The development of automated segmentation and quantitative characterization of microtextured regions (MTRs) from the complex heterogeneous microstructures is urgently needed, since MTRs have been proven to be the critical issue that dominates the dwell-fatigue performance of aerospace components. In addition, MTRs in Ti alloys have similarities to microstructures encountered in other materials, including minerals and biomaterials. Meanwhile, machine learning (ML) offers new opportunities. This paper addresses segmentation and quantitative characterization of MTRs, where an ML approach, the Gaussian mixture models (GMMs) coupled with density-based spatial clustering of applications with noise (DBSCAN) clustering algorithms, was employed in order to process the orientation data acquired via EBSD in the Matlab environment. Pixels with orientation information acquired through electron backscatter diffraction (EBSD) are divided and colored into several “classes” (MTRs) within the defined c-axis misorientations (i.e., 25°, 20°, 15°, 10°, and 5°), the precision and efficacy of which are verified by the morphology and pole figure of the segmented MTR. An appropriate range of c-axis misorientations for MTR segmentation was derived, i.e., 15~20°. The contribution of this innovative technique is compared with previous studies. At the same time, the MTRs were statistically characterized in the global region.

The impact of the torsional component on the microstructure and mechanical characteristics of a titanium alloy is examined in this work using a combination of numerical simulation and experimental validation. During the axial closed die rolling (ACDR) forming, the combined effects of compressive and torsional deformation cause a significant increase in the specimen’s cumulative strain. The specimen’s shear strain changes most significantly at the height of H/2. The α-phase has a greater propensity to slip on the conical surface, followed by the cylindrical surface, according to SEM and EBSD analyses. The basal surface has the highest resistance to slip. The formation of a fine isometric α-phase occurs when the compressive component causes the α-phase to become more prone to breakage and fracture. A larger α-phase will form because of the torsional component’s influence, which increases the likelihood that the α-phase will slip and exhibit bending and twisting. With a difference in strength of less than 1 percent and a difference in plasticity between the tangential and radial directions of less than 5 percent, the mechanical properties of the TC11 disks formed by the ACDR show a greater degree of isotropy. The specimens show a tough fracture mode, with radial performance outperforming tangential performance, according to fracture morphology analysis.

The torsional behavior during the deformation process of the axial closed die rolling the axial closed rolling (ACDR) forming is studied in this paper using a numerical simulation technique on TC11 titanium alloy. The axial and radial pinch angles, as well as the degree of specimen torsion, increased with the amount of deformation. The orientation distribution function (ODF) maps of the α-phase and β-phase were obtained by Electron Back Scatter Diffraction (EBSD) treatment of the TC11 titanium alloy. It can be noticed that there were different types of texture with different strengths in the ACDR samples, and in the xz and yz planes, textures in the direction of the column were predominantly of {0001} and {01$\overline{1}$0} ; the weaker the texture was, the closer to the edge of the sample. In the xy plane, the texture structure was mainly distributed along the cone direction, and the textures were {$\overline{1}$2$\overline{1}$0} and {01$\overline{1}$0} . However, the closer to the edge position of the specimen, the higher the intensity of the texture, and the texture was {1$\overline{2}$1$\overline{2}$} . The β-phase is mainly distributed as {001} , {110} , and {110} textures within the specimen, and the texture strength is about 8.5 times. However, owing to the small proportion of the β-phase content in the specimen, the distribution pattern of its texture has a weak impact on the texture distribution of the overall specimen. A high degree of isotropy in the radial and tangential tensile properties, with a strength isotropy of over 99 percent and a plasticity isotropy of over 95 percent, resulted from the distribution of texture types with varying strengths and orientations within the ACDR specimens, which weakened the TC11 discs’ overall orientation.

This study proposes a novel framework for quantifying curved grain boundaries that overcomes key limitations of existing methods. Unlike Fourier-based approaches that require labor-intensive sequential analysis of individual boundaries and selectively represent only high-amplitude regions, or spline-based methods that demand complex parameter selection for interpolation points, the proposed framework integrates curvature variance filtering with U-chord curvature calculation to enable automated, comprehensive, and noise-resistant characterization of grain boundary morphology. The curvature variance filtering adaptively determines smoothing parameters based on local curve properties, while the U-chord curvature method ensures rotational invariance and robustness against digitization errors. Four heat treatment processes were applied to GH4169 alloy, producing distinct grain boundary morphologies with mean curvature (MC) values ranging from 0.0625 to 0.1252. Controlled cooling alone (Process A) yielded predominantly straight boundaries (91.06% straight, 0.12% serrated), while re-dissolution treatment (Process D) produced the highest serration degree (58.81% straight, 3.53% serrated). The quantitative analysis reveals that dispersed δ-phase precipitation creates discrete pinning points, forming serrated boundaries with sharp curvature peaks, whereas dense, parallel δ-phase arrays at specific angles produce coordinated wavy undulations. This framework provides a reliable quantitative tool for optimizing heat treatment protocols to achieve target grain boundary configurations in nickel-based superalloys.