Literature DB >> 28664888

Accuracy of stress measurement by Laue microdiffraction (Laue-DIC method): the influence of image noise, calibration errors and spot number.

F G Zhang1, M Bornert2, J Petit3, O Castelnau1.   

Abstract

Laue microdiffraction, available at several synchrotron radiation facilities, is well suited for measuring the intragranular stress field in deformed materials thanks to the achievable submicrometer beam size. The traditional method for extracting elastic strain (and hence stress) and lattice orientation from a microdiffraction image relies on fitting each Laue spot with an analytical function to estimate the peak position on the detector screen. The method is thus limited to spots exhibiting ellipsoidal shapes, thereby impeding the study of specimens plastically deformed. To overcome this difficulty, the so-called Laue-DIC method introduces digital image correlation (DIC) for the evaluation of the relative positions of spots, which can thus be of any shape. This paper is dedicated to evaluating the accuracy of this Laue-DIC method. First, a simple image noise model is established and verified on the data acquired at beamline BM32 of the European Synchrotron Radiation Facility. Then, the effect of image noise on errors on spot displacement measured by DIC is evaluated by Monte Carlo simulation. Finally, the combined effect of the image noise, calibration errors and the number of Laue spots used for data treatment is investigated. Results in terms of the uncertainty of stress measurement are provided, and various error regimes are identified.

Keywords:  Laue microdiffraction; digital image correlation; image noise; stress analysis

Year:  2017        PMID: 28664888     DOI: 10.1107/S1600577517006622

Source DB:  PubMed          Journal:  J Synchrotron Radiat        ISSN: 0909-0495            Impact factor:   2.616


  2 in total

1.  Processing Laue Microdiffraction Raster Scanning Patterns with Machine Learning Algorithms: A Case Study with a Fatigued Polycrystalline Sample.

Authors:  Peng Rong; Fengguo Zhang; Qing Yang; Han Chen; Qiwei Shi; Shengyi Zhong; Zhe Chen; Haowei Wang
Journal:  Materials (Basel)       Date:  2022-02-17       Impact factor: 3.623

2.  LaueNN: neural-network-based hkl recognition of Laue spots and its application to polycrystalline materials.

Authors:  Ravi Raj Purohit Purushottam Raj Purohit; Samuel Tardif; Olivier Castelnau; Joel Eymery; René Guinebretière; Odile Robach; Taylan Ors; Jean-Sébastien Micha
Journal:  J Appl Crystallogr       Date:  2022-06-15       Impact factor: 4.868

  2 in total

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