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

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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

In this study, anisotropic ductility and associated damage mechanisms of a grade X100 line pipe steel previously studied at the macroscopic scale were investigated using in situ synchrotron radiation computed tomography of notched round bars. Line pipe materials have anisotropic mechanical properties, such as tensile strength, ductility and toughness. Specimens were tested for loading along both rolling

We introduce a novel methodology, based on in situ X-ray tomography measurements, to quantify and analyze 3D crack morphologies in biological cellular materials during damage process. Damage characterization in cellular materials is challenging due to the difficulty of identifying and registering cracks from the complicated 3D network structure. In this paper, we develop a pipeline of computer vision

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

The accuracy of the stresses predicted from crystal plasticity-based finite element formulation depends on estimation and control of the errors associated with the discretization. In the current work, the errors in the stress distribution are estimated in virtual polycrystalline samples of α-phase titanium (hexagonal close-packed phase of Ti–6Al–4V). To estimate the error, the stress field, which does

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

Accurate creep life prediction is necessary for the evaluation of the remaining creep lives of in-service turbine blades and for the design of new turbine blades in aircraft engines. In this study, an integrated computational material engineering methodology for predicting the remaining creep life of in-service turbine blades was developed by taking a microstructural criterion and creep strain criterion

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

Additive manufacturing (AM) combines all of the complexities of materials processing and manufacturing into a single process. The digital revolution made this combination possible, but the commercial viability of these technologies for critical parts may depend on digital process simulations to guide process development, product design, and part qualification. For laser powder bed fusion, one must

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

PSICHE is a high-energy, multi-technique beamline at the SOLEIL synchrotron facility. It performs X-ray tomography for materials science and other applications and X-ray diffraction for samples at extreme conditions. The beamline has been in service for user experiments since 2013, but is in continual development to add new capabilities. In this article, we present a series of new developments which

Integrated Computational Materials Engineering (ICME) is an engineering approach where the materials, manufacturing process, and component designs are optimized concurrently before an actual physical component is realized. This requires the integration of models across vast length and timescales. A key benefit of ICME is the ability to reduce the bulk of expensive and lengthy experiments via tailored

High-energy X-ray diffraction (HEXD) has become a powerful technique for studying deformation and failure in structural metals over the last two decades. In this work, we used multi-grain HEXD to investigate hydrogen embrittlement in polycrystalline nickel by quantifying the effects of solute hydrogen on the grain-scale plastic response. Five polycrystalline samples were probed: one undeformed control

The present study aims at demonstrating the feasibility of performing a fracture test in less than 1 min in a laboratory CT scanner despite the severe time constraints of tomography acquisition. After introducing the basic concepts of projection-based digital volume correlation, the specific implementation of this methodology to a wedge splitting test on a refractory material is presented. The kinematics

Thermal barrier coatings (TBC) are multilayered systems comprising a metallic oxidation protection layer or so-called bond coat, a ceramic topcoat, and a thermally grown aluminum oxide developing at the interface. The coating systems fail typically by delamination cracking near this interface, which has a complex three-dimensional morphology influencing the crack path. This study combines laser shock

A proper understanding of the structure and microstructure of additively manufactured (AM) alloys is essential not only to the prediction and assessment of their material properties, but also to the validation and verification of computer models needed to advance AM technologies. To accelerate AM development, as part of the AM-Bench effort, we conducted rigorous synchrotron-based X-ray scattering and

Laser sintering (LS) of polyamide 12 (PA12) is increasingly being adopted for industrial production of end-use parts, yet the complexity of this process coupled with the lack of organized, rigorous, publicly available process-structure-physical property datasets exposes manufacturers and customers to risks of unacceptably poor part quality and high costs. Although an extensive scientific literature

One of the primary barriers for adoption of additive manufacturing (AM) has been the uncertainty in the performance of AM parts due to residual stresses/strains. The rapid heating and cooling rates from the thermal history of the laser melting process result in high residual stresses/strains that produce significant part distortion. Efforts to mitigate residual stresses using post-process heat treatments

Direct imaging of three-dimensional microstructure via X-ray diffraction-based techniques gives valuable insight into the crystallographic features that influence materials properties and performance. For instance, X-ray diffraction tomography provides information on grain orientation, position, size, and shape in a bulk specimen. As such techniques become more accessible to researchers, demands are

This work presents a comparison of simulation results with the experimental data for four of the six challenges within the National Institute of Standards and Technology (NIST) Additive Manufacturing (AM) Benchmark Test Series (AM Bench) problem AMB2018-02. This comparison is akin to a test case to assess the technology maturity level (TML) for the AM predictive capabilities that can be utilized to

The laser powder bed fusion (LPBF) process involves using a laser beam to selectively melt metal powder with a desired shape on a substrate to create a part layer-by-layer. As an Additive Manufacturing (AM) process, laser powder bed fusion (commonly referred to as selective laser melting—SLM) offers superior design freedom over conventional manufacturing methods and enables the production of complex

