The formation and evolution of macrozone in Ti-6242S alloy during thermo-mechanical processing

Huang, Long; Sun, Zhichao; Cao, Jing; Yin, Zhikun

doi:10.1016/j.jallcom.2020.158533

Cited by 8 publications

(8 citation statements)

References 35 publications

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“…Figure 5a depicts the segmentation of MTRs from the global region, and Figure 5c depicts MTRs with long-axis lengths exceeding 200 µm (approximately 10 average alpha particles) being separated. Figure 5d,e show the statistical results of the length and high aspect ratio of the MTRs presented in Figure 5c, where the mean value of length is 581.9 µm and the mean value of the high [11,14]. The macrozone IV is an MTR framed with rectangles.…”

Section: Characterization Of Mtrsmentioning

confidence: 99%

“…Therefore, 15~20 • could be employed as an appropriate range of c-axis misorientations for MTR segmentation. The contribution of the segmentation technique using the ML approach here is compared with previous studies, which segmented manually [11,14]. In this study, MTRs were segmented according to the c-axis misorientation (i.e., 15 • in Figure 4), where the ML approach, the GMMs coupled with DBSCAN, was employed to process the orientation data acquired via EBSD in the Matlab environment.…”

Section: Mtr Segmentationmentioning

confidence: 99%

“…In summary, the segmentation technique using the ML approach here is more effective. [11,14]. The macrozone IV is an MTR framed with rectangles.…”

Section: Characterization Of Mtrsmentioning

confidence: 99%

“…The developed method was able to characterize the orientation of MTRs, but it cannot identify information about the morphology and size of MTRs. Then, a more precise and detailed solution to segmenting MTRs from the obtained electron backscatter diffraction (EBSD) data was devised, based on inverse pole figure maps [13][14][15]. These studies lacked a clear basis for segmenting MTRs and relied on manual selection of MTRs, which cannot characterize MTRs quantitatively, automatically, and precisely.…”

Section: Introductionmentioning

confidence: 99%

“…As one may have noticed, 15° is a threshold, beyond which (i.e., at 15° or 20°) the length of the MTR and microtexture strength start to vary gently with c-axis misorientations. Therefore, 15~20° could be employed as an appropriate range of c-axis misorientations for MTR segmentation.The contribution of the segmentation technique using the ML approach here is compared with previous studies, which segmented manually[11,14]. In this study, MTRs were segmented according to the c-axis misorientation (i.e., 15° in Figure4), where the ML approach, the GMMs coupled with DBSCAN, was employed to process the orientation data acquired via EBSD in the Matlab environment.…”

mentioning

confidence: 99%

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A Machine Learning Approach for Segmentation and Characterization of Microtextured Regions in a Near-α Titanium Alloy

Rao,

Liu,

Jin

et al. 2023

Crystals

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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.

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Section: Characterization Of Mtrsmentioning

confidence: 99%

Section: Mtr Segmentationmentioning

confidence: 99%

“…In summary, the segmentation technique using the ML approach here is more effective. [11,14]. The macrozone IV is an MTR framed with rectangles.…”

Section: Characterization Of Mtrsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%