Analysis of SEM Images with Artificial Intelligence Methods
Yıl 2022,
, 35 - 38, 31.12.2022
Ayşe Demirkan
,
İsmail Topcu
Öz
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.
Teşekkür
Northwestern University 'de Doç. Dr. Ulaş BAĞCI'ya teşekkürler.
Kaynakça
- 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.
- 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.
- Acı, M., Avcı M. (2016). ‘’Artificial neural network approach for atomic coordinate prediction of carbon nanotubes’’, Appl. Phys., 122, 631.
- Raitoharju, J., (2022).‘’Chapter 3 - Convolutional neural networks’’, Editor(s): Al. Iosifidis, A. Tefas, Deep Learning for Robot Perception and Cognition,Academic Press, 35-69, ISBN 9780323857871.
- Sowmya, B. P., Supriya,M. C. (2021). ‘’Convolutional Neural Network (CNN) Fundamental Operational Survey’’, Learning and Analytics in Intelligent Systems, 21, Springer, Cham.
- 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.
- Rauniyar,A., Hagos,D., Bağcı,U.(2022). ‘’Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions’’.
- Yamashita, R., Nishio, M., Do, R.K.G. et al,.(2018). ‘’Convolutional neural networks: an overview and application in radiology. Insights Imaging 9’’, 611–629.
- Virtanen, P., Gommers, Oliphant, R., T. E., et al.(2020). ‘’SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods’’ ,17,261–272.
- LeCun,Y.,Bengio,Y.,Hinton,G.E.(2015)‘’Deeplearning’’, Nature ,521,436–444.
- Turing,A. M.(2009). ‘’Computing Machinery and Intelligence’’, In: Epstein R., Roberts G., Beber G. (eds) Parsing the Turing Test. Springer, Dordrecht.
- Booth,T. J.,Baker, M.A.B.(2017). ‘’Chapter 32 - Nanotechnology: Building and Observing at the Nanometer Scale’’, Pharmacognosy,Academic Press,633-643.
Analysis of SEM Images with Artificial Intelligence Methods
Yıl 2022,
, 35 - 38, 31.12.2022
Ayşe Demirkan
,
İsmail Topcu
Öz
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.
Kaynakça
- 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.
- 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.
- Acı, M., Avcı M. (2016). ‘’Artificial neural network approach for atomic coordinate prediction of carbon nanotubes’’, Appl. Phys., 122, 631.
- Raitoharju, J., (2022).‘’Chapter 3 - Convolutional neural networks’’, Editor(s): Al. Iosifidis, A. Tefas, Deep Learning for Robot Perception and Cognition,Academic Press, 35-69, ISBN 9780323857871.
- Sowmya, B. P., Supriya,M. C. (2021). ‘’Convolutional Neural Network (CNN) Fundamental Operational Survey’’, Learning and Analytics in Intelligent Systems, 21, Springer, Cham.
- 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.
- Rauniyar,A., Hagos,D., Bağcı,U.(2022). ‘’Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions’’.
- Yamashita, R., Nishio, M., Do, R.K.G. et al,.(2018). ‘’Convolutional neural networks: an overview and application in radiology. Insights Imaging 9’’, 611–629.
- Virtanen, P., Gommers, Oliphant, R., T. E., et al.(2020). ‘’SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods’’ ,17,261–272.
- LeCun,Y.,Bengio,Y.,Hinton,G.E.(2015)‘’Deeplearning’’, Nature ,521,436–444.
- Turing,A. M.(2009). ‘’Computing Machinery and Intelligence’’, In: Epstein R., Roberts G., Beber G. (eds) Parsing the Turing Test. Springer, Dordrecht.
- Booth,T. J.,Baker, M.A.B.(2017). ‘’Chapter 32 - Nanotechnology: Building and Observing at the Nanometer Scale’’, Pharmacognosy,Academic Press,633-643.