Weibull Regression Analysis of Filtered Surface Roughness Data and Strain Prediction in a Metallic Alloy
Joseph B. Hubbard, Mark R. Stoudt, Antonio Possolo
We fit a two-parameter Weibull regression (WR) model by maximum likelihood estimation (MLE) to filtered surface roughness data. These data were acquired by scanning confocal laser microscopy performed on aluminum alloy AA5754-O surfaces that were subjected to a range of plastic strain intensities in different three in-plane strain modes. Noting that one of the two WR parameters is, to a good approximation, invariant with strain intensity for a given strain mode, we find that the variation of the second parameter with strain intensity conforms to a simple quadratic form. These forms may then be used to create accurate, single parameter predictors of both strain mode and strain intensity up to and including the onset of critical strain localization and/or failure.
, Stoudt, M.
and Possolo, A.
Weibull Regression Analysis of Filtered Surface Roughness Data and Strain Prediction in a Metallic Alloy, Materials Science and Technology, [online], https://doi.org/10.1179/026708310x12701149768179, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=901996
(Accessed December 5, 2023)