We present work on the application of sequential supervised machine learning for a reduced-dimension, ballistic deposition, Monte Carlo particle packing. Calculations are carried out for a combination of three distinguishable hard spheres representing different materials. Each set of spheres has a distribution of particle sizes in order to mimic realistic milling conditions of raw ingredients. Since

Laser-based powder bed fusion is a thermal metal additive manufacturing process suitable for the fabrication of complex structures. Laser-based powder bed fusion induces large localized thermal gradients and cooling rates, which can produce significant variation in mechanical properties by affecting the underlying microstructure and porosity. This research describes how thermal imaging data can be

Crystal plasticity simulation is an important tool for advanced Integrated Computational Materials Engineering for metals and alloys. The current work presents a calibration and validation framework for crystal plasticity finite element (CPFE) simulation of extension twinning in the Mg alloy WE43 using the scanning electron microscopy with digital image correlation (SEM-DIC) technique. Rolled Mg alloy

Genetic programming-based symbolic regression (GPSR) is a machine learning method which produces symbolic models that can be readily interpreted. This study utilized GPSR to derive uniaxial texture-based constitutive models for an additively manufactured alloy which were evaluated in post hoc analyses. Training data consisted of microscopy and mechanical testing data provided by the Air Force Research

This work presents a detailed instructional demonstration using the Rietveld refinement software MAUD for evaluating the crystallographic texture of single- and dual-phase materials, as applied to High-Pressure-Preferred-Orientation (HIPPO) neutron diffraction data obtained at Los Alamos National Laboratory (LANL) and electron backscatter diffraction (EBSD) pole figures on Ti–6Al–4V produced by additive

The knowledge of the distribution of local micromechanical fields is crucial in the design of composite materials. Traditionally full-field methods (such as finite element methods) and fast Fourier transformation-based methods are used to obtain the local fields. However, full-field simulations are computationally expensive and time-consuming. Recently, there has been a push toward using the big-data-driven

Additive manufacturing (AM) is a transformative technology to many industries that enables the fabrication of parts with complex geometries. A vast majority of powder-bed metal AM techniques use powder as feedstock. The powder packing behavior and flowability significantly influence the defect density of as-built parts and, eventually, affect their reliability and mechanical performance. The experimental

Design of additively manufactured metallic parts requires computational models that can predict the mechanical response of the parts considering the microstructural, manufacturing, and operating conditions. This article documents our response to Air Force Research Laboratory (AFRL) Additive Manufacturing Modeling Challenge 3, which asks the participants to predict the mechanical response of tensile

Post-heat treatment takes a key role for the enhancement of mechanical properties in selective laser melting (SLM) additive manufacturing (AM) of 6xxx series aluminum alloy. To understand the basic mechanism for changes of mechanical properties in the SLM process and the following post-heat treatment, the finite element model and the precipitate evolution (PE) model of SLM AM were established and combined

This work presents solutions to Challenge 2 within the Air Force Research Laboratory Materials Inform Data-Driven Design for AM Structures (MID3AS) AM (Additive Manufacturing) Challenge. The solutions are for quasi-steady-state and transient Nth-track cross-sectional predictions as a function of scan paths. Tracks are deposited successively to produce a pad and overlap each other; their cross section

The Air Force Research Laboratory Additive Manufacturing Modeling Series was executed to create calibration and validation data sets relevant to models of laser powder bed fusion-processed metallic materials. This article describes the data generated for the 2nd of 4 challenge questions which was specifically focused on microscale process-to-structure modeling needs. This work describes the experimental

The relative fatigue resistance of different polycrystalline microstructures can be evaluated using fatigue indicator parameters (FIPs) that serve as surrogate measures for the fatigue crack formation driving force. This typically requires simulating many grains/phases to capture sufficient microstructure heterogeneity. Thus, the concept of a representative volume element (RVE) for fatigue-related

We describe 3D characterization of an additively manufactured Inconel 625 nickel-base superalloy specimen conducted during a uniaxial tension test using a suite of nondestructive x-ray techniques. High-energy diffraction microscopy in both near- and far-field modalities are employed in situ to track evolution of the material orientation and stress–strain fields at six points during the mechanical test

Modern machine learning and autonomous experimentation schemes in materials science rely on accurate analysis of the data ingested by these models. Unfortunately, accurate analysis of the underlying data can be difficult, even for domain experts, complicating the training of the models intended to drive experiments. This is especially true when the goal is to identify the presence of weak signatures

Thermal distortion and residual stresses are two important factors that affect the quality and reliability of steel parts manufactured by laser powder bed fusion (LPBF) processes. A cost-effective model for evaluation of those heat effects is needed to refine the manufacturing process and provides insights into the product design and heat treatment. In this study, the layer heating method and sophisticated

The Air Force Research Laboratory Additive Manufacturing Modeling Challenge series launched in November 2019 was devised as a means to assess the Additive Manufacturing modeling community’s ability to predict, in a locally resolved fashion, details of the structure and subsequent mechanical performance of metallic materials fabricated with a laser powder bed fusion process. This communication gives

