Smart classification method to detect irregular nozzle spray patterns inside carbon black reactor using ensemble transfer learning
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Industrial and Manufacturing Engineering,Software
Link
https://link.springer.com/content/pdf/10.1007/s10845-022-01951-y.pdf
Reference59 articles.
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2. Aminzadeh, M., & Kurfess, T. R. (2018). Online quality inspection using Bayesian classification in powder-bed additive manufacturing from high-resolution visual camera images. Journal of Intelligent Manufacturing, 30(6), 2505–2523. https://doi.org/10.1007/s10845-018-1412-0
3. Badmos, O., Kopp, A., Bernthaler, T., & Schneider, G. (2019). Image-based defect detection in lithium-ion battery electrode using convolutional neural networks. Journal of Intelligent Manufacturing, 31(4), 885–897. https://doi.org/10.1007/s10845-019-01484-x
4. Bermejo-Pelaez, D., Ash, S. Y., Washko, G. R., Jose Estepar, S., R., & Ledesma-Carbayo, M. J. (2020). Classification of interstitial lung abnormality patterns with an ensemble of deep convolutional neural networks. Scientific Reports, 10(1), 338. https://doi.org/10.1038/s41598-019-56989-5
5. Bustillo, A., Reis, R., Machado, A. R., & Pimenov, D. Y. (2020). Improving the accuracy of machine-learning models with data from machine test repetitions. Journal of Intelligent Manufacturing, 33(1), 203–221. https://doi.org/10.1007/s10845-020-01661-3
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