Rapid characterization of microstructural properties such as phase, crystallographic texture, and grain size distribution is critical to accelerating the discovery and development of new materials and processing routes. Our goal in this work is to increase the throughput of microstructure characterization by transmission high-energy x-ray diffraction (XRD). To reduce the time required for XRD data collection and analysis, we have developed a suite of easily-calculated similarity metrics that reveal differences in microstructure between samples or between points on a graded sample. Each metric is designed to be most sensitive to a different aspect of the microstructure, which allows us to gain a deeper understanding of the differences between samples than would be possible with a single, global similarity metric. Using only a single 2D diffraction image eliminates the need to rotate the sample for texture measurements, which vastly increases the speed of data collection compared to traditional texture measurements. The simplicity of the metrics enables real-time analysis of the data.
To systematically explore the impact of different aspects of microstructure on our similarity metrics, we calculated diffraction patterns from virtual microstructures in which each characteristic was varied. This enables us to measure the accuracy of each metric to its corresponding property as well as the effect of changes in non-target characteristics, e.g. the sensitivity of the lattice parameter metric to changes in the texture. We conclude by presenting the results of applying the metrics to real data from a rolled aluminum 7085 sample with spatial variations in the microstructure to show how our methods integrate into a fully automated x-ray system.