A method of detecting an anomaly in a crystallographic structure, the method comprising: illuminating the structure with x-ray radiation in a known direction relative to the crystallographic orientation; positioning the structure such that its crystallographic orientation is known; detecting a pattern of the diffracted x-ray radiation transmitted through the structure; generating the simulated pattern based on the known direction relative to the crystallographic orientation; comparing the detected pattern to a simulated pattern for x-ray radiation illuminating in the known direction; and, detecting the anomaly in the crystallographic structure based on the comparison.

Provided is a scattering measurement analysis method including obtaining a theoretical scattering intensity from a structural model that contains a lot of scatterers, wherein the obtaining of a theoretical scattering intensity includes obtaining a contribution to the theoretical scattering intensity of a pair of a scatterer “m” and a scatterer “n” existing at a distance “r” from the scatterer “m” among a plurality of scatterers by at least one of calculations in accordance with the distance “r”, the calculations including a first calculation of calculating contributions of the scatterer “m” and the scatterer “n” from respective scattering factors f.sub.m(q) and f.sub.n*(q) and a center-to-center distance r.sub.mn between the scatterer “m” and the scatterer “n”, and a second calculation of substituting the scattering factor f.sub.n*(q) of the scatterer “n” by a first representative value and substituting a probability density function of the number of scatterers existing at the distance “r” by a constant value.

A magnetoscop inspection system includes a magnetoscop, a computed tomography unit, and a corrosion model unit. The magnetoscop measures a permeability at a plurality of inspection points of a turbine component. The computed tomography unit generates a measured profile of a hollowed portion of the turbine component based at least in part on the permeability at the measured inspection points. The corrosion model unit stores in memory at least one reference computed tomography profile of a known turbine component. The magnetoscop inspection system determines a structural integrity of the turbine component based on a comparison between the measured profile and the reference profile corresponding to the turbine component currently under inspection.

The sample component determination method includes: acquiring a spectrum of a sample which is measured by a wavelength dispersive X-ray analyzer; defining a target element to be analyzed in the sample and an input wavelength range corresponding to the target element; and determining a chemical bonding state of the target element in the sample by inputting a partial spectrum of the sample spectrum that falls within the input wavelength range to a first trained model.

Determining a property of a layer of an integrated circuit (IC), the layer being formed over an underlayer, is implemented by performing the steps of: irradiating the IC to thereby eject electrons from the IC; collecting electrons emitted from the IC and determining the kinetic energy of the emitted electrons to thereby calculate emission intensity of electrons emitted from the layer and electrons emitted from the underlayer calculating a ratio of the emission intensity of electrons emitted from the layer and electrons emitted from the underlayer; and using the ratio to determine material composition or thickness of the layer. The steps of irradiating IC and collecting electrons may be performed using x-ray photoelectron spectroscopy (XPS) or x-ray fluorescence spectroscopy (XRF).

The present invention discloses, a full-view-field quantitative statistical distribution representation method for microstructures of phases in a metal material, comprising the following steps: step a: labeling phases, cloud clutters and matrixes by Labelme, and then making standard feature training samples; step b: building a deep learning-based feature recognition and extraction model by means of BDU-Net; step e: collecting feature maps in the metal material to be detected; step d: automatically recognizing and extracting the phases; and step e: performing in-situ quantitative statistical distribution representation on the phases in the full view field within a large range. The full-view-field quantitative statistical distribution representation method for microstructures of phases in a metal material provided by the present invention realizes automatic, high-speed and high-quality recognition and extraction of features of phases in the metal material

The present disclosure relates to a method for detecting a fibre misalignment in an elongated structure, such as a wind turbine blade component. The elongated structure has a length along a longitudinal direction and comprises a plurality of stacked reinforcing fibre layers. The plurality of fibre layers comprises fibres having an orientation aligned, unidirectionally, substantially in the longitudinal direction. The method comprises scanning a surface of the elongated structure for identifying one or more surface irregularities, selecting one or more regions of interest comprising said one or more surface irregularities, examining said region of interest using penetrating radiation, and determining a position and/or size of the fibre misalignment based on said examining step.

There is provided a method for assigning an attribute to x-ray attenuation including scanning in an x-ray scanning device first and second reference materials each having known atomic composition, dimensions and orientation in the scanning device. The device emits x-rays which pass through the first reference material with first reference material path lengths and the second reference material with second reference material path lengths. The x-rays are detected by detectors to provide a plurality of dual-energy attenuation images having dual-energy x-ray attenuation information. The dual-energy x-ray attenuation information in the dual-energy attenuation images is associated with the first and second reference material path lengths. Then, each of the first and second reference material path lengths are expressed collectively as a function of the associated attenuation information to define attenuation surfaces upon which may be imposed dual-energy attenuation values to determine corresponding first and second reference material equivalent path lengths.

A system for predicting rare earth elements (REEs) in a feedstock sample includes a measurement instrument that records a measurement for a sample, a processor communicatively coupled to the measuring instrument, and a memory communicatively coupled to the processor and containing machine readable instructions that, when executed by the processor, cause the processor to correlate the measurement series using a model; and predict a presence of one or more rare earth element based at least in part on the correlation. A method for predicting rare earth elements includes measuring feedstock samples via XRF or PGNAA, to generate a measurements of elements of interest with a lower atomic weight than REEs; correlating the measurements with a model; and predicting a presence of one or more rare earth elements based at least in part on the correlation.

A method for the inspection of contained flowable materials within containers, such as detecting an explosive liquid in a luggage, and an apparatus for performing the method are described. The method includes the steps of: performing a radiation scan, using X-rays or Gamma rays, of a target item container of contained flowable material in a radiation scanning system to derive a spatially distributed and spectroscopically resolved measured dataset of the intensity of radiation emergent from the target item; considering the spatially distributed and spectroscopically resolved dataset of transmitted radiation intensity to be nominally determined in accordance with a relationship: [O].Math.[]=[] where the operators [] and [] define, respectively, physical parameters describing the liquid and the container and the system response and the operator [O] defines the relationships between the system response and the liquid and container parameters; numerically processing the measured dataset by operator inversion in order to derive a best fit solution of: []=[O].sup.1.Math.[]; and using that derived solution to determine the threat status of the target item.