The present work shows a novel physical-mathematical model to estimate the average corrosion resistance index from the crystallographic texture in API 5L steels. The crystallographic texture of the studied steels was measured by means of the X-ray diffraction technique. The model, based on the symmetric spherical surface harmonics for a BCC structure, is capable of describing the anisotropy surface of anodic dissolution resistance of the crystal and establishing a straightforward relationship between crystallographic texture, surface roughness, and metal corrosion behavior. The predictions of the average corrosion resistance index made from the crystallographic texture were in good agreement with those obtained from potentiodynamic polarization curves for the investigated steels. This agreement validates the capacity of this model and opens the possibility of applying it as a novel criterion for the material selection and design stages in order to combat metal corrosion problems.

Shear deformation of a solid-fluid, two-phase material induces a fluid segregation process that produces fluid-enriched bands and fluid-depleted regions, and 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, as CO2 is different from basaltic melt in rheological properties. We conducted high‐temperature and high-pressure shear deformed experiments at 1 GPa and 1100ºC 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 occurred in these CO2-bearing samples, inconsistency with basaltic melt-bearing samples. The SPO-induce CPO model was used to explain 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.

Cellular substructure has been widely observed in the sample fabricated by laser powder bed fusion, while its growth direction and the crystallographic orientation have seldom been studied. This research tries to build a general model to construct the substructure from its two-dimensional morphology. All the three Bunge Euler angles to specify a unique growth direction are determined, and the crystallographic orientation corresponding to the growth direction is also obtained. Based on the crystallographic orientation, the substructure in the single track of austenitic stainless steel 316L is distinguished between the cell-like dendrite and the cell. It is found that, with the increase of scanning velocity, the substructure transits from cell-like dendrite to cell. When the power is 200 W, the critical growth rate of the transition in the single track can be around 0.31 ms^-1.

Geometric Akaike Information Criteria (G-AICs) for generalized noise-level dependent crystallographic symmetry classifications of two-dimensional (2D) images that are more or less periodic in either two or one dimensions as well as Akaike weights for multi-model inferences and predictions are reviewed. Such novel classifications do not refer to a single crystallographic symmetry class exclusively in a qualitative and definitive way. Instead, they are quantitative, spread over a range of crystallographic symmetry classes, and provide opportunities for inferences from all classes (within the range) simultaneously. The novel classifications are based on information theory and depend only on information that has been extracted from the images themselves by means of maximal likelihood approaches so that these classifications are objective. This is in stark contrast to the common practice whereby arbitrarily set thresholds or null hypothesis tests are employed to force crystallographic symmetry classifications into apparently definitive/exclusive states, while the geometric feature extraction results on which they depend are never definitive in the presence of generalized noise, i.e. in all real world applications. Thus, there is unnecessary subjectivity in the currently practiced ways of making crystallographic symmetry classifications, which can be overcome by the approach outlined in this review.

In the present work we revise and extend the Characteristic Crystallographic Element (CCE) norm, an algorithm used to simultaneously detect radial and orientational similarity of computer-generated structures with respect to specific reference crystals and local symmetries. Based on the identification of point group symmetry elements, the CCE descriptor is able to gauge local structure with high precision and finely distinguish between competing morphologies. As test cases we use computer-generated monomeric and polymer systems of spherical particles interacting with the hard-sphere and square-well attractive potentials. We demonstrate that the CCE norm is able to detect and differentiate, between others, among: hexagonal close packed (HCP), face centered cubic (FCC), hexagonal (HEX) and body centered cubic (BCC) crystals as well as non-crystallographic fivefold (FIV) local symmetry in bulk 3-D systems; triangular (TRI), square (SQU) and honeycomb (HON) crystals, as well as pentagonal (PEN) local symmetry in thin films of one-layer thickness (2-D systems). The descriptor is general and can be applied to identify the symmetry elements of any point group for arbitrary atomic or particulate system in two or three dimensions, in the bulk or under confinement.