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有效预测疲劳损伤分析碳纤维复合材料使用多时间尺度分析和机器学习

Satoru Yoshimori¹, Jun Koyanagi², Ryosuke Matsuzaki¹

¹Department of Mechanical and Aerospace Engineering, Tokyo University of Science, 2641 Yamazaki, Noda 278-8510, Japan.

|December 17, 2024

概括

本研究介绍了一种基于的模型,用于评估碳纤维增强塑料 (CFRP) 的疲劳损伤. 通过使用LightGBM和多时间尺度分析进行优化,计算成本在不牺牲准确度的情况下降低了90%以上.

科学领域:

材料科学材料科学材料科学
机械工程机械工程
计算力学计算力学计算力学

背景情况:

碳纤维增强塑料 (CFRP) 具有出色的机械性能,但表现出复杂的疲劳损伤机制.
精确评估CFRP中疲劳损伤对于结构完整性和安全至关重要.
在CFRP中评估疲劳损伤的现有方法可能是计算密集的,需要详细的加载历史.

研究的目的:

提出并演示基于的CFRP损害评估模型.
为了解决基于的模型的计算强度,用于高周期疲劳分析.
使用机器学习优化特征变量,以提高预测准确度和减少分析时间.

主要方法:

开发了一个基于的损害评估模型,利用独立于负载历史的热容量测量.
通过将循环负载替换为常量位移负载来降低计算成本,实现了多时间尺度分析.
使用LightGBM机器学习模型和响应表面方法 (RSM) 优化特征变量.

主要成果:

基于的模型准确地评估了CFRP的疲劳损伤,而不需要加载历史.
多时间尺度分析显著降低了计算需求 (分析时间减少了90%以上).
在特征变量优化方面,LightGBM的表现优于RSM,实现了75%较低的根平均平方误差.

结论:

开发的基于的模型为评估CFRP疲劳损伤提供了有效的方法.
多时间尺度分析与LightGBM优化相结合,为高周期疲劳分析提供了计算高效的方法.
这种优化的方法提高了CFRP应用先进疲劳损伤评估的实用性.

关键词:

在CFRP的基础上.轻GBMM 轻GBM 轻GBM 轻GBM 复合材料复合材料是一种复合材料.进入的过程中,疲劳疲劳疲劳疲劳疲劳疲劳有限元分析是有限元分析.机器学习是机器学习.数值分析是指数值分析.响应表面方法响应表面方法

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