@article{ART002790290},
author={Boyeol Kim and Ga Won Seo and HaManJin and Younwoo Hong and Chan-Yeup Chung},
title={Sintering process optimization of ZnO varistor materials by machine learning based metamodel},
journal={Journal of the Korean Crystal Growth and Crystal Technology},
issn={1225-1429},
year={2021},
volume={31},
number={6},
pages={258-263},
doi={10.6111/JKCGCT.2021.31.6.258}
TY - JOUR
AU - Boyeol Kim
AU - Ga Won Seo
AU - HaManJin
AU - Younwoo Hong
AU - Chan-Yeup Chung
TI - Sintering process optimization of ZnO varistor materials by machine learning based metamodel
JO - Journal of the Korean Crystal Growth and Crystal Technology
PY - 2021
VL - 31
IS - 6
PB - The Korea Association Of Crystal Growth, Inc.
SP - 258
EP - 263
SN - 1225-1429
AB - ZnO varistor is a semiconductor device which can serve to protect the circuit from surge voltage because itsnon-linear I-V characteristics by controlling the microstructure of grain and grain boundaries. In order to obtain desiredelectrical properties, it is important to control microstructure evolution during the sintering process. In this research, wedefined a dataset composed of process conditions of sintering and relative permittivity of sintered body, and collectedexperimental dataset with DOE. Meta-models can predict permittivity were developed by learning the collected experimentaldataset on various machine learning algorithms. By utilizing the meta-model, we can derive optimized sintering conditionsthat could show the maximum permittivity from the numerical-based HMA (Hybrid Metaheuristic Algorithm) optimizationalgorithm. It is possible to search the optimal process conditions with minimum number of experiments if meta-modelbasedoptimization is applied to ceramic processing.
KW - ZnO varistor;Sintering process;Process optimization;DOE;Machine learning;Ensemble decision tree
DO - 10.6111/JKCGCT.2021.31.6.258
ER -
Boyeol Kim, Ga Won Seo, HaManJin, Younwoo Hong and Chan-Yeup Chung. (2021). Sintering process optimization of ZnO varistor materials by machine learning based metamodel. Journal of the Korean Crystal Growth and Crystal Technology, 31(6), 258-263.
Boyeol Kim, Ga Won Seo, HaManJin, Younwoo Hong and Chan-Yeup Chung. 2021, "Sintering process optimization of ZnO varistor materials by machine learning based metamodel", Journal of the Korean Crystal Growth and Crystal Technology, vol.31, no.6 pp.258-263. Available from: doi:10.6111/JKCGCT.2021.31.6.258
Boyeol Kim, Ga Won Seo, HaManJin, Younwoo Hong, Chan-Yeup Chung "Sintering process optimization of ZnO varistor materials by machine learning based metamodel" Journal of the Korean Crystal Growth and Crystal Technology 31.6 pp.258-263 (2021) : 258.
Boyeol Kim, Ga Won Seo, HaManJin, Younwoo Hong, Chan-Yeup Chung. Sintering process optimization of ZnO varistor materials by machine learning based metamodel. 2021; 31(6), 258-263. Available from: doi:10.6111/JKCGCT.2021.31.6.258
Boyeol Kim, Ga Won Seo, HaManJin, Younwoo Hong and Chan-Yeup Chung. "Sintering process optimization of ZnO varistor materials by machine learning based metamodel" Journal of the Korean Crystal Growth and Crystal Technology 31, no.6 (2021) : 258-263.doi: 10.6111/JKCGCT.2021.31.6.258
Boyeol Kim; Ga Won Seo; HaManJin; Younwoo Hong; Chan-Yeup Chung. Sintering process optimization of ZnO varistor materials by machine learning based metamodel. Journal of the Korean Crystal Growth and Crystal Technology, 31(6), 258-263. doi: 10.6111/JKCGCT.2021.31.6.258
Boyeol Kim; Ga Won Seo; HaManJin; Younwoo Hong; Chan-Yeup Chung. Sintering process optimization of ZnO varistor materials by machine learning based metamodel. Journal of the Korean Crystal Growth and Crystal Technology. 2021; 31(6) 258-263. doi: 10.6111/JKCGCT.2021.31.6.258
Boyeol Kim, Ga Won Seo, HaManJin, Younwoo Hong, Chan-Yeup Chung. Sintering process optimization of ZnO varistor materials by machine learning based metamodel. 2021; 31(6), 258-263. Available from: doi:10.6111/JKCGCT.2021.31.6.258
Boyeol Kim, Ga Won Seo, HaManJin, Younwoo Hong and Chan-Yeup Chung. "Sintering process optimization of ZnO varistor materials by machine learning based metamodel" Journal of the Korean Crystal Growth and Crystal Technology 31, no.6 (2021) : 258-263.doi: 10.6111/JKCGCT.2021.31.6.258
