Many of the machine learning-based approaches for materials property predictions use low-cost computational data. The motivation for machine learning models is based on the orders of magnitude speedup compared to DFT calculations or experimental characterization. High-quality experimental materials data would be ideal for training these models; unfortunately, experimental data are typically costly

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Several challenges are encountered in the development of maraging steels with desired combinations of mechanical properties. These include the need to explore a large process design space, the time- and effort-consuming standardized testing protocols for property evaluations, and the lack of a formal design of experiments strategy that guides the selection of the process conditions for the next set

A MultiStage Fatigue (MSF) model that admits different hierarchical microstructural features and their stereological information is used to predict the fatigue behavior of 17 different processed aluminum alloys: A000 series (A319, A356, A357, and A380), 2000 series (2024, 2055, 2099, 2198, 2297), 5000 series (5052, 5456), and 7000 series (7050, 7055, 7065, 7075, 7085, 7175). A single set of MSF model

With the advent of integrated computational materials engineering, there is a drive to exchange statistical confidence in a design obtained from repeated experimentation with one developed through modeling and simulation. Since these models are often missing physics or include incomplete knowledge or simplifying assumptions into their mathematical construct, they may not capture the physical system

Materials informatics is increasingly finding ways to exploit machine learning algorithms. Techniques such as decision trees, ensemble methods, support vector machines, and a variety of neural network architectures are used to predict likely material characteristics and property values. Supplemented with laboratory synthesis, applications of machine learning to compound discovery and characterization

Additive manufacturing (AM) of alloys creates segregated microstructures with significant differences from those of traditional wrought alloys. Understanding how the local build conditions generate specific microstructures is essential for developing post-build heat treatments with the goal of producing parts with reliable and predictable properties. This research examines the position- and orientation-dependent

Materials researchers are paying ever more attention to sustainability, criticality, availability, and other industrial ecology metrics and concepts as they develop new materials. Previous reports for these metrics have typically been either for a few specific compositions or for a single year. In this work, we present a new curated dataset which reports the global elemental production on a per country

This work provides results and analysis of the in situ thermal measurement acquired during the 3D builds performed for the 2018 additive manufacturing benchmark tests. The objective is to provide context for post-process characterization of distortion, residual strain, and microstructure, which are reported elsewhere in this Journal, and to provide validation data for thermal models of the build process

The article “PolyProc: A Modular Processing Pipeline for X-ray Diffraction Tomography” written by Jiwoong Kang, Ning Lu, Issac Loo, Nancy Senabulya, and Ashwin J. Shahani, was originally published Online First without Open Access.

The Additive Manufacturing Benchmark (AM-Bench) test series was established to provide rigorous measurement test data for validating additive manufacturing (AM) simulations for a broad range of AM technologies and material systems. AM-Bench includes extensive in situ and ex situ measurements, simulation challenges for the AM modeling community, and a corresponding conference series. In 2018, the first

The complex physical nature of the laser powder bed fusion (LPBF) process warrants use of multiphysics computational simulations to predict or design optimal operating parameters or resultant part qualities such as microstructure or defect concentration. Many of these simulations rely on tuning based on characteristics of the laser-induced melt pool, such as the melt pool geometry (length, width, and

Significant advances in theory, simulation tools, advanced computing infrastructure, and experimental frameworks have enabled the field of materials science to become increasingly reliant on computer simulations. Theory-grounded computational models provide a better understanding of observed materials phenomena. At the same time, computational tools constitute an important ingredient of any framework

This work introduces a methodology to assess data quality for the tensile, creep/stress relaxation, and fatigue properties of alloys (as well as metadata associated with manufacture) as a part of a project to develop new materials for extreme environments. The extreme environments in question deal with those found in the power generation sector. Data quality assessment is needed to ensure the reliability

Segmentation of microscopy images is an essential step in most experimental studies of process–structure–property relationships in advanced materials. Currently employed segmentation approaches require the user to identify and string together a sequence of algorithms (and codes) into customized workflows that need extensive tweaking and optimization (often accomplished through repeated trials) for

Successful deployment of the highly heterogeneous, laminated, polymer matrix composites (PMCs) in high-performance structural applications is currently hindered by the lack of reliable experimental protocols for evaluation of the local mechanical responses at the salient meso-length/structure scales present in these material systems. Our main interest in this paper lies in establishing and demonstrating

Characterizing a material’s microstructure, especially as it relates to the manufacturing processes used to fabricate it, is of great interest to engineers and researchers. In recent years, state-of-the-art imaging techniques have been able to yield a plethora of high resolution 3D data that can be used to study materials at various length scales. This 3D data is usually organized as stacked serial

A systems approach within an Integrated Computational Materials Engineering framework was used to design three new low-cost seamless replacement coinage alloys to reduce the raw material of the current US coinage alloys. Maintaining compatibility with current coinage materials required matching the currently used alloy properties of yield strength, work-hardening behavior, electrical conductivity,

There is a critical need for customized analytics that take into account the stochastic nature of the internal structure of materials at multiple length scales in order to extract relevant and transferable knowledge. Data driven Process-Structure-Property (PSP) linkages provide systemic, modular and hierarchical framework for community driven curation of materials knowledge, and its transference to

We present a case study in which we use natural language processing and machine learning techniques to automatically select candidate scientific articles that may contain new experimental thermophysical property data from thousands of articles available in five different relevant journals. The National Institute of Standards and Technology (NIST) Thermodynamic Research Center (TRC) maintains a large

There has been a growing recognition of the opportunities afforded by advanced data science and informatics approaches in addressing the computational demands of modeling and simulation of multiscale materials science phenomena. More specifically, the mining of microstructure-property relationships by various methods in machine learning and data mining opens exciting new opportunities that can potentially

Large and highly textured regions, referred to as macrozones or microtextured regions, with sizes up to several orders of magnitude larger than those of the individual grains, are found in dual-phase titanium alloys as a consequence of the manufacturing process route. These macrozones have been shown to play a critical role in the failure of titanium alloys, specifically being linked to crack initiation

Materials data management, analytics, and e-collaborations have been identified as three of the main technological gaps currently hindering the realization of the accelerated development and deployment of advanced materials targeted by the federal materials genome initiative. In this paper, we present our ongoing efforts aimed at addressing these critical gaps through the customized design and build

We present the original results pertaining to understanding topographical features on real industrial alloy surfaces. Quite often, such materials are subjected to different thermomechanical treatments with the goal of improving their yield strength, ductility, corrosion resistance, and other properties. Operations of casting, rolling, extruding, and stretching metal invariably leave their distinct