In-Situ Visualization of Long-Range Defect Interactions at the Edge of Melting

Connecting a bulk materials microscopic defects to its macroscopic properties is an age-old problem in materials science. Long-range interactions between dislocations (line defects) are known to play a key role in how materials deform or melt, but we lack the tools to connect these dynamics to the macroscopic properties. We introduce time-resolved dark-field X-ray microscopy to directly visualize how dislocations move and interact over hundreds of micrometers, deep inside bulk aluminum. With real-time movies, we reveal the thermally-activated motion and interactions of dislocations that comprise a boundary, and show how weakened binding forces inhomogeneously destabilize the structure at 99% of the melting temperature. Connecting dynamics of the microstructure to its stability, we provide important opportunities to guide and validate multiscale models that are yet untested.

Methods to Quantify Dislocation Behavior with Dark-field X-ray Microscopy Timescans of Single-Crystal Aluminum

Crystal defects play a large role in how materials respond to their surroundings, yet there are many uncertainties in how extended defects form, move, and interact deep beneath a materials surface. A newly developed imaging diagnostic, dark-field X-ray microscopy (DFXM) can now visualize the behavior of line defects, known as dislocations, in materials under varying conditions. DFXM images visualize dislocations by imaging the very subtle long-range distortions in the materials crystal lattice, which produce a characteristic adjoined pair of bright and dark regions. Full analysis of how these dislocations evolve can be used to refine material models, however, it requires quantitative characterization of the statistics of their shape, position and motion. In this paper, we present a semi-automated approach to effectively isolate, track, and quantify the behavior of dislocations as composite objects. This analysis drives the statistical characterization of the defects, to include dislocation velocity and orientation in the crystal, for example, and is demonstrated on DFXM images measuring the evolution of defects at 98$%$ of the melting temperature for single-crystal aluminum, collected at the European Synchrotron Radiation Facility.

Machine Learning to Analyze Images of Shocked Materials for Precise and Accurate Measurements

A supervised machine learning algorithm, called locally adaptive discriminant analysis (LADA), has been developed to locate boundaries between identifiable image features that have varying intensities. LADA is an adaptation of image segmentation, which includes techniques that find the positions of image features (classes) using statistical intensity distributions for each class in the image. In order to place a pixel in the proper class, LADA considers the intensity at that pixel and the distribution of intensities in local (nearby) pixels. This paper presents the use of LADA to provide, with statistical uncertainties, the positions and shapes of features within ultrafast images of shock waves. We demonstrate the ability to locate image features including crystals, density changes associated with shock waves, and material jetting caused by shock waves. This algorithm can analyze images that exhibit a wide range of physical phenomena because it does not rely on comparison to a model. LADA enables analysis of images from shock physics with statistical rigor independent of underlying models or simulations

A Locally Adapting Technique for Boundary Detection using Image Segmentation

Rapid growth in the field of quantitative digital image analysis is paving the way for researchers to make precise measurements about objects in an image. To compute quantities from the image such as the density of compressed materials or the velocity of a shockwave, we must determine object boundaries. Images containing regions that each have a spatial trend in intensity are of particular interest. We present a supervised image segmentation method that incorporates spatial information to locate boundaries between regions with overlapping intensity histograms. The segmentation of a pixel is determined by comparing its intensity to distributions from local, nearby pixel intensities. Because of the statistical nature of the algorithm, we use maximum likelihood estimation theory to quantify uncertainty about each boundary. We demonstrate the success of this algorithm on a radiograph of a multicomponent cylinder and on an optical image of a laser-induced shockwave, and we provide final boundary locations with associated bands of uncertainty.