Abstract
We describe the analysis of in-situ HT-XRD data of a dual phase stainless steel exposed to a complex thermal cycle of heating, holding and cooling. For the conditions used only low quality diffraction data could be collected. Peak positions, peak areas and peak broadening are modeled by the Rietveld method. The low signal-to noise ratio and the presence of artificial peaks due to tube tails complicate the data evaluation. In a first attempt the parameters are refined by a local optimization procedure (e.g. Levenberg-Marquardt). However, this procedure fails by being caught in one of several local minima. Next, a Bayesian approach with a Markov Chain Monte Carlo (MCMC) algorithm is used as a global optimization procedure to refine the simulated Rietveld diffractograms. Accurate estimates of the evolution of the phase fractions and dislocation densities in martensite and austenite during all stages of the thermal cycle are obtained by this MCMC algorithm. While an approach based on multivariate second order Taylor series completely underestimates the error, the uncertainties in the model parameters could be estimated appropriately from histograms obtained by the MCMC method.
Original language | English |
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Article number | 110860 |
Number of pages | 9 |
Journal | Materials Characterization |
Volume | 172 |
DOIs | |
Publication status | Published - 2021 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- Bayesian statistics
- Dislocation densities
- Levenberg-Marquardt
- Markov Chain Monte Carlo
- Rietveld method
- Stainless steels