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在燃烧阶段预测陶属性的先进机器学习模型

Milica V Vasić¹, Paul O Awoyera², Olaolu George Fadugba^3,4

¹Institute for Testing of Materials, Centre for Materials, Bulevar vojvode Mišića 43, Beograd, 11040, Serbia. milica.vasic@institutims.rs.

Scientific reports

|August 26, 2025

概括

机器学习使用CatBoost准确预测陶的吸水性和曲强度. 关键因素包括粘土矿物质含量,SiO2度和燃烧温度,以优化节能制造.

科学领域:

材料科学
陶工程
人工智能

背景情况:

陶生产依赖于精确控制原材料和烧制条件,以达到所需的性能.
吸水 (WA) 和曲强度 (BS) 是由复杂的材料属性关系影响的关键性能指标.
传统的方法难以捕捉这些属性的复杂非线性相互作用.

研究的目的:

开发和评估用于预测陶属性的先进机器学习 (ML) 模型 (WA和BS).
确定影响WA和BS的主要原料和加工参数.
利用可解释的人工智能 (XAI) 为陶制剂和工艺优化提供数据驱动的洞察力.

主要方法:

分析包含312个陶样本的数据集,其中包括详细的成分和加工数据.
评估了四个整体ML模型,其中CatBoost被选为更高的预测准确度.
使用了SHapley添加式解释 (SHAP) 和部分依赖图表来解释模型.

主要成果:

CatBoost模型实现了WA和BS的高预测性能.
粘土矿物质含量 (~40%) 和SiO2度 (~30%) 是WA的主要驱动因素.
燃烧温度 (~35%) 和Al2O3含量 (~25%) 是BS的主要预测因素.
确定SiO2含量和BS最佳燃烧温度的关键值.

结论:

可解释的人工智能有效地预测陶性能并识别关键影响因素.
数据驱动的洞察力支持陶制造中的实时流程优化,产品一致性和能源效率.
这些发现符合已知的陶原理,同时提供了新的配方指导.

关键词:

曲强度陶瓦片发射阶段机器学习过程优化 SHAP分析水的吸收

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