Robust Empirical Predictions of Residual Performance of Damaged Composites with Quantified Uncertainties

  • W. J. R. Christian
  • E. A. Patterson
  • F. A. DiazDelaO
Article

Abstract

Robust predictions with estimated uncertainties were made for the residual strength of impact-damaged composite laminates based on simple non-destructive measurements of the size of the damage from ultrasound C-scans. Experimental data was acquired for two sets of composite coupons, one with a crossply and the other with a quasi-isotropic layup. The laminates were subject to drop-weight impacts, non-destructively evaluated using ultrasound and then loaded to failure in bending. An empirical model of the residual strength of each laminate layup, as a function of the ultrasound measurements, was generated by fitting a Bayesian linear regression model to the normalised measured data. Bayesian linear regression was demonstrated to provide conservative estimates when only minimal data is available. Unlike classical regression, this technique provides a robust treatment of outliers, which avoids underestimation of residual strength. The Leave-One-Out-Cross-Validation (LOOCV) metric was used to assess the performance of models allowing for the quantitative comparison of the predictive power of regression models as well as being consistent in the presence of outliers in the data. The LOOCV metric indicated that predictions of residual strength are up to 25% more accurate when based on damage area than when using measurements of the damage width or length. The proposed approach provides a robust methodology for performing damage assessments in safety critical composite components based on reliable predictions with quantified uncertainties.

Keywords

Residual strength prediction Robust Bayesian regression Composite materials Impact damage Quantified uncertainty Ultrasonic NDE 

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • W. J. R. Christian
    • 1
  • E. A. Patterson
    • 1
  • F. A. DiazDelaO
    • 1
  1. 1.School of EngineeringUniversity of LiverpoolLiverpoolUK

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