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Machine Learning Application for Prediction of Sapphire Crystals Defects

: Klunnikova, Yulia Vladimirovna; Anikeev, Maxim Vladimirovich; Filimonov, Alexey Vladimirovich; Kumar, Ravi

Volltext ()

Journal of electronic science and technology : JEST 18 (2020), Nr.1, Art. 100029, 9 S.
ISSN: 1674-862X
ISSN: 1672-6464
ISSN: 1001-0548
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer SIT ()
defects; machine learning; sapphire crystals

We investigate the impact of different numbers of positive and negative examples on machine learning for sapphire crystals defects prediction. We obtain the models of crystal growth parameters influence on the sapphire crystal growth. For example, these models allow predicting the defects that occur due to local overcooling of crucible walls in the thermal node leading to the accelerated crystal growth. We also develop the prediction models for obtained crystal weight, blocks, cracks, bubbles formation, and total defect characteristics. The models were trained on all data sets and later tested for generalization on testing sets, which did not overlap the training set. During training and testing, we find the recall, precision of prediction and analyze the correlation among the features. The results have shown that the precision of neural network method for predicting defects formed by local overcooling of the crucible reached 0.94.