干滑式tribological特征评估和预测TiB2-CDA/Al6061混合复合材料练习机器学习方法
Amit Aherwar1, Anamika Ahirwar2, Vimal Kumar Pathak3
1Department of Mechanical Engineering, Madhav Institute of Technology and Science (Deemed University), Gwalior, 474005, India.
Scientific reports
|May 14, 2025
概括
这项研究开发了Al6061混合复合材料与二化物 (TiB2) 和牛灰 (CDA). 机器学习,特别是高斯过程回归 (GPR),准确地预测了磨损和摩擦,优化了材料性能.
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.
- 机械工程 机械工程
背景情况:
- 合金6061 (Al6061) 是一种广泛使用的材料,但对于苛刻的应用需要改进.
- 混合复合材料通过结合不同类型的钢筋提供量身定制的性能.
- 了解 tribological 行为对于工程元件中的材料耐用性至关重要.
研究的目的:
- 制造和评估用二化物 (TiB2) 和牛灰 (CDA) 增强的Al6061-合金混合复合材料.
- 在不同的条件下研究三极学性能 (磨损和摩擦系数).
- 开发和验证用于预测复合材料磨损行为的机器学习模型.
主要方法:
- 混合造技术用于复合材料制造.
- 用于干式滑动磨损测试的针盘式三角计.
- 分析参数效应的全因数实验设计 (增强率,负载,速度,距离).
- 扫描电子显微镜 (SEM) 用于磨损机制分析.
- 机器学习算法 (RF,SVM,GPR,GBTA) 用于预测建模.
主要成果:
- 成功制造了TiB2-CDA/Al6061混合复合材料.
- 磨损和摩擦系数 (COF) 受到钢筋含量,负载,速度和距离的显著影响.
- 确定的主要磨损机制包括粘附,磨损,氧化和分层.
- 机器学习模型有效地预测了磨损和COF的损失.
- 与其他模型相比,高斯过程回归 (GPR) 显示出更高的预测准确性.
结论:
- TiB2和CDA增强剂增强了Al6061.1.的三元学特性.
- 机器学习,特别是GPR,为预测和优化先进复合材料的 Tribological 性能提供了一个强大的工具.
- 开发的模型可以减少对广泛实验测试的需求,加速材料开发.
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