Brian L. DeCost

UHCSDB: UltraHigh Carbon Steel Micrograph DataBase

We present a new microstructure dataset consisting of ultrahigh carbon steel (UHCS) micrographs taken over a range of length scales under systematically varied heat treatments. Using the UHCS dataset as a case study, we develop a set of visualization tools for interacting with and exploring large microstructure and metadata datasets. Based on generic microstructure representations adapted from the field of computer vision, these tools enable image-based microstructure retrieval, as well as spatial maps of both microstructure and related metadata, such as processing conditions or properties measurements. We provide the microstructure image data, processing metadata, and source code for these microstructure exploration tools. The UHCS dataset is intended as a community resource for development and evaluation of microstructure data science techniques and for creation of microstructure data science teaching modules.

Elucidating multi-physics interactions in suspensions for the design of polymeric dispersants: a hierarchical machine learning approach

A computational method for understanding and optimizing the properties of complex physical systems is presented using polymeric dispersants as an example. Concentrated suspensions are formulated with dispersants to tune rheological parameters, such as yield stress or viscosity, but their competing effects on solution and particle variables have made it impossible to design them based on our knowledge of the interplay of chemistry and function. Here, physical and statistical modeling are integrated into a hierarchical framework of machine learning that provides insight into sparse experimental datasets. A library of 10 polymers having similar molecular weight but incorporating different functional groups commonly found in aqueous dispersants was used as a training set in magnesium oxide slurries. The compositions of these polymers were the experimental variables that determined the complex system responses, but the method leverages knowledge of the constituent “single-physics” interactions that underlie the suspension properties. Integration of domain knowledge is shown to allow robust predictions based on orders of magnitude fewer samples in the training set compared with purely statistical methods that directly correlate dispersant chemistry with changes in rheological properties. Minimization of the resulting function for slurry yield stress resulted in the prediction of a novel dispersant that was synthesized and shown to impart similar reductions as a leading commercial dispersant but with a significantly different composition and molecular architecture.

Phenomenology of Abnormal Grain Growth in Systems with Nonuniform Grain Boundary Mobility

We have investigated the potential for nonuniform grain boundary mobility to act as a persistence mechanism for abnormal grain growth (AGG) using Monte Carlo Potts model simulations. The model system consists of a single initially large candidate grain embedded in a matrix of equiaxed grains, corresponding to the abnormal growth regime before impingement occurs. We assign a mobility advantage to grain boundaries between the candidate grain and a randomly selected subset of the matrix grains. We observe AGG in systems with physically reasonable fractions of fast boundaries; the probability of abnormal growth increases as the density of fast boundaries increases. This abnormal growth occurs by a series of fast, localized growth events that counteract the tendency of abnormally large grains to grow more slowly than the surrounding matrix grains. Resulting abnormal grains are morphologically similar to experimentally observed abnormal grains.

A large dataset of synthetic SEM images of powder materials and their ground truth 3D structures.

This data article presents a data set comprised of 2048 synthetic scanning electron microscope (SEM) images of powder materials and descriptions of the corresponding 3D structures that they represent. These images were created using open source rendering software, and the generating scripts are included with the data set. Eight particle size distributions are represented with 256 independent images from each. The particle size distributions are relatively similar to each other, so that the dataset offers a useful benchmark to assess the fidelity of image analysis techniques. The characteristics of the PSDs and the resulting images are described and analyzed in more detail in the research article “Characterizing powder materials using keypoint-based computer vision methods” (B.L. DeCost, E.A. Holm, 2016) [1]. These data are freely available in a Mendeley Data archive “A large dataset of synthetic SEM images of powder materials and their ground truth 3D structures” (B.L. DeCost, E.A. Holm, 2016) located at http://dx.doi.org/10.17632/tj4syyj9mr.1[2] for any academic, educational, or research purposes.

Corrigendum to “A large dataset of synthetic SEM images of powder materials and their ground truth 3D structures” [Data Brief 9 (2016) 727–731]

[This corrects the article DOI: 10.1016/j.dib.2016.10.011.].