用可解释的机器学习预测结造中的孔隙性
Rafael Gaspar Bessa de Oliveira1, Jones Yudi1, Edson Paulo da Silva1
1College of Technology, Department of Mechanical Engineering, University of Brasília, Federal District, Brasília 70910-900, Brazil.
ACS omega
|August 4, 2025
概括
机器学习模型准确地预测了结造材料中的多孔性. CatBoost获得了最好的结果,固体负载被确定为影响毛孔性的关键因素.
科学领域:
- 材料科学 材料科学 材料科学
- 制造业 工程 制造工程
- 计算材料科学科学 计算材料科学
背景情况:
- 结造是创建具有可调节性质的多孔材料的关键方法.
- 从工艺参数预测孔隙性是复杂的,对材料设计至关重要.
- 现有的方法缺乏优化结造材料所需的精度.
研究的目的:
- 开发和评估机器学习模型,用于预测冷成型中的多孔性.
- 使用可解释的人工智能识别影响毛孔度的关键过程参数.
- 为了提高冷造材料的设计和优化.
主要方法:
- 利用来自252篇关于陶,聚合物和复合材料的研究论文的实验数据.
- 应用机器学习算法:CatBoost,随机森林和XGBoost.
- 为了模型的可解释性,使用了沙普利增量解释 (SHAP).
主要成果:
- 在测试组中,CatBoost模型实现了最高的预测准确性,测试组的R2为0.81.
- SHAP分析发现固体负载是最有影响力的参数.
- 较低的固体负载与更高的预测孔隙度相关,与理论预期保持一致.
结论:
- 机器学习,特别是CatBoost,提供了一种强大的工具,用于预测冷成型中的多孔性.
- 可解释人工智能 (SHAP) 提供了对材料行为和参数影响的关键见解.
- 这种方法可以指导实验设计,并优化特定应用的材料特性.
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