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Ill-posedness and the bias-variance tradeoff in residual stress measurement inverse solutions
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  • Marco Beghini ,
  • Tommaso Grossi ,
  • Michael B. Prime ,
  • Ciro Santus
Marco Beghini
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Tommaso Grossi
Università di Pisa

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Michael B. Prime
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Ciro Santus
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Background: Relaxation methods determine residual stresses by measuring the deformations produced by incremental removal of a subdomain of the specimen.
Measured strains at any given increment, determined by the cumulative effect of the relieved stresses, appear as an integral equation, which must be inverted to obtain residual stresses. In practice, stress distributions are discretized by a finite-dimensional basis, to transform the integral equations into a linear system of equations, which is often ill-conditioned.
Objective: This article demonstrates that the problem is actually ill-posed and comes with an inherent bias-variance tradeoff.
Methods: The hole drilling method is used as an example application, and the practical effects of ill-posedness are illustrated.
Results: Traditional regularization of the solution by limiting the resolution of the discretization reduces solution variance (noise) at the expense of increased bias
and often results in the ultimately harmful practice of taking fewer data points. A careful analysis including the alternate Tikhonov regularization approach shows
that the highest number of measurements should always be taken to reduce the variance for a given regularization scheme. Unfortunately, the variability of a regularized solution cannot be used to build a valid confidence interval, since an unknown bias term is always present in the true overall error.
Conclusions: The mathematical theory of ill-posed problems provides tools to manage the bias-variance tradeoff on a reasonable statistical basis, especially
when the statistical properties of measurement errors are known. In the long run, physical arguments that provide constraints on the true solution would be of utmost importance, as they could regularize the problem
without introducing an otherwise unknown bias. Constraining the minimum length scale to some physically meaningful value is one promising possibility.
Mar 2023Published in Experimental Mechanics volume 63 issue 3 on pages 495-516. 10.1007/s11340-022-00928-5