferences

Download Paper

https://doi.org/10.1007/978-3-319-41600-7
Local Download

Keywords

Kolsky Bar, High Strain Rate, Advanced High Strength Steels, Automotive Light-Weighting, High Speed Digital Image Correlation, Finite Element Analysis, Virtual Fields Method
Materials

Citation

Mates, S. and Abu-Farha, F. (2016), Opportunities for Inverse Analysis in Dynamic Tensile Testing, Proceeding of the Society for Experimental Mechanics 2016 Annual Meeting, Orlando, FL, [online], https://doi.org/10.1007/978-3-319-41600-7 (Accessed April 27, 2024)

Created June 6, 2016, Updated February 6, 2020