使用机器学习回归模型对B4C和花岩粉填充Al 6082复合材料进行三元信息分析和预测
Amit Aherwar1, Anamika Ahirwar2, Vimal Kumar Pathak3
1Department of Mechanical Engineering, Madhav Institute of Technology and Science (Deemed University), Gwalior, 474005, India.
Scientific reports
|July 27, 2025
概括
机器学习准确地预测B4C-花岩复合材料的磨损和摩擦,减少了广泛的物理测试. 模糊逻辑模型在预测磨损和摩擦系数方面表现出卓越的性能.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 部落学 (tribology) 是一个学科.
背景情况:
- 评估材料磨损的传统方法耗时且昂贵.
- 机器学习 (ML) 提供了一个有前途的替代方案,用于预测先进材料的机械和 tribological 属性,如基于 Al 的复合材料.
研究的目的:
- 将实验数据与ML算法相结合,准确预测B4C花岩复合材料的磨损和摩擦系数 (COF).
- 为了方便设计具有更好的磨损性能的材料.
主要方法:
- 通过混合造合成的复合材料.
- 在干式滑动条件下 (81个样本) 使用针盘式三角计评估磨损行为.
- 应用了七个监督回归ML模型,并对比分析进行了超参数调整.
主要成果:
- 随着负载的增加和强化百分比的降低,磨损损失会增加.
- 模糊逻辑模型实现了最高的预测准确性:磨损R2为0.9638和COF为0.9833的R2.
- 强化百分比与磨损损失 (-0.57) 和COF (-0.50) 有着强烈的负相关性.
结论:
- 机器学习模型,特别是模糊逻辑模型,可以准确预测部落学行为,减少对广泛物理测试的需求.
- 该研究提供了一种数据驱动的方法,用于优化B4C-花岩复合材料组成,以提高耐磨性.
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