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Data-Science Analysis of the Macro-scale Features Governing the Corrosion to Crack Transition in AA7050-T7451

  • Data-driven Material Investigations: Understanding Fatigue Behavior
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Abstract

This study applies data science approaches (random forest and logistic regression) to determine the extent to which macro-scale corrosion damage features govern the crack formation behavior in AA7050-T7451. Each corrosion morphology has a set of corresponding predictor variables (pit depth, volume, area, diameter, pit density, total fissure length, surface roughness metrics, etc.) describing the shape of the corrosion damage. The values of the predictor variables are obtained from white light interferometry, x-ray tomography, and scanning electron microscope imaging of the corrosion damage. A permutation test is employed to assess the significance of the logistic and random forest model predictions. Results indicate minimal relationship between the macro-scale corrosion feature predictor variables and fatigue crack initiation. These findings suggest that the macro-scale corrosion features and their interactions do not solely govern the crack formation behavior. While these results do not imply that the macro-features have no impact, they do suggest that additional parameters must be considered to rigorously inform the crack formation location.

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Acknowledgements

Funding was provided by Office of Naval Research (US) (Grant Nos. N00014-17-1-2033, N00014-14-1-0012).

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Authors and Affiliations

  1. Center for Electrochemical Science and Engineering, Department of Materials Science and Engineering, University of Virginia, Charlottesville, VA, 22904, USA

    Noelle Easter C. Co & James T. Burns

  2. Data Science Institute, Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA, 22904, USA

    Donald E. Brown

Authors
  1. Noelle Easter C. Co
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  2. Donald E. Brown
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  3. James T. Burns
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Correspondence to Noelle Easter C. Co.

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Co, N.E.C., Brown, D.E. & Burns, J.T. Data-Science Analysis of the Macro-scale Features Governing the Corrosion to Crack Transition in AA7050-T7451. JOM 70, 1168–1174 (2018). https://doi.org/10.1007/s11837-018-2864-6

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  • DOI: https://doi.org/10.1007/s11837-018-2864-6

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