基于随机森林和AdaBoost回归分析的高速激光层涂层质量的预测方法
Yifei Xv1, Yaoning Sun1, Yuhang Zhang1,2
1School of Mechanical Engineering, Xinjiang University, Urumqi 830017, China.
Materials (Basel, Switzerland)
|March 28, 2024
概括
这项研究开发了一个准确的AdaBoost (自适应提升) 模型,以预测Fe-Cr-Ni合金涂层的高速激光层质量. 该模型有效指导工艺参数调整,以提高涂层性能.
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
- 表面工程是什么?表面工程是什么?
背景情况:
- 高速激光层的质量对于维修组件的性能和寿命至关重要,例如煤矿中的液压支柱.
- 了解工艺参数对Fe-Cr-Ni合金涂层的影响对于优化涂层质量和确保可靠应用至关重要.
研究的目的:
- 研究高速激光涂层参数对Fe-Cr-Ni合金涂层质量的影响.
- 开发和比较用于准确评估涂层质量的预测模型.
- 确定影响涂层特性的关键过程参数.
主要方法:
- 使用了塔古奇直角方法 (L25(5^6) 来设计用于调查覆盖过程参数的实验.
- 开发了使用随机森林 (RF) 和AdaBoost (AB) 算法的预测模型,以将过程参数与涂层质量相关联.
- 执行特征重要性评估,以确定影响涂层性能的关键过程参数.
主要成果:
- 与随机森林 (RF) 模型相比,AdaBoost (AB) 模型显示出更高的预测准确性和对异常数据的敏感性.
- 扫描速度被确定为影响涂层高度和表面粗度的重要因素.
- 覆盖率关键控制了涂料的稀释比和近表面颗粒大小.
- 激光功率和扫描速度的调整有效地增强了微硬度和基板的热效应.
结论:
- AdaBoost算法是一种可行且准确的方法,用于预测高速激光覆盖质量,预测误差低于6%.
- 该研究提供了一个数据驱动的基础,用于优化过程参数在后续质量控制的激光覆盖操作.
- 像扫描速度和重叠率这样的关键参数可以被操纵,以实现要求高的应用程序所需的涂层特性.
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