评估微结构和机器学习预测模型,用于可持续的牛增强基复合材料的摩擦钻探
Rajesh Jesudoss Hynes Navasingh1,2, R Sankaranarayanan3, Priyanka Mishra4
1Faculty of Mechanical Engineering, Opole University of Technology, Proszkowska 76, Opole, 45-758, Poland. findhynes@yahoo.co.in.
Scientific reports
|August 5, 2025
概括
矩阵复合材料 (AMC) 的摩擦钻探通过控制线速度和料速率来优化,以获得更好的长和圆度. 微观结构分析揭示了影响表面质量的不同区域.
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
- 部落学 (tribology) 是一个学科.
背景情况:
- 轻量级汽车应用越来越多地使用矩阵复合材料 (AMC).
- 摩擦钻探提供了一个环保的替代方案,用于制造AMC的孔.
- 在AMC中加强绿牛的强化带来了独特的加工挑战.
研究的目的:
- 调查过程变量对AMC摩擦钻井的影响.
- 分析线速度,料速率,工件厚度和钻孔直径的影响.
- 评估长,厚和圆度作为关键输出响应.
主要方法:
- 在AMC上进行了摩擦钻探实验,使用牛增强.
- 进行了微观结构检查,以分析灌木形成区域.
- 表面地形分析评估了机械和热影响.
- 机器学习模型 (RF,MLP,GPR,SVM) 用于响应预测.
主要成果:
- 较高的线速度和较低的料速率对长产生了积极的影响.
- 低于最佳的参数导致圆度降低和谷粒大小变化.
- 不同的微观结构区域 (头叶,尾叶,上临界区域) 显示出不同的谷物特性.
- 表面质量与热量产生和材料变形相关;最佳条件产生更好的质量.
结论:
- 过程参数的优化对于在摩擦钻孔的AMC中实现所需的 bushing 尺寸和圆度至关重要.
- 微观结构和表面地形分析为结合的热力学效应提供了洞察力.
- 机器学习模型显示了预测强化AMC中的摩擦钻井结果的潜力.
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