논문 국내 국제전문학술지(SCI급) Modeling the Relationship between Process Parameters and Bulk Density of Barium Titanates
- 학술지 구분 국제전문학술지(SCI급)
- 게재년월 2019-10
- 저자명 김정한,Sang Eun Park,Hong In Kim,N.S.Reddy
- 학술지명 한국분말야금학회지
- 발행처명 한국분말야금학회
- 발행국가 국내
- 논문언어 외국어
- 전체저자수 4
홈페이지논문 초록 (Abstract)
The properties of powder metallurgy products are related to their densities. In the present work, we demonstrate a method to apply artificial neural networks (ANNs) trained on experimental data to predict the bulk density of barium titanates. The density is modeled as a function of pressure, press rate, heating rate, sintering temperature, and soaking time using the ANN method. The model predictions with the training and testing data result in a high coefficient of correlation (R2 = 0.95 and Pearson’s r = 0.97) and low average error. Moreover, a graphical user interface for the model is developed on the basis of the transformed weights of the optimally trained model. It facilitates the prediction of an infinite combination of process parameters with reasonable accuracy. Sensitivity analysis performed on the ANN model aids the identification of the impact of process parameters on the density of barium titanates.