为APS飞行中的粒子温度和速度预测,考虑火电极衰老的组合方法
K R Yu1, C V Cojocaru1, F Ilinca1
1National Research Council of Canada, AST, Boucherville, QC Canada.
Journal of thermal spray technology
|July 31, 2023
概括
机器学习模型预测大气等离子体喷雾 (APS) 过程中的飞行中的粒子温度和速度,并考虑电极老化. 随机森林模型的性能优于梯度增强,特别是在使用时间序列对静止数据进行差异化时.
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
- 数据科学数据科学数据科学
背景情况:
- 大气等离子喷雾 (APS) 过程涉及输入参数和飞行中的粒子特征之间的复杂非线性关系,这对涂层质量至关重要.
- 火电极的老化显著影响了这些关系,为过程控制和预测增加了另一层复杂性.
- 机器学习 (ML) 提供了一种强大的方法来建模和管理这些复杂的,工业过程中的非线性相互作用.
研究的目的:
- 应用集体ML方法来预测APS中的飞行粒子温度和速度,考虑到火电极老化的影响.
- 在这种情况下,比较随机森林 (RF) 和梯度提升 (GB) 在特征选择和预测建模方面的有效性.
- 调查时间序列分析技术的实用性,以提高APS飞行中的粒子特征的预测准确性.
主要方法:
- 收集了APS输入参数,飞行中的粒子特性和电极使用时间的实验数据.
- 使用随机森林 (RF) 和梯度提升 (GB) 进行特征排名,选择和预测模型开发.
- 探索了两个时间序列嵌入策略:直接的属性/目标嵌入和静态差异化后的嵌入.
主要成果:
- 与GB相比,RF表现出优越的性能,能够同时预测粒子速度和温度,同时捕捉目标间的相互作用.
- 当APS数据被视为时间序列时,RF和GB模型都显示出更好的性能,这突显了时间依赖的重要性.
- 预处理时间序列数据,使用差分来实现静止,进一步提高了射频模型的预测准确度.
结论:
- 集体ML方法,特别是随机森林,对于预测APS中的飞行粒子特征是有效的,即使电极老化.
- 将APS数据视为时间序列,尤其是在应用静态差异化后,可以显著提高预测准确性.
- 这些发现表明,RF是一个比GB更合适的算法,可以同时预测APS中的多个相互作用的飞行中的粒子特性.
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