This is a viewpoint paper on recent progress in the understanding of the microstructure–property relations of advanced high-strength steels (AHSS). These alloys constitute a class of high-strength, formable steels that are designed mainly as sheet products for the transportation sector. AHSS have often very complex and hierarchical microstructures consisting of ferrite, austenite, bainite, or martensite matrix or of duplex or even multiphase mixtures of these constituents, sometimes enriched with precipitates. This complexity makes it challenging to establish reliable and mechanism-based microstructure–property relationships. A number of excellent studies already exist about the different types of AHSS (such as dual-phase steels, complex phase steels, transformation-induced plasticity steels, twinning-induced plasticity steels, bainitic steels, quenching and partitioning steels, press hardening steels, etc.) and several overviews appeared in which their engineering features related to mechanical properties and forming were discussed. This article reviews recent progress in the understanding of microstructures and alloy design in this field, placing particular attention on the deformation and strain hardening mechanisms of Mn-containing steels that utilize complex dislocation substructures, nanoscale precipitation patterns, deformation-driven transformation, and twinning effects. Recent developments on microalloyed nanoprecipitation hardened and press hardening steels are also reviewed. Besides providing a critical discussion of their microstructures and properties, vital features such as their resistance to hydrogen embrittlement and damage formation are also evaluated. We also present latest progress in advanced characterization and modeling techniques applied to AHSS. Finally, emerging topics such as machine learning, through-process simulation, and additive manufacturing of AHSS are discussed. The aim of this viewpoint is to identify similarities in the deformation and damage mechanisms among these various types of advanced steels and to use these observations for their further development and maturation.

/pdf/current-challenges-and-opportunities-in-microstructure-4ruzdmwooi.pdf

Current Challenges and Opportunities in Microstructure-Related Properties of Advanced High-Strength Steels

https://journals.iucr.org/m/issues/2016/01/00/ti5006/ti5006.pdf

Comparison between diffraction contrast tomography and high-energy diffraction microscopy on a slightly deformed aluminium alloy

A previously introduced mathematical framework for full-field X-ray orientation microscopy is for the first time applied to experimental near-field diffraction data acquired from a polycrystalline sample. Grain by grain tomographic reconstructions using convex optimization and prior knowledge are carried out in a six-dimensional representation of position-orientation space, used for modelling the inverse problem of X-ray orientation imaging. From the 6D reconstruction output we derive 3D orientation maps, which are then assembled into a common sample volume. The obtained 3D orientation map is compared to an EBSD surface map and local misorientations, as well as remaining discrepancies in grain boundary positions are quantified. The new approach replaces the single orientation reconstruction scheme behind X-ray diffraction contrast tomography and extends the applicability of this diffraction imaging technique to material micro-structures exhibiting sub-grains and/or intra-granular orientation spreads of up to a few degrees. As demonstrated on textured sub-regions of the sample, the new framework can be extended to operate on experimental raw data, thereby bypassing the concept of orientation indexation based on diffraction spot peak positions. This new method enables fast, three-dimensional characterization with isotropic spatial resolution, suitable for time-lapse observations of grain microstructures evolving as a function of applied strain or temperature.

/pdf/three-dimensional-full-field-x-ray-orientation-microscopy-4vc1me5vsz.pdf

Three-dimensional full-field X-ray orientation microscopy

A method for reconstructing the three-dimensional grain structure from data collected with a recently introduced laboratory-based X-ray diffraction contrast tomography system is presented. Diffraction contrast patterns are recorded in Laue-focusing geometry. The diffraction geometry exposes shape information within recorded diffraction spots. In order to yield the three-dimensional crystallographic microstructure, diffraction spots are extracted and fed into a reconstruction scheme. The scheme successively traverses and refines solution space until a reasonable reconstruction is reached. This unique reconstruction approach produces results efficiently and fast for well suited samples.

/pdf/3d-grain-reconstruction-from-laboratory-diffraction-contrast-1z1tl467m9.pdf

3D grain reconstruction from laboratory diffraction contrast tomography.

In this paper explains how to turn full three dimensional (3D) experimental grain maps into a finite elements (FE) mesh suitable for mechanical analysis. Two examples from diffraction contrast tomography characterizations are presented. Deformation of a pure titanium sample with 1400 grains is computed using elastic anisotropy and accurate boundary conditions allows to correctly capture the grain to grain elastic strain variations. In the second example a significantly large zone of a polycrystalline Al–Li sample is meshed and computed using elastoplastic finite strain calculation. Mean lattice reorientations and intra-grain lattice orientation spread are obtained as a function of deformation.

Coupling Diffraction Contrast Tomography with the Finite Element Method

The reconstruction of the 3D grain microstructure in the bulk of polycrystalline materials is a problem that presents many challenges, both from an experimental and data analysis point of view. In non-destructive techniques based on monochromatic beam X-Ray diffraction, the local orientation of the crystal lattice, whose knowledge is the aim of the reconstruction, is also responsible for the deformation of the images recorded by the detector.
Different computational approaches have been proposed over the years, to deal with the different available experimental techniques. One of the common shortcomings has been the restriction to single voxel optimization.
It has been shown that stochastic optimization techniques can be used to approach the solution, matching the diffracted intensities and data acquisition schemes (namely, line beam illumination or 3D volumes from stacking of 2D layers), requiring however long acquisition times.
Ideally, reconstruction of the 3D orientation field would be achieved from extended beam diffraction data and the optimization should be performed over the volume of a grain or ultimately the sample volume.
X-Ray Diffraction Contrast Tomography is an extended beam tomographic imaging technique which demands shorter acquisition time compared to other techniques.
If the reconstruction of the local crystal orientation is extended to the full 3D volume of the grain, the problem will be intrinsically six-dimensional, while if we restrict it only a 2D slice, the reconstruction problem will become five-dimensional, because for each point in the real-space we always assume a three-dimensional orientation-space.
In both cases, it will always be possible to describe the reconstruction as the solution to a non-linear system A(x) x = b, where x* is the true solution to the system. Unfortunately the availability of components in b will be less than required by the number of unknowns in the vector x, and even if the problem could be translated into a classic l2-norm minimization problem over the residual r(x) = b - A(x) x, the intrinsic non-linearity of the problem will not guarantee a unique minimum.
The developments in mathematical optimization, during the last 15 years, have shown that tools like l1-minimization, used along with sparsifying transformations in the solution domain, can recover specific solutions to heavily under-determined systems.
We propose a practical representation of the orientation space, which avoids higher degrees of under-determinacy and linearizes the reconstruction problem. We also describe the application of the recent developments mathematical optimization to DCT data. This approach overcomes the single voxel optimization restriction.
Finally we present promising results obtained on simulated data.

Reconstruction of Local Orientation in Grains using a Discrete Representation of the Orientation Space

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Reconstruction of Local Orientation in Grains using a Discrete Representation of the Orientation Space