Research Article

Analysis of SEM Images with Artificial Intelligence Methods

Number: 44 December 31, 2022
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Analysis of SEM Images with Artificial Intelligence Methods

Abstract

Today, the quality of Nanotechnology and Nanoscience working with Artificial Intelligence is increasing day by day. Gains the importance of materials science effectively. Examination of SEM Images with Artificial Intelligence Methods represents a multidisciplinary field. In forming the data used in the experimental part, 22,000 SEM data are publicly available. It is known that CNR-IOM's TASC laboratory in Trieste was obtained as a result of 5 years of work of 100 scientists with the ZEISS SUPRA 40 resolution device. After examining the resolution, image size and quality one by one for the selection of the data in the prototype created for the experimental study, the feature that is considered is the image quality. In the creation of this data, after 100 image data are manually selected and arranged in nano and micro dimensions; A total of 1000 image data were created in 10 data sets. Then, artificial intelligence training was carried out using the CNN classification technique in the experimental study using the unsupervised learning method through machine learning. The approach used here enables the application of new methods and tools by adjusting to develop suitable parameters to solve specific properties of nanomaterials that can be applied to a wide variety of nanoscience use cases. Using it to create a materials science library may pave the way for future studies in the field of artificial intelligence and nanotechnology.

Keywords

Thanks

Northwestern University 'de Doç. Dr. Ulaş BAĞCI'ya teşekkürler.

References

  1. Akhtar, K., Khan, S.A., Khan, S.B., Asiri, A.M. (2018). Scanning Electron Microscopy: Principle and Applications in Nanomaterials Characterization In: Sharma, S. (eds) Handbook of Materials Characterization. Springer, Cham.
  2. Topcu, İ. (2018). Investigation of Mechanical Behavior of Carbon Nanotube Reinforced Aluminum Matrix AlMg/CNT Composites. Çanakkale Onsekiz Mart University Journal of ScienceInstitute,4(1),99-109.
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  6. Topcu, İ., Muhammet, C., Yılmaz, E.B. ‘‘Experimental investigation on mechanical properties of Multi Wall Carbon Nanotubes (MWCNT) reinforced aluminium metal matrix composites’’, Journal of Ceramic Processing Research, 21 (5), 596-601.
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Details

Primary Language

English

Subjects

Engineering

Journal Section

Research Article

Publication Date

December 31, 2022

Submission Date

December 14, 2022

Acceptance Date

December 26, 2022

Published in Issue

Year 2022 Number: 44

APA
Demirkan, A., & Topcu, İ. (2022). Analysis of SEM Images with Artificial Intelligence Methods. Avrupa Bilim Ve Teknoloji Dergisi, 44, 35-38. https://doi.org/10.31590/ejosat.1219252
AMA
1.Demirkan A, Topcu İ. Analysis of SEM Images with Artificial Intelligence Methods. EJOSAT. 2022;(44):35-38. doi:10.31590/ejosat.1219252
Chicago
Demirkan, Ayşe, and İsmail Topcu. 2022. “Analysis of SEM Images With Artificial Intelligence Methods”. Avrupa Bilim Ve Teknoloji Dergisi, nos. 44: 35-38. https://doi.org/10.31590/ejosat.1219252.
EndNote
Demirkan A, Topcu İ (December 1, 2022) Analysis of SEM Images with Artificial Intelligence Methods. Avrupa Bilim ve Teknoloji Dergisi 44 35–38.
IEEE
[1]A. Demirkan and İ. Topcu, “Analysis of SEM Images with Artificial Intelligence Methods”, EJOSAT, no. 44, pp. 35–38, Dec. 2022, doi: 10.31590/ejosat.1219252.
ISNAD
Demirkan, Ayşe - Topcu, İsmail. “Analysis of SEM Images With Artificial Intelligence Methods”. Avrupa Bilim ve Teknoloji Dergisi. 44 (December 1, 2022): 35-38. https://doi.org/10.31590/ejosat.1219252.
JAMA
1.Demirkan A, Topcu İ. Analysis of SEM Images with Artificial Intelligence Methods. EJOSAT. 2022;:35–38.
MLA
Demirkan, Ayşe, and İsmail Topcu. “Analysis of SEM Images With Artificial Intelligence Methods”. Avrupa Bilim Ve Teknoloji Dergisi, no. 44, Dec. 2022, pp. 35-38, doi:10.31590/ejosat.1219252.
Vancouver
1.Ayşe Demirkan, İsmail Topcu. Analysis of SEM Images with Artificial Intelligence Methods. EJOSAT. 2022 Dec. 1;(44):35-8. doi:10.31590/ejosat.1219252

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