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

Laue microdiffraction on polycrystalline samples above 1500 K achieved with the QMAX-µLaue furnace

X-ray Laue microdiffraction aims to characterize microstructural and mechanical fields in polycrystalline specimens at the sub-micrometre scale with a strain resolution of ∼10-4. Here, a new and unique Laue microdiffraction setup and alignment procedure is presented, allowing measurements at temperatures as high as 1500 K, with the objective to extend the technique for the study of crystalline phase transitions and associated strain-field evolution that occur at high temperatures. A method is provided to measure the real temperature encountered by the specimen, which can be critical for precise phase-transition studies, as well as a strategy to calibrate the setup geometry to account for the sample and furnace dilation using a standard α-alumina single crystal. A first application to phase transitions in a polycrystalline specimen of pure zirconia is provided as an illustrative example.

Upgraded LauePt4 for rapid recognition and fitting of Laue patterns from crystals with unknown orientations

The LauePt program is a popular and easy-to-use crystallography tool for indexing and simulating X-ray Laue patterns, but its previous versions lack search functions for recognizing Laue patterns taken from crystals with unknown orientations. To overcome this obstacle, a major upgrade of the program, called LauePt4, is presented with three robust search schemes implemented: (i) crystal rotation along a single diffraction vector, (ii) a look-up method to search for reflection pairs matching the interplanar angle of two selected diffraction spots, and (iii) a more efficient look-up scheme to search for reflection triplets matching three interplanar angles. Extensive tests show that all these schemes, together with the convenient graphical user interfaces and highly optimized computing algorithms, are reliable and powerful for recognizing and fitting Laue patterns of any crystal taken under any diffraction geometry.

CNN-Based Laue Spot Morphology Predictor for Reliable Crystallographic Descriptor Estimation

Laue microdiffraction is an X-ray diffraction technique that allows for the non-destructive acquisition of spatial maps of crystallographic orientation and the strain state of (poly)crystalline specimens. To do so, diffraction patterns, consisting of thousands of Laue spots, are collected and analyzed at each location of the spatial maps. Each spot of these so-called Laue patterns has to be accurately characterized with respect to its position, size and shape for subsequent analyses including indexing and strain analysis. In the present paper, several approaches for estimating these descriptors that have been proposed in the literature, such as methods based on image moments or function fitting, are reviewed. However, with the increasing size and quantity of Laue image data measured at synchrotron sources, some datasets become unfeasible in terms of computational requirements. Moreover, for irregular Laue spots resulting, e.g., from overlaps and extended crystal defects, the exact shape and, more importantly, the position are ill-defined. To tackle these shortcomings, a procedure using convolutional neural networks is presented, allowing for a significant acceleration of the characterization of Laue spots, while simultaneously estimating the quality of a Laue spot for further analyses. When tested on unseen Laue spots, this approach led to an acceleration of 77 times using a GPU while maintaining high levels of accuracy.

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

A feed-forward neural-network-based model is presented to index, in real time, the diffraction spots recorded during synchrotron X-ray Laue microdiffraction experiments. Data dimensionality reduction is applied to extract physical 1D features from the 2D X-ray diffraction Laue images, thereby making it possible to train a neural network on the fly for any crystal system. The capabilities of the LaueNN model are illustrated through three examples: a two-phase nano-structure, a textured high-symmetry specimen deformed in situ and a polycrystalline low-symmetry material. This work provides a novel way to efficiently index Laue spots in simple and complex recorded images in <1 s, thereby opening up avenues for the realization of real-time analysis of synchrotron Laue diffraction data.

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

The massive amount of diffraction images collected in a raster scan of Laue microdiffraction calls for a fast treatment with little if any human intervention. The conventional method that has to index diffraction patterns one-by-one is laborious and can hardly give real-time feedback. In this work, a data mining protocol based on unsupervised machine learning algorithm was proposed to have a fast segmentation of the scanning grid from the diffraction patterns without indexation. The sole parameter that had to be set was the so-called “distance threshold” that determined the number of segments. A statistics-oriented criterion was proposed to set the “distance threshold”. The protocol was applied to the scanning images of a fatigued polycrystalline sample and identified several regions that deserved further study with, for instance, differential aperture X-ray microscopy. The proposed data mining protocol is promising to help economize the limited beamtime.

Alignment of sample position and rotation during in situ synchrotron X-ray micro-diffraction experiments using a Laue cross-correlation approach

Laue micro-diffraction has proven to be able to reveal material properties at the sub-grain scale for many polycrystalline materials and is now routinely available at several synchrotron facilities, providing an approach for nondestructive three-dimensional probing of the microstructures and mechanical states of materials. However, for in situ experiments, maintaining the positioning of the sample throughout the experiment, to achieve a good alignment of the characterized volumes, is a challenging issue. The aim of the present work is to address this problem by developing an approach based on digital image correlation of focused-beam Laue diffraction patterns. The method uses small changes in the diffraction signal as a focused X-ray beam is scanned over a surface region to allow corrections to be made for both sample lateral movement and rotation. The method is demonstrated using a tensile deformation experiment on an Al sample with 2.5 µm average grain size. The results demonstrate an accuracy of 0.5 µm for sample position registration and a precision in sample rotation of ∼0.01°. The proposed method is fast to implement and does not require the use of additional surface markers.

EBSD-assisted Laue microdiffraction for microstrain analysis

The X-ray Laue microdiffraction (µLaue) technique has been establishing itself as a reliable means for microstrain analysis for the past few decades. One problem with this technique is that when the crystal size is significantly smaller than the probed volume and when the diffracting crystals are closely oriented, a large number of individual µLaue patterns are superimposed in a complex way on the recorded diffraction images. In that case, because of the difficulty of isolating unambiguously a single-grain µLaue pattern, a reliable analysis of strains is tedious manually and hardly achievable with current automated methods. This issue is even more severe for low-symmetry crystals or when high-energy X-rays are used, since each single-crystal µLaue pattern already contains a large number of spots. This paper proposes overcoming this challenge through the development of a combined approach coupling µLaue and electron backscatter diffraction (EBSD). The capabilities of this `EBSD-assisted µLaue' automated method are illustrated on a monoclinic zirconia-based specimen and µLaue diffraction patterns are analysed with the crystal orientation input from EBSD. The obtained results are statistically reliable, reproducible and provide a physical insight into the micromechanical characteristics of the material.

Direct measurement of local constitutive relations, at the micrometre scale, in bulk metallic alloys

Multiscale models involving crystal plasticity are essential to predict the elastoplastic behavior of structural materials with respect to their microstructure. However, those models are often limited by a poor knowledge of the local constitutive behavior. This article reports a method to measure the mechanical behavior directly, at the micrometre scale, in bulk crystalline materials. Local strain and stress states were evaluated at the surface of a bent stainless steel crystal by combining total strain measurements – performed with the digital image correlation technique on optical images – with elastic strain measurements obtained by Laue microdiffraction. A local constitutive relation was measured, in an efficient nondestructive way, without the need for full-field simulations. The method was validated by a comparison between the measured local behavior and the macroscopic behavior of the single crystal.