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In the energy production and transportation industries, numerous metallic structures may be subjected to at least several billions of cycles, i.e. loaded in the very high cycle fatigue domain (VHCF). Therefore, to design structures in the VHCF domain, a reliable methodology is necessary. One useful quantity to characterize plastic activity at the microscopic scale and fatigue damage evolution is the mechanical work supplied to a material. However, the estimation of this mechanical work in a metal during ultrasonic fatigue tests remains challenging. This paper aims to present an innovative methodology to quantify this. An experimental procedure was developed to estimate the mechanical work from stress and total strain evolution measurements during one loading cycle with a time accuracy of about 50 ns. This was achieved by conducting time-resolved X-ray diffraction coupled to strain gauge measurements at a synchrotron facility working in pulsed mode (single-bunch mode). [ABSTRACT FROM AUTHOR]

Polycrystalline materials exhibit physical properties that are driven by both the interatomic crystallographic structure as well as the nature and density of structural defects. Crystallographic evolutions driven by phase transitions and associated twinning process can be observed in situ in three-dimensional (3D) using monochromatic synchrotron radiation at very high temperatures (over 1000 °C). This paper focuses on continuous measurements of the 3D-reciprocal space maps by high-resolution x-ray diffraction as a function of temperature along a phase transition process occurring between 1200 °C and room temperature. These high precision measurements allow observing the reciprocal space node splitting and the evolution of the diffuse scattering signal around that node as a function of temperature. The capability of this experimental method is illustrated by direct in situ high temperature measurements of the 3D splitting of a reciprocal space node due to phase transition recorded on dense pure zirconia polycrystals. [ABSTRACT FROM AUTHOR]

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 nanostructure, 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We have used Differential Aperture X-ray Microscopy (DAXM) to measure grain orientations and deviatoric elastic strains in 3D around a tin whisker. The results show strain gradients through the depth of the tin coating, revealing a higher strain deeper in the Sn layer. These higher strains are explained by the volume change occurring during growth of the intermetallic phase Cu 6 Sn 5 at the interface between the Cu substrate and the Sn coating and at grain boundaries between Sn grains. [ABSTRACT FROM AUTHOR]

• A Bayesian framework with high-energy X-ray diffraction and elastic self-consistent (ELSC) micromechanical model is successfully employed to identify the single-crystal elastic constants and their uncertainities in a near-β titanium alloy (Ti–10V–2Fe–3Al). • Among the different material parameters (grain texture, morphology, volume fraction) of the micromechanical model, the grains morphology was found to be significant in obtaining best fit with experimental data. • The Bayesian identified single crystal elastic constants of the β phase are C 11 β = 92.6 ± 19.1 GPa , C 12 β = 82.5 ± 16.3 GPa , C 44 β = 43.5 ± 7.1 GPa accounting for prolate spheroid grains with aspect ratio of ~3.8 ± 0.8. A two-phase near-β titanium alloy (Ti–10V–2Fe–3Al, or Ti-1023) in its as-forged state is employed to illustrate the feasibility of a Bayesian framework to identify single-crystal elastic constants (SEC). High Energy X-ray diffraction (HE-XRD) obtained at the Diamond synchrotron source are used to characterize the evolution of lattice strains for various grain orientations during in situ specimen loading in the elastic regime. On the other hand, specimen behavior and grain deformation are estimated using the elastic self-consistent (ELSC) homogenization scheme. The XRD data and micromechanical modelling are revisited with a Bayesian framework. The effect of different material parameters (crystallographic and morphological textures, phase volume fraction) of the micromechanical model and the biases introduced by the XRD data on the identification of the SEC of the β phase are systematically investigated. In this respect, all the three cubic elastic constants of the β phase ( C 11 β , C 12 β , C 44 β) in the Ti-1023 alloy have been derived with their uncertainties. The grain aspect ratio in the ELSC model, which is often not considered in the literature, is found to be an important parameter in affecting the identified SEC. The Bayesian inference suggests a high probability for non-spherical grains (aspect ratio of ∼ 3.8 ± 0.8) : C 11 β = 92.6 ± 19.1 GPa , C 12 β = 82.5 ± 16.3 GPa , C 44 β = 43.5 ± 7.1 GPa. The uncertainty obtained by Bayesian approach lies in the range of ~1-3 GPa for the shear modulus μ ′ = C 11 β − C 12 β 2 , and ~7 GPa for the shear modulus μ ″ = C 44 β , while it is significantly larger in the case of the bulk modulus C 11 β + 2 C 12 β 3 (~17-24 GPa). [Display omitted] [ABSTRACT FROM AUTHOR]