机器学习辅助的多性质预测和烧结机制 探索多-金刚石陶
Qingyue Chen1, Weijin Zhang1, Xiaocheng Liang2
1School of Energy Science and Engineering, Central South University, Changsha 410083, China.
Materials (Basel, Switzerland)
|March 27, 2025
概括
梯度增强回归准确地预测了聚合物-金刚陶的性能,优于其他模型. 这种机器学习方法揭示了烧结温度和添加剂如何影响陶性能.
科学领域:
- 材料科学 材料科学 材料科学
- 陶工程 陶工程
- 计算材料科学科学 计算材料科学
背景情况:
- 聚合玉合金陶提供卓越的机械,热和化学性能,适用于热传输和热能存储等苛刻的应用.
- 传统的实验方法来表征这些陶是低效和劳动密集型.
- 预测建模提供了一种更有效的方法来理解结构-属性关系.
研究的目的:
- 开发和比较机器学习模型,用于预测聚合物-冠状陶的关键性质.
- 确定影响陶性能的关键加工参数和组成因素.
- 为了深入了解控制陶性质的潜在机制.
主要方法:
- 渐变增强回归 (GBR),随机森林 (RF) 和人工神经网络 (ANN) 模型的开发.
- 预测表面孔径,体密度,吸水能力和屈曲强度.
- 功能重要性和部分依赖性分析,以了解参数影响.
主要成果:
- 与RF (R2 0.83-0.89) 和ANN (R2 0.88-0.91) 相比,GBR模型显示出更高的预测准确性 (R2 0.91-0.95).
- 烧结温度和K2O含量与散装密度和屈曲强度正相关.
- 烧结温度和Fe2O3含量对屈曲强度产生了积极的影响,而K2O对孔隙性和水吸收产生了负面影响.
结论:
- 机器学习模型,特别是GBR,提供了一个有效的工具来预测多合金-金刚石陶的性能.
- 烧结温度,K2O和Fe2O3是影响陶性能的关键因素.
- 这项研究为先进陶的原料成分和加工属性关系提供了宝贵的见解.
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