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基于机器学习的预测Epoxy复合材料涂层的tribological特性
Han Yan1, Junling Tan1, Hui Chen1
1School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, China.
Polymers
|February 13, 2025
概括
机器学习准确地预测了涂层 tribology. 梯度增强回归表现出色,确定酸盐含量是工程应用的关键.
科学领域:
- 材料科学 材料科学 材料科学
- 部落学 (tribology) 是一个学科.
- 计算工程 计算工程 计算工程
背景情况:
- 涂料的tribological特性对于材料性能至关重要.
- 非常希望以非破坏性的方式评估这些特性.
- 机器学习为此类评估提供了一个有前途的方法.
研究的目的:
- 评估填充剂含量和网格大小对赛里/环氧复合涂料三元学性质的影响.
- 为了评估预测这些属性的六个机器学习算法的性能.
- 确定影响部落学行为最有影响的因素.
主要方法:
- 使用了6个机器学习算法.
- 采用了来自不同填充剂含量 (10-30重量%) 和网状尺寸的赛里/环氧复合涂料的数据.
- 模型的准确性被评估为预测摩擦系数和磨损率.
主要成果:
- 梯度增强回归显示出卓越的精度 (93.7%的摩擦系数,85.7%的磨损率) 和稳定性.
- 这个模型的性能优于决策树,极端梯度增强和高斯过程回归.
- 矿含量被确定为影响部落学性质的最重要因素.
结论:
- 机器学习,特别是渐变增强回归,对于评估复合涂料的tribological特性是有效的.
- 酸盐含量是涂层 tribological 性能的主要决定因素.
- 这项研究为工程应用石/环氧复合涂料提供了指导.
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