High-energy diffraction microscopy (HEDM) in-situ mechanical testing experiments offer unique insight into the evolving deformation state within polycrystalline materials. These experiments rely on a sophisticated analysis of the diffraction data to instantiate a 3D reconstruction of grains and other microstructural features associated with the test volume. For microstructures of engineering alloys

In the Air Force Research Laboratory Additive Manufacturing Challenge Series, melted track geometries for a laser powder bed fusion (L-PBF) process of Inconel 625 were used to challenge and validate computational models predicting melting and solidification behavior. The impact of process parameters upon single-track single-layer, multi-track single-layer, and single-track multi-layer L-PBF processes

A three-dimensional dataset of Inconel-625 generated as part of the Air Force Research Laboratory’s Additive Manufacturing Challenge Series is considered to interrogate modeling errors in a crystal-plasticity finite-element (CPFE) framework. Grain-average elastic strains from this framework are compared to measurements made with far-field high-energy X-ray diffraction microscopy (ff-HEDM) on a specimen

Growing demand in applications like portable electronics and electric vehicles calls for cost-effective, safe, and high-performance energy storage systems. Development of solid-state electrolytes with Li\(^{+}\) ionic conductivities comparable to those of the current liquid chemistries is an important step towards meeting these needs. Unfortunately, one of the most promising solid electrolytes known

In recent times, machine learning-based methods have gained popularity in various materials science applications including microstructure image classification. This paper explores the use of classifier combination approaches for classifying the microstructure images with an improved accuracy. Classifier combination methods have been recognized as a state-of-the-art approach to enhance the performance

The efficacy of an elasto-viscoplastic fast Fourier transform (EVPFFT) code was assessed based on blind predictions of micromechanical fields in a sample of Inconel 625 produced with additive manufacturing (AM) and experimentally characterized with high-energy X-ray diffraction microscopy during an in situ tensile test. The blind predictions were made in the context of Challenge 4 in the AFRL AM Modeling

Challenge 4 of the Air Force Research Laboratory additive manufacturing modeling challenge series asks the participants to predict the grain-average elastic strain tensors of a few specific challenge grains during tensile loading, based on experimental data and extensive characterization of an IN625 test specimen. In this article, we present our strategy and computational methods for tackling this

The details of polycrystalline microstructure often influence the early stages of yielding and strain localization under monotonic and cyclic loading, particularly in elastically anisotropic materials. A new software package, MechMet (mechanical metrics) provides a convenient finite element tool for solving field equations for elasticity in polycrystals in conjunction with investigations of microstructure-induced

The efficiencies of jet turbine engines are limited in part by the high-temperature properties of Ni-based superalloys utilized within turbine blades. Although Mo–Si–B alloys exhibit promising high-temperature properties, traditional materials development approaches relying extensively upon costly trial-and-error experiments inhibit the adoption rate of new materials. The present research seeks to

Unsupervised machine learning offers significant opportunities for extracting knowledge from unlabeled datasets and for achieving maximum machine learning performance. This paper demonstrates how to construct, use, and evaluate a high-performance unsupervised machine learning system for classifying images in a popular microstructural dataset. The Northeastern University Steel Surface Defects Database

Stitching partially overlapping image tiles into a montage is a common requirement for materials microscopy. We developed ITKMontage, a new module for the open-source Insight Toolkit (ITK), capable of robustly and quickly generating extremely large, high-bit-depth montages within the memory constraints of standard workstations. The phase correlation method is at the core of our pairwise tile registration

Sequences of projection images collected during in situ tomography experiments can capture the formation of patterns in crystallization and yield their three-dimensional growth morphologies. These image streams generate enormous and high dimensional datasets that span the full extent of a phase transition. Detecting from the continuous image stream the characteristic times and temperatures at which

This work presents a comparative assessment of different computation models with considering varying degrees of physics for the challenges within the National Institute of Standards and Technology (NIST) Additive Manufacturing benchmark problem AMB2018-02. The melt pool geometry, cooling rates, and dendritic microstructure of the single laser scan tracks on bare Inconel 625 plates are predicted by

Quantitative treatment of microstructure data is the first step in establishing the structure–property linkages using materials informatics. However, the microstructure data are often huge and require dimensionality reduction techniques to use it in a computationally meaningful way. In this paper, we present a simple and unique approach to estimate the intrinsic dimensionality of microstructure data

The present paper describes a probabilistic framework to predict the fatigue life and failure mode under various thermo-mechanical loading conditions. Specifically, inclusion- and matrix-driven competing failure modes are examined within nickel-based superalloys. The critical accumulated plastic strain energy density (APSED) is employed as a unified metric to predict fatigue crack initiation in metals

Besides chemical composition, microstructure plays a key role to control the properties of engineering materials. A strong correlation exists between microstructure and many mechanical and physical properties of a metal. It has the utmost importance to understand the microstructure and distinguish the microstructure accurately for the appropriate selection of engineering materials in product fabrication

