With the rising importance of aluminum sheets for automotive applications, the influence of microstructure and texture on mechanical properties and on forming behavior has gained re-increased interest in recent years. This paper provides an introduction to the topic and demonstrates the evolution of microstructure and texture in the standard alloys EN AW-5182 and EN AW-6016 for different processing scales. Moreover, strategies for texture and microstructure characterization of automotive Al-sheets are discussed. As the development of alloys or processes usually starts in laboratory facilities, the transferability to the industrial scale of the results thereof is studied. A detailed analysis of the entire processing chain shows good conformity of careful laboratory production with the industrial production concerning microstructure as well as qualitative and quantitative texture evolution for EN AW-5182. While comparable grain sizes can be achieved in final annealed sheets of EN AW-6016, quantitative discrepancies in texture occur between the different production scales for some sample states. The results are discussed in light of the basics of plasticity and recrystallisation including the effect of solutes, primary phases, and secondary phases in the alloys.

We performed deformation and grain growth experiments on natural olivine aggregates with olivine water contents (COH = 600 ± 300 H/106 Si) similar to upper mantle olivine, at 1000–1200°C and 1,400 ± 100 MPa confining pressure. Our experiments differ from published grain growth studies in that most were (1) conducted on natural olivine cores rather than hot‐pressed aggregates and (2) dynamically recrystallized prior to or during grain growth. We combine our results with similar experiments performed at 1200–1300°C and fit the data to a grain growth relationship, yielding a growth exponent (p) of 3.2, activation energy (EG) 620 ± 145 kJ mol−1 (570 ± 145 kJ mol−1 when accounting for the role of temperature on water content), activation volume (VG) ~5 × 10−6 m3 mol−1, and rate constant (k0) 1.8 × 103 mp s−1. Our EG is within uncertainty of that predicted for dislocation creep of wet olivine (E* = 480 ± 40 kJ mol−1). Grain size in strain rate‐stepping samples adjusted to the olivine piezometer within 1.3–7.9% strain. The active grain boundary migration processes during deformation and dynamic recrystallization affect the kinetics of postdeformation grain growth, as grain boundary migration driven by strain energy density (ρGBM) may delay the onset of grain growth driven by interfacial energy (γGBM). We compared our postdeformation grain growth rates with data from previously published hydrostatic annealing experiments on synthetic olivine. At geologic timescales, the growth rates are much slower than predicted by the existing wet olivine grain growth law.

We conducted high‐temperature and high-pressure deformation experiments on hot-pressed aggregates of olivine + 9 wt% dolomite at 1 GPa and 1100oC in a Griggs-type apparatus. At run conditions,...

Volumetric crystal structure indexing and orientation mapping are key data processing steps for virtually any quantitative study of spatial correlations between the local chemistry and the microstructure of a material. For electron and X-ray diffraction methods it is possible to develop indexing tools which compare measured and analytically computed patterns to decode the structure and relative orientation within local regions of interest. Consequently, a number of numerically efficient and automated software tools exist to solve the above characterisation tasks. For atom probe tomography (APT) experiments, however, the strategy of making comparisons between measured and analytically computed patterns is less robust because many APT datasets may contain substantial noise. Given that general enough predictive models for such noise remain elusive, crystallography tools for APT face several limitations: Their robustness to noise, and therefore, their capability to identify and distinguish different crystal structures and orientation is limited. In addition, the tools are sequential and demand substantial manual interaction. In combination, this makes robust uncertainty quantifying with automated high-throughput studies of the latent crystallographic information a difficult task with APT data. To improve the situation, we review the existent methods and discuss how they link to those in the diffraction communities. With this we modify some of the APT methods to yield more robust descriptors of the atomic arrangement. We report how this enables the development of an open-source software tool for strong-scaling and automated identifying of crystal structure and mapping crystal orientation in nanocrystalline APT datasets with multiple phases.

