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Conference Paper
Predicting and Optimizing Shrinkage Rates in Zirconia Block Production through Machine Learning
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- Authors
- Hyunjong Kim, Suyoung Chi, Hyunwoo Oh
- Issue Date
- 2024-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2024, pp.221-222
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- In the production of dental zirconia blocks, accurately predicting and optimizing the shrinkage rate after sintering is essential for manufacturing high-quality prosthetics. This study proposes a machine learning model that considers up to five different process conditions and raw material compositions to predict and deduce the optimal conditions for achieving the desired shrinkage rate. The model successfully analyzed various manufacturing data to accurately predict shrinkage rates and derive optimal process conditions. This approach suggests that significant improvements in product consistency and production efficiency can be achieved within the dental materials industry.
- KSP Keywords
- Dental materials, Dental zirconia(Y-TZP), High-quality, Manufacturing data, Optimal condition, Process conditions, Production efficiency, learning models, machine Learning, raw material, shrinkage rate