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Prediction of bending strength of Si3N4 using machine learning by Yang, Ping (author);Wu, Shuangshuang (author);Wu, Haonan (author);Lu, Donglin (author);Zou, Wenjing (author);Chu, Luojing (author);Shao, Yuanzhi (author);Wu, Shanghua (author) is a Materials Science article available to read on EtoBox.
What is Prediction of bending strength of Si3N4 using machine learning about?
The bending strength of silicon nitride (Si 3 N 4 ) plays a vital role in its application and is influenced by various process factors. Current experimental methods for investigating Si 3 N 4 ceramics exhibiting low efficiency and high cost are incapable of systematically analysing the effect of process factors on the bending strength of Si 3 N 4 ceramics and quantitatively predicting the optimum process parameters. In this study, machine learning (ML) approaches based on extreme gradient boosting (XGBoost) were applied to predict and analyse the bending strength of Si 3 N 4 ceramics. Because the classification model of XGBoost is easily interpretable, the factors affecting the bending strength could be quantitatively evaluated. The current model can provide a suitable order of adding sintering additives to obtain excellent bending strength in Si 3 N 4 ceramics. Although this study focuses on the bending strength of Si 3 N 4 ceramics, the new approach reported herein is applicable for the in silico design and analysis of other ceramic materials.
Who reads Prediction of bending strength of Si3N4 using machine learning?
It is typically read by researchers, students, and practitioners in Materials Science.
- Author
- Yang, Ping (author);Wu, Shuangshuang (author);Wu, Haonan (author);Lu, Donglin (author);Zou, Wenjing (author);Chu, Luojing (author);Shao, Yuanzhi (author);Wu, Shanghua (author)
- Publisher
- Elsevier BV
- Published
- 2021
- Language
- EN
- Field
- Materials Science (Physical Sciences)