Twinning in hexagonal close-packed (hcp) metals is a multi-scale process that depends on the microstructural and mechanical response details at the polycrystalline aggregate, grain, micro, and atomic scales. Twinning can generally be regarded as a two-step process, a nucleation event followed by propagation and growth. This articles presents a stochastic model for the nucleation of deformation twins in hcp polycrystals. Twin nucleation is mod- eled through its dependence on lower length scale material details, such as the defect con- figurations at potential nucleation sites within grain boundaries, and mechanical details such as highly localized stress concentrations at the microscale in a probabilistic manner. These two aspects, the material and mechanical, must align for a successful nucleation event. The nucleation process is cast as a survival model parameterized by the local stress at the grain boundary. The model gives an explicit form for the probability distribution for the critical stress values required for twin nucleation. The model is implemented into a viscoplastic self-consistent (VPSC) crystal plasticity framework in order to test its predic- tive capability against previously reported statistical characterization in deformed zirco- nium at multiple temperatures. For implementation in VPSC, the stress concentrations are sampled from a distribution calibrated to full-field crystal plasticity simulations and a three-dimensional model of grain neighbors and distribution of grain boundary areas are implemented.

This study concentrates on the development of experimental techniques that are of benefit to research into high strain rate fracture and fragmentation. Two main areas are pursued, namely the effect of the stress state in the sample and the initial temperature of the sample on the resulting fracture mechanism and fragmentation behaviour when under tensile loading at strain rates of 10 s−1. Both areas use expanding rings and cylinders to achieve this. Experiments are designed and fielded on explosively loaded Ti-6Al-4V rings where the aspect ratio (sample wall thickness to height) is adjusted to create stress states ranging from uniaxial stress to plane strain with velocimetry and fragment recovery used to measure the expansion and failure processes. A transition to necking before failure under uniaxial stress was observed, as opposed to ductile tearing under shear loading in plane strain conditions. Intermediate geometries were found to undergo massive internal damage not seen in the other experiments, leading to premature failure and smaller fragments. Temperature dependence was investigated using a new gas gun driven expanding cylinder technique with Ti-6Al-4V cylinders 150 mm long, 50 mm inner diameter and 4 mm wall thickness reaching temperatures between 150 K and 800 K before expansion. The loading mechanism was found to be highly repeatable and independent of sample temperature, providing a robust platform for generating high strain rate tensile and failure test data at temperatures unobtainable by other means. A full suite of velocimetry, high speed imaging, fragment recovery and microscopy techniques were used to fully characterise the material during and after deformation. At elevated temperatures adiabatic shear banding was found to be an active failure mechanism. The fragmentation toughness parameter Kf was found to be 101 ± 13 MPa m under these conditions.

The variant graph is a new, hybrid algorithm that combines the strengths of established global grain graph and local neighbor level voting approaches, while alleviating their shortcomings, to reconstruct parent grains from orientation maps of partially or fully phase-transformed microstructures. The variant graph algorithm is versatile and is capable of reconstructing transformation microstructures from any parent-child combination by clustering together child grains based on a common parent orientation variant. The main advantage of the variant graph over the grain graph is its inherent ability to more accurately detect prior austenite grain boundaries. A critical examination of Markovian clustering and neighbor level voting as methods to reconstruct prior austenite orientations is first conducted. Following this, the performance of the variant graph algorithm is showcased by reconstructing the prior austenite grains and boundaries from an example low-carbon lath martensite steel microstructure. Programmatic extensions to the variant graph algorithm for specific morphological conditions and the merging of variants with small mutual disorientation angles are also proposed. The accuracy of the reconstruction and the computational performance of the variant graph algorithm is either on-par or outperforms alternate methods for parent grain reconstruction. The variant graph algorithm is implemented as a new addition to the functionalities for phase transformation analysis in MTEX 5.8 and is freely available for download by the community.

The quaternion Bingham distribution has been used to model preferred crystallographic orientation, or crystallographic texture, in polycrystalline materials in the materials science and geological communities. A primary difficulty in applying the Bingham distribution has been the lack of an efficient method for fitting the distribution parameters with respect to the materials underlying crystallographic symmetry or any statistical sample symmetry due to processing. In this paper we present a symmetrized distribution, based on the quaternion Bingham, which can account for any general combination of crystallographic or sample symmetries. We also introduce a numerical scheme for estimating the parameters of the symmetrized distribution based on the well known expectation maximization algorithm.