Extracting accurate volume fraction and size measurements of γ″ and γ′ precipitates in iron-based superalloys from micrographs is challenging and conventionally involves manual image processing due to their smaller size, and similar crystal structures and chemistries. The co-precipitation of composite particles further complicates automated segmentation. In this work, different types of traditional

This paper describes a method combining in situ X-ray diffraction data and dimensionality reduction (local linear embedding) to inform the development of state variable plasticity models. The method is applied to developing a state variable plasticity model for pure nickel deformed in uniaxial tension in the small strain regime. Prior to model development, connections between state variables representing

Process-dependent residual stresses are one of the main burdens to a widespread adoption of laser powder bed fusion technology in industry. Residual stresses are directly influenced by process parameters, such as laser path, laser power, and speed. In this work, the influence of various scan speed and laser power control strategies on residual stresses is investigated. A set of nine different laser

The effective permittivity of composite materials depends highly on the geometry, the arrangement, and the permittivity of each component. This research proposes a new numerical method that takes into account those dependencies through assemblies of virtual capacitors (electrical circuit). Then, the effective permittivity is calculated from the equivalent capacity of the suggested circuits. The new

The digital transformation of manufacturing requires digitalization, including automatic and efficient data exchange. Model-based definitions (MBDs) capture digital product definitions, in order to eliminate error-prone information exchange associated with traditional paper-based drawings and to provide contextual information through additional metadata. The flow of MBDs extends throughout the product

Powder spreading precedes creation of every new layer in powder bed additive manufacturing (AM). The powder spreading process can lead to powder layer defects such as porosity, poor surface roughness and particle segregation. Therefore, the creation of homogeneous layers is the first task for optimal part printing. Discrete element methods (DEM) powder spreading simulations are typically limited to

Integrated computational materials engineering (ICME) facilitates efficient approaches to new material discovery and design, as well as optimization of existing materials. Computational models provide a way to rapidly screen candidate material designs such that materials can be tailored for specific applications in the product design cycle. Uncertainty is ubiquitous in ICME process–structure–property

Material extrusion (MatEx) is finding increasing applications in additive manufacturing of thermoplastics due to the ease of use and the ability to process disparate polymers. Since part strength is anisotropic and frequently deviates negatively with respect to parts produced by injection molding, an urgent challenge is to predict final properties of parts made through this method. A nascent effort

An important leap forward for the 3D community is the possibility to perform non-destructive 3D microstructural imaging in the home laboratories. This possibility is profiled by a recently developed technique—laboratory X-ray diffraction contrast tomography (LabDCT). As diffraction spots in LabDCT images are the basis for 3D reconstruction of microstructures, it is critical to get their identification

Predictions of the mechanical response of structural elements are conditioned by the accuracy of constitutive models used at the engineering length-scale. In this regard, a prospect of mechanistic crystal-plasticity-based constitutive models is that they could be used for extrapolation beyond regimes in which they are calibrated. However, their use for assessing the performance of a component is computationally

This paper investigates the role of material microstructures in structural analysis and establishes the need for microstructure-integrated constitutive models in predicting structural response. A focus is on microstructure and temperature dependency of stresses and plastic strains in a structural panel under realistic loading conditions. Structural analysis is conducted using the recently developed

Materials processing is a critical subset of manufacturing which is benefitting by implementing machine learning to create knowledge from the data mined/collected and gain a deeper understanding of manufacturing processes. In this study, we focus on aluminum high-pressure die-casting (HPDC) process, which constitutes over 60% of all cast Al components. Routinely collected process data over a year’s

An integrated computational materials engineering (ICME) methodology was applied in this study to systematically and quantitatively study the second phase dissolution kinetics during the solution treatment process of a high pressure die cast magnesium sample. The study was conducted on Mg–9 wt% Al, Mg–5 wt% Al, and Mg–11 wt% Al binary alloys after isothermal solution treatments ranging from 380 to

Integrated computational materials engineering (ICME)-based tools and techniques have been identified as the best path forward for distortion mitigation in thin-plate steel construction at shipyards. ICME tools require temperature-dependent material properties to achieve accurate computational results for distortion and residual stress. However, the required temperature-dependent material property

This paper demonstrates the feasibility of extracting quantitative linkages between optical micrographs and mechanical properties of cold-rolled HSLA (high-strength low alloy) steels measured in standardized tension tests. These linkages were established by bringing together modern toolsets for (i) image segmentation, (ii) rigorous statistical quantification of segmented microstructures, (iii) low-dimensional

An integrated computational materials engineering (ICME)-based workflow was adopted for the study of microstructure and property evolution at the heat-affected zone (HAZ) of gas metal arc-welded DP980 steel. The macroscale simulation of the welding process was performed with finite element method (FEM) implemented in Simufact Welding® software and was experimentally validated. The time–temperature

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

Readers should note the following error in this article.

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