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Recent advances and applications of deep learning methods in materials science.
- Published in:
- NPJ Computational Materials, 2022, v. 8, n. 1, p. 1, doi. 10.1038/s41524-022-00734-6
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- Article
Correction to: Instance Segmentation for Direct Measurements of Satellites in Metal Powders and Automated Microstructural Characterization from Image Data.
- Published in:
- 2022
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- Publication type:
- Correction Notice
Instance Segmentation for Direct Measurements of Satellites in Metal Powders and Automated Microstructural Characterization from Image Data.
- Published in:
- JOM: The Journal of The Minerals, Metals & Materials Society (TMS), 2021, v. 73, n. 7, p. 2159, doi. 10.1007/s11837-021-04713-y
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- Article
Calorimetric Study with Uncertainty Analysis to Investigate the Precipitation Kinetics in a Nanostructured Al Composite.
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- Advanced Engineering Materials, 2018, v. 20, n. 4, p. 1, doi. 10.1002/adem.201700728
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- Article
Unsupervised Machine Learning Via Transfer Learning and k-Means Clustering to Classify Materials Image Data.
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- Integrating Materials & Manufacturing Innovation, 2021, v. 10, n. 2, p. 231, doi. 10.1007/s40192-021-00205-8
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- Article
COMPUTER VISION AND MACHINE LEARNING TO QUANTIFY MICROSTRUCTURE.
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- Advanced Materials & Processes, 2021, v. 179, n. 2, p. 13, doi. 10.31399/asm.amp.2021-02.p013
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- Article
Overview: Computer Vision and Machine Learning for Microstructural Characterization and Analysis.
- Published in:
- Metallurgical & Materials Transactions. Part A, 2020, v. 51, n. 12, p. 5985, doi. 10.1007/s11661-020-06008-4
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- Article