The process chain of non-oriented silicon based electrical steels consists of continuous casting, hot rolling with considerate heat treatment followed by cold rolling and final heat treatment. Every step in this process chain has an influence on the microstructure development like texture and grain size, which in turn influence the desired electromagnetic properties (low iron losses and good magnetic flux densities). Texture evolution during cold rolling plays a major role on the recrystallization (RX) characteristics (RX texture and grain growth) in the final heat treatment. To optimize the texture evolution in cold rolling in order to obtain better electromagnetic properties a verified simulation model is necessary. For this purpose, hot band strips are cold rolled in three consecutive passes for 50 % height reduction. After each pass macro texture was measured to evaluate the texture evolution. This multi pass rolling process is simulated in a top-down multiscale modelling approach where elasto-plastic based FEM simulations of cold rolling are performed to obtain the history of the deformation gradient at the centre of the workpiece. This history variable is then imposed on a polycrystal Representative Volume Element (RVE) using a phenomenological Crystal Plasticity FEM (CPFEM) material model developed in DAMASK crystal plasticity tool. The influence of the RVE dimension on the predicted texture evolution is analysed by the comparison of a 2D RVE simulation to a 3D RVE simulation. Thereafter, the fibre texture intensities from both models are compared with the experimental results. The 3D RVE is able to predict the experimentally measured texture evolution more precisely and thus overcoming the limitation of a 2D RVE. A. Vuppala, X. Wei, S. Hojda, M. Teller and G. Hirt

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, the currently implemented software tools, simulation workflows, data storage, grid infrastructure, and many more, some example workflows which have been run on AixViPMaP® are presented in detail. These workflow examples—although each being specific—can readily be transferred to other materials or to similar processes as the major simulation tools used in these workflows are all generic and thus applicable to a wide range of metals and technical alloys. The article concludes with a discussion on the performance and benefits of the platform, an outlook on its future development and on its open, future availability for both academic and commercial use.

The possibility to characterize in an automatized way the spatial-temporal evolution of individual grains and their properties is essential to the understanding of annealing phenomena. The development of advanced experimental techniques, computational models and tools helps the acquisition of real time and real space-resolved datasets. Whereas the reconstruction of 3D grain representatives from serial-sectioning or tomography datasets becomes more common and microstructure simulations on parallel computers become ever larger and longer lasting, few efforts have materialized in the development of tools that allow the continuous tracking of properties at the grain scale. In fact, such analyses are often left neglected in practice due to the large size of the datasets that exceed the available physical memory of a computer or the shared-memory cluster. We identified the key tasks that have to be solved in order to define suitable and lean data structures and computational methods to evaluate spatio-temporal grain property datasets by working with parallel computer architectures. This is exemplified with data from grain growth simulations.

The design and content of MSAT, a new Matlab toolkit for the study and analysis of seismic and elastic anisotropy, is described. Along with a brief introduction to the basic theory of anisotropic elasticity and a guide to the functions provided by the toolkit, three example applications are discussed. First, the toolkit is used to analyse the effect of pressure on the elasticity of the monoclinic upper mantle mineral diopside. Second, the degree to which a model of elasticity in the lowermost mantle can be approximated by transverse isotropy is examined. Finally backazimuthal variation in the effective shear wave splitting caused by two anisotropic layers where the lower layer is dipping is calculated. MSAT can be freely reused for any purpose and the implementation of these and other examples are distributed with the source code.

The John Ruskin's 19th century adage suggests that personal taste is not merely an absolute set of aesthetic principles valid for everyone: actually, it is a process of interpretation which have also roots in one's life experiences. This aspect represents nowadays a major problem for inferring automatically the quality of a picture. In this paper, instead of trying to solve this age-old problem, we consider an intriguing, orthogonal direction, aimed at discovering how different are the personal tastes. Given a set of preferred images of a user, obtained from Flickr, we extract a pool of low- and high-level features; LASSO regression is then exploited to learn the most discriminative ones, considering a group of 200 random Flickr users. Such aspects can be easily recovered, allowing to understand what is the "what we like" which distinguish us from the others. We then perform multi-class classification, where a test sample is a set of preferred pictures of an unknown user, and the classes are all the users. The results are surprising: given only 1 image as test, we can match the user preferences definitely more than the chance, and with 20 images we reach an nAUC of 91%, considering the cumulative matching characteristic curve. Extensive experiments promote our approach, suggesting new intriguing perspectives in the study of computational aesthetics.

