Automated phase maps are an important tool for characterizing samples but the data quality must be evaluated. Common options include the overlay of phases on backscattered electron (BSE) images and phase composition averages and standard deviations. Both these methods have major limitations. We propose two methods of evaluation involving principal component analysis. First, a red–green–blue composite image of the first three principal components, which comprise the majority of the chemical variation, which provides a good reference against which phase maps can be compared. Advantages over a BSE image include discriminating between similar mean atomic number phases and sensitivity across the entire range of mean atomic numbers present in a sample. Second, principal component maps for identified phases, to examine for chemical variation within phases. This ensures the identification of unclassified phases and provides the analyst with information regarding the chemical heterogeneity of phases (e.g., chemical zoning within a mineral or mineral chemistry changing across an alteration zone). Spatial information permits a good understanding of heterogeneity within a phase and allows analytical artifacts to be easily identified. These methods of evaluation were tested on a complex geological sample. K-means clustering and K-nearest neighbor algorithms were used for phase classification, with the evaluation methods demonstrating their limitations.

iSpectra is an open source and system-independent toolbox for the analysis of spectral images (SIs) recorded on energy-dispersive spectroscopy (EDS) systems attached to scanning electron microscopes (SEMs). The aim of iSpectra is to assign pixels with similar spectral content to phases, accompanied by cumulative phase spectra with superior counting statistics for quantification. Pixel-to-phase assignment starts with a threshold-based pre-sorting of spectra to create groups of pixels with identical elemental budgets, similar to a method described by van Hoek (2014). Subsequent merging of groups and re-assignments of pixels using elemental or principle component histogram plots enables the user to generate chemically and texturally plausible phase maps. A variety of standard image processing algorithms can be applied to groups of pixels to optimize pixel-to-phase assignments, such as morphology operations to account for overlapping excitation volumes over pixels located at phase boundaries. iSpectra supports batch processing and allows pixel-to-phase assignments to be applied to an unlimited amount of SIs, thus enabling phase mapping of large area samples like petrographic thin sections.

As a consequence of substantial advances in computer-aided design and manufacturing technology, engineering parts are no longer restricted to combination of simple geometrical shapes. Implementing complex curved surfaces in engineering components in combination with finite-element geometry optimization has become a prevalent means of designing a part. Measuring residual stresses using X-ray diffraction (XRD) on complex curved surfaces requires further development of current measurement methods. Here we investigate how a laboratory XRD system equipped with a five-axis stage and two-dimensional detector can execute sin2ψ residual stress measurements on curved surfaces. Shadowing that blocks the diffracted beam to reach the detector was avoided using proper rotations and tilting of the sample. A standard video-laser alignment system commonly used to manually place the sample in the center of diffraction was used to also generate virtual maps of the sample's curved surfaces on a fine mesh grid. The geometry was then used for setting the required rotations and tilt angles. A set of diffraction frames collected using this method on a model zirconia dental ceramic, afforded the opportunity to superimpose phase and stresses on a complex geometry. This is a step forward for the XRD technology, and its usefulness applies to many different industries.