AUTHOR=Tallman Aaron E. , Paul Tanaji , John Denny , Agarwal Arvind TITLE=Uncertainty Quantification of a High-Throughput Profilometry-Based Indentation Plasticity Test of Al 7075 T6 Alloy JOURNAL=Frontiers in Materials VOLUME=9 YEAR=2022 URL=https://www.frontiersin.org/journals/materials/articles/10.3389/fmats.2022.919797 DOI=10.3389/fmats.2022.919797 ISSN=2296-8016 ABSTRACT=

The quantification of spatially variable mechanical response in structural materials remains a challenge. Additive manufacturing methods result in increased spatial property variations—the effect of which on component performance is of key interest. To assist iterative design of additively manufactured prototypes, lower-cost benchtop test methods with high precision and accuracy will be necessary. Profilometry-based indentation plastometry (PIP) promises to improve upon the instrumented indentation test in terms of the measurement uncertainty. PIP uses an isotropic Voce hardening model and inverse numerical methods to identify plasticity parameters. The determination of the baseline uncertainty of PIP test is fundamental to its use in characterizing spatial material property variability in advanced manufacturing. To quantify the uncertainty of the PIP test, ninety-nine PIP tests are performed on prepared portions of a traditionally manufactured Al 7075 plate sample. The profilometry data and the Voce parameter predictions are examined to distinguish contributions of noise, individual measurement uncertainty, and additional set-wide variations. Individual measurement uncertainty is estimated using paired profilometry measurements that are taken from each indentation. Principal component analysis is used to analyze and model the measurement uncertainty. The fitting procedure used within the testing device software is employed to examine the effect of profile variations on plasticity predictions. The expected value of the error in the plasticity parameters is given as a function of the number of tests taken, to support rigorous use of the PIP method. The modeling of variability in the presence of measurement uncertainty is discussed.