Tantalum thin films are used in a wide range of applications, from microand nanoelectronics to military and aerospace applications, to medical devices. Tantalum has two phases, the stable bulk α phase and the metastable β phase found only in thin films. In a recent discovery, it has been shown that when tantalum is phase-transformed from β to α, it can form a unique microstructure with continuous changes in orientation and discontinuous grain boundaries. Dislocation structures must be present in order to account for the lattice curvature associated with orientation gradients. To identify the dislocation arrays that give rise to this microstructure, we first analyzed the electron backscatter diffraction (EBSD) data of the orientation gradients using geometrically necessary dislocations (GNDs), but we found that our EBSD data are too noisy and do not have sufficient resolution for this approach. In this work, two models are presented in an attempt to describe the dislocation structures that must be present to account for the orientation gradients. In the first, a method of generating smooth, noise-free orientation data that match key features of the actual phase-transformed microstructure is developed and validated by comparing model data with film data. In the second model, a genetic algorithm approach is used to generate dislocation structures that can account for the orientation gradients produced using the first model. The genetic algorithm model consistently generates dislocation structures that produce orientation maps with an average misorientation of less than 2◦ from the smooth orientation gradients generated by the first model. The generated dislocation structure is consistent across multiple runs, with similar shapes and Burgers vectors. The genetic algorithm model selects two types of dislocations, mostly (∼80%) [1 1 1] and the rest [1 1 1]. The dominance of these two slip systems provides a starting point to search for a mechanism in the β-to-α phase transformation that is capable of producing these dislocations.

Subgrain structures formed during plastic deformation of metals can be observed by electron backscatter diffraction (EBSD) but are challenging to identify automatically. We have adapted a 2D image segmentation technique, fast multiscale clustering (FMC), to 3D EBSD data using a novel variance function to accommodate quaternion data. This adaptation, which has been incorporated into the free open source texture analysis software package MTEX, is capable of segmenting based on subtle and gradual variation as well as on sharp boundaries within the data. FMC has been further modified to group the resulting closed 3D segment boundaries into distinct coherent surfaces based on local normals of a triangulated surface. We demonstrate the excellent capabilities of this technique with application to 3D EBSD data sets generated from cold rolled aluminum containing well-defined microbands, cold rolled and partly recrystallized extra low carbon steel microstructure containing three magnitudes of boundary misorientations, and channel-die plane strain compressed Goss-oriented nickel crystal containing microbands with very subtle changes in orientation.

Spatially resolved orientation mapping is increasingly performed using electron microscopy techniques, including: electron backscatter diffraction, transmission Kikuchi diffraction and scanning precession electron diffraction. The resulting orientation maps contain a wealth of information with crystal phase and orientation specified at each pixel. However, the depth of this data is often underutilized owing to challenges posed by the analysis of such large quantities of data. In the context of understanding complex and multi-phase materials it is important to characterize inter-phase relationships between nanoscale precipitates and the surrounding matrix, which both affect properties and are indicative of formation pathways. Revealing inter-phase relationships requires statistical assessment of the orientation relationship across the phase boundary, the spatial occurrence of particular boundaries and the surfaces of contact at interfaces. Analysis procedures that highlight relationships in both spatial and orientation dimensions are therefore required. Here, we present an approach to revealing inter-phase relationships, based on considering orientation data in 3-dimensional vector spaces constrained to fundamental zones defined by the crystal symmetry of both crystals.