A new mechanical stage to perform in situ 3D imaging using synchrotron X-ray tomography is presented. Pairing control and acquisition allows the running of high quality continuous mechanical tests to study damage and fracture in any kind of structural materials. The modular design make this device very versatile with the possibility to use many specimen geometries and load ranges up to 5 kN, and switch

Mechanical properties exhibited by the materials used in biomedical device components for articulating joints play an important role in determining the implant performance. In the fabrication of complex-shaped parts, the thermomechanical history experienced in different locations of the final part can be substantially dissimilar, which may lead to large differences in the local microstructures and

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

The present article describes design, architecture, and implementation of the Aachen (“Aix”) Virtual Platform for Materials Processing—AixViPMaP®. This simulation platform focuses on enabling automatic simulation workflows in the area of microstructure evolution and microstructure property relationships by continuum models. Following a description of a variety AixViPMaP® functionalities like user management

Facilitating integrated computational materials engineering (ICME) in the digitized world necessitates facilitating a network of participants (material scientists, systems designers, software developers, service customers) to share material/product/manufacturing process/market data, information, knowledge, and resources instantly and collaborate so as to facilitate a cost-effective co-creation of value

The development and use cases of an open-source filter for DREAM.3D that instantiates synthetic, grain-resolved, open-cell metal foam volumes are presented. The new capability allows for both synthetic-grain overlay of X-ray computed tomography data as well as fully synthetic foam geometry and grains. For the latter, a novel technique using Euclidean distances instantiates the 3D open-cell foam morphology

Process-property relations are central to ICME. Engineers are often interested in using these relations to make decisions on process configurations to achieve desired properties. This is known as the inverse problem and is typically solved using forward models (physics-based or data-based) in an optimization loop, which can sometimes be expensive and error prone, especially when used on process chains

Dictionary indexing of electron back-scatter patterns was recently proposed as an alternative to the commercially available indexing packages. In this tutorial paper, we describe in detail the various steps that need to be taken to successfully complete an indexing run on an arbitrary data set. We provide three toy data sets for the reader to experiment with: poly-crystalline nickel with several different

Laboratory diffraction contrast tomography (LabDCT) enables the user to reconstruct three-dimensional (3D) grain maps of polycrystalline materials. For each grain, the size, orientation, and 3D morphology including the number of faces can be derived. Since the technique is non-destructive, LabDCT opens up new possibilities for studies of microstructural evolution at the level of individual grains.

In this contribution, we validate a physical model based on a transient temperature equation (including latent heat), w.r.t. the experimental set AMB2018-02 provided within the additive manufacturing benchmark series, established at the National Institute of Standards and Technology, USA. We aim at predicting the following quantities of interest, width, depth, and length of the melt pool by numerical

The prediction of solidification microstructures associated with additive manufacture of metallic components is fundamental in the identification scanning strategies, process parameters and subsequent heat treatments for optimised component properties. Interactions between the powder particles and the laser heat source result in complex thermal fields in and around the metal melt pool, which will influence

Laser powder bed fusion (LPBF) is an additive manufacturing process with many adjustable input parameters that directly affect manufacturability and quality of the final product. The selection of the optimal input parameters makes the process qualification and part certification a costly and time-consuming task if performed using the traditional sequential and empirical approach.Within the scope of

In the NIST additive manufacturing benchmark (AM-Bench) experiments, melt pool geometry, cooling rates, surface topography, and dendritic microstructure in laser melted Inconel 625 were used to challenge and validate computational models of the melting and solidification process. To this end, three thermal models incorporating different physics are compared with the experimental data. It is identified

We present a meshfree direct numerical simulation (DNS) capability for the additive manufacturing (AM) process of metals based on the hot optimal transportation meshfree (HOTM) method. The HOTM method is a meshfree thermomechanical Lagrangian computational framework for material behaviors under extreme thermomechanical loading conditions. It combines the optimal transportation meshfree (OTM) method

Reduced-order structure–property (S-P) linkages play a pivotal role in the tailored design of materials for advanced engineering components. There is a critical need to distill these from the simulation datasets aggregated using sophisticated, computationally expensive, physics-based numerical tools (e.g., finite element methods). The recent emergence of materials data science approaches has opened

A microstructure analytics and 3D reconstruction software package, DREAM.3D, was integrated as a module into a cloud-based platform, BisQue. Parallelization of DREAM.3D module executions and the ability to parameterize pipeline variables over a range of values have led to insights about the grain segmentation misorientation tolerance in TriBeam-collected 3D EBSD datasets of additively manufactured

Accelerating the design and development of new advanced materials is one of the priorities in modern materials science. These efforts are critically dependent on the development of comprehensive materials cyberinfrastructures which enable efficient data storage, management, sharing, and collaboration as well as integration of computational tools that help establish processing–structure–property relationships