دورية أكاديمية
A Denoising Autoencoder for Improved Kikuchi Pattern Quality and Indexing in Electron Backscatter Diffraction.
| العنوان: | A Denoising Autoencoder for Improved Kikuchi Pattern Quality and Indexing in Electron Backscatter Diffraction. |
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| المؤلفون: | Andrews CE; Johns Hopkins University, Department of Materials Science and Engineering, Baltimore, MD., Strantza M; Lawrence Livermore National Laboratory, Materials Science Division, Livermore, CA., Calta NP; Lawrence Livermore National Laboratory, Materials Science Division, Livermore, CA., Matthews MJ; Lawrence Livermore National Laboratory, Materials Science Division, Livermore, CA., Taheri ML; Johns Hopkins University, Department of Materials Science and Engineering, Baltimore, MD. Electronic address: mtaheri4@jhu.edu. |
| المصدر: | Ultramicroscopy [Ultramicroscopy] 2023 Nov; Vol. 253, pp. 113810. Date of Electronic Publication: 2023 Jul 07. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Elsevier Country of Publication: Netherlands NLM ID: 7513702 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2723 (Electronic) Linking ISSN: 03043991 NLM ISO Abbreviation: Ultramicroscopy Subsets: PubMed not MEDLINE; MEDLINE |
| أسماء مطبوعة: | Publication: Amsterdam : Elsevier Original Publication: Amsterdam, North-Holland. |
| مستخلص: | The rapid collection and indexing of electron diffraction patterns as produced via electron backscatter diffraction (EBSD) has enabled crystallographic orientation and structural determination, as well as additional property-determining strain and dislocation density information with increasing speed, resolution, and efficiency. Pattern indexing quality is reliant on the noise of the collected electron diffraction patterns, which is often convoluted by sample preparation and data collection parameters. EBSD acquisition is sensitive to many factors and thus can result in low confidence index (CI), poor image quality (IQ), and improper minimization of fit, which can result in noisy datasets and misrepresent the microstructure. In an attempt to enable both higher speed EBSD data collection and enable greater orientation fit accuracy with noisy datasets, an image denoising autoencoder was implemented to improve pattern quality. We show that EBSD data processed through the autoencoder results in a higher CI, IQ, and a more accurate degree of fit. In addition, using denoised datasets in HR-EBSD cross correlative strain analysis can result in reduced phantom strain from erroneous calculations due to the increased indexing accuracy and improved correspondence between collected and simulated patterns. Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (Copyright © 2023. Published by Elsevier B.V.) |
| فهرسة مساهمة: | Keywords: Electron backscatter diffraction; HR-EBSD; Image Processing; Machine Learning; Scanning electron microscopy; Strain measurement |
| تواريخ الأحداث: | Date Created: 20230710 Latest Revision: 20230825 |
| رمز التحديث: | 20230826 |
| DOI: | 10.1016/j.ultramic.2023.113810 |
| PMID: | 37429066 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1879-2723 |
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| DOI: | 10.1016/j.ultramic.2023.113810 |