Shear deformation of a solid-fluid, two-phase material induces a fluid segregation process that produces fluid-enriched bands and fluid-depleted regions, and a crystallographic preferred orientation (CPO) characterized by girdles of [100] and [001] axes sub-parallel to the shear plane and a cluster of [010] axes sub-normal to the shear plane, namely the AG-type fabric. Based on experiments of two-phase aggregates of olivine + basalt, a two-phase flow theory and a CPO formation model were established to explain these microstructures. Here, we investigate the microstructure in a two-phase aggregate with supercritical CO2 as the fluid phase and examine the theory and model, to evaluate differences in rheological properties due to the presence of CO2 or basaltic melt. We conducted high-temperature and high-pressure shear deformed experiments at 1 GPa and 1100 °C in a Griggs-type apparatus on samples made of olivine + dolomite, which decomposed into carbonate melt and CO2 at experimental conditions. After deformation, CO2 segregation and an AG-type fabric were observed in these CO2-bearing samples, similar to basaltic melt-bearing samples. An SPO-induce CPO model was used to explain to the formation of the fabric. Our results suggest that the influences of CO2 as a fluid phase on the microstructure of a two-phase olivine aggregate is similar to that of basaltic melt and can be explained by the CPO formation model for the solid-fluid system.

Selective Laser Melting (SLM) is an additive manufacturing technology used to directly produce metallic parts from thin powder layers. To evaluate the anisotropic mechanical properties, tensile test specimens of the Ni-base alloy Hastelloy X were built with the loading direction oriented either parallel (z-specimens) or perpendicular to the build-up direction (xy- specimens). Specimens were investigated in the "as-built" condition and after high temperature heat treatment. Tensile tests at room temperature and at 850°C of "as-built" material have shown different mechanical properties for z- and xy-specimens. The anisotropy is reflected in the Young's modulus, with lower values measured parallel to the build-up direction. It is shown that the anisotropy is significantly reduced by a subsequent recrystallization heat treatment. The characterization of microstructural and textural anisotropy was done by Electron Back Scatter Diffraction (EBSD) analysis. Predictions of Young's modulus calculated from the measured textures compare well with the data from tensile tests.

Samples of carbonado from Brazil and the Central African Republic were studied with the use of electron backscatter diffraction (EBSD) for quantitative textural analysis (QTA) and transmission electron microscopy (TEM). The grain size distribution in carbonado is either random or may be approximated by a log-normal distribution model with a mode at 6–8 μm. No bimodal distribution, as suggested previously for some carbonado samples, was observed. The crystallographic orientations of diamond grains in carbonado are quasi-random. The following minerals were identified among syngenetic mineral inclusions, enclosed in diamond grains of carbonado: garnet, apatite (including fluorapatite), phlogopite (or high-silica mica), SiO 2 , Ca-Mg-Sr- and Ca-Ba-carbonates, halides (sylvite and bismocolite BiOCl), native Ni and metal alloys (Fe-Ni, Cr-Fe-Mn, and Pb-As-Mo), oxides (FeO, Fe-Sn-O, TiO 2 , SnO 2 , and PbO 2 ), and Fe-sulfides, as well as fluid inclusions. Most of these occur over a very wide range of stability conditions. Only bismocolite, which is characteristic of the weathered crust, can be considered an entirely crustal mineral phase, which implies a possible crustal origin of the entire mineral association. Among syngenetic liquid inclusions in diamond grains comprising carbonado, silicate-carbonate ones overwhelmingly predominate. In addition to the usual silicate components, such as Si, Ti, Al, and Fe, they have Ca, K, and Cl; the latter three comprise 11.9 at.% of the only analyzed fluid inclusion.

Revision 2 1 Carbonate mineralization in percolated olivine aggregates: Linking effects of 2 crystallographic orientation and fluid flow 3 Steve Peuble , Muriel Andreani , Marguerite Godard , Philippe Gouze , Fabrice Barou , 4 Bertrand Van de Moortele , David Mainprice , Bruno Reynard 2 5 Géosciences Montpellier, Université Montpellier 2, CNRS, Place Eugène Bataillon cc060, F6 34095 Montpellier Cedex 5, France 7 Laboratoire de Géologie de Lyon: Terre, Planètes, Environnement, ENS-Université Lyon 1, 8 CNRS, F-69622 Villeurbanne, France 9 10 * Corresponding author. Tel.: + 33 467143944. 11 E-mail address: Steve.Peuble@gm.univ-montp2.fr 12 13