Reconstruction of Stress and Composition Profiles from X-Ray Diffraction Experiments — How to Avoid Ghost Stresses?

Abstract:

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On evaluating lattice strain-depth or stress-depth profiles with X-ray diffraction, the variation of the information depth while combining various tilt angles, in combination with lattice spacing gradients leads to artefacts, so-called ghost or fictitious stresses. X-ray diffraction lattice-strain analysis was simulated for a model stress-depth profile combined with a composition-depth profile. Two principally different methods were investigated for the reconstruction of the actual stress and composition profiles from the simulated data: - considering the stress/strain determined at a specific depth as a weighted average over the actual stress/strain depth profile - considering the lattice spacing determined at a specific depth, for a specific value for as a weighted average over the actual lattice spacing profile for this direction. On the basis of the results it is possible to propose a preferred method for the evaluation of stress/strain and composition profiles, while minimising the risk for ghost stresses.

Info:

Periodical:

Materials Science Forum (Volumes 443-444)

Edited by:

Yvonne Andersson, Eric J. Mittemeijer and Udo Welzel

Pages:

91-94

Citation:

T. L. Christiansen and M. A.J. Somers, "Reconstruction of Stress and Composition Profiles from X-Ray Diffraction Experiments — How to Avoid Ghost Stresses?", Materials Science Forum, Vols. 443-444, pp. 91-94, 2004

Online since:

January 2004

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$41.00

[1] B. Eigenmann, B. Scholtes and E. Macherauch, in: H. Fujiwara, T. Abe, K. Tanaka (Hrsg. ): Residual Stresses –III, Science and Technology (Elsevier Applied Science London, New York 1992), p.601.

[2] M.A.J. Somers and E.J. Mittemeijer: Metallurgical Transactions A Vol. 21A (1990) p.189.

[3] W.G. Sloof, M.A.J. Somers, R. Delhez, Th. H. de Keijser and E.J. Mittemeijer: Residual Stresses in Science and Technology (1987), p.493.

[4] G.A.F. Seber and C.J. Wild: Nonlinear Regression (John Wiley & Sons 1989).