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http://dx.doi.org/10.6111/JKCGCT.2021.31.6.258

Sintering process optimization of ZnO varistor materials by machine learning based metamodel  

Kim, Boyeol (Virtual Engineering Center, Korea Institute of Ceramic Engineering & Technology)
Seo, Ga Won (Virtual Engineering Center, Korea Institute of Ceramic Engineering & Technology)
Ha, Manjin (Virtual Engineering Center, Korea Institute of Ceramic Engineering & Technology)
Hong, Youn-Woo (Virtual Engineering Center, Korea Institute of Ceramic Engineering & Technology)
Chung, Chan-Yeup (Technology Convergence Division, Korea Institute of Ceramic Engineering & Technology)
Publication Information
Journal of the Korean Crystal Growth and Crystal Technology / v.31, no.6, 2021 , pp. 258-263 More about this Journal
Abstract
ZnO varistor is a semiconductor device which can serve to protect the circuit from surge voltage because its non-linear I-V characteristics by controlling the microstructure of grain and grain boundaries. In order to obtain desired electrical properties, it is important to control microstructure evolution during the sintering process. In this research, we defined a dataset composed of process conditions of sintering and relative permittivity of sintered body, and collected experimental dataset with DOE. Meta-models can predict permittivity were developed by learning the collected experimental dataset on various machine learning algorithms. By utilizing the meta-model, we can derive optimized sintering conditions that could show the maximum permittivity from the numerical-based HMA (Hybrid Metaheuristic Algorithm) optimization algorithm. It is possible to search the optimal process conditions with minimum number of experiments if meta-model-based optimization is applied to ceramic processing.
Keywords
ZnO varistor; Sintering process; Process optimization; DOE; Machine learning; Ensemble decision tree;
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