聚合物的快速疲劳寿命预测通过组合构造性数学和基于AI的建模
T Barriere1, S Carbillet1, X Gabrion1
1Marie and Louis Pasteur University, SUPMICROTECH, UTBM, CNRS, Institute FEMTO-ST, F-25000 Besancon, France.
Polymers
|February 27, 2026
概括
现在可以更快地预测聚合物的疲劳寿命. 一个新的模型将构成数学与人工智能结合起来,减少对高周期疲劳设计的昂贵实验的依赖.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算科学 计算科学
背景情况:
- 疲劳寿命预测对于材料设计至关重要,但实验测试是昂贵和耗时的.
- 现有的构成性疲劳模型使用损伤演变和移动耐力表面整合低周期和高周期行为.
- 聚合物经常表现出类似于金属的S-N曲线特性,允许通过Coffin-Manson-Basquin模型进行参数化.
研究的目的:
- 开发一种计算效率高的方法,用于预测聚合物和性材料的高循环疲劳寿命.
- 利用机器学习 (ML) 加快疲劳寿命预测.
- 为了减少对广泛的实验性疲劳测试的依赖.
主要方法:
- 开发了一种构成性疲劳模型,将低周期和高周期行为与移动耐力表面相结合.
- 使用Coffin-Manson-Basquin参数表达的模型参数,适用于聚合物.
- 利用构成模型为机器学习模型生成高质量的训练数据.
主要成果:
- 组合的构成模型和ML方法显著提高了用于高周期疲劳预测的计算效率.
- 该模型成功生成了用于训练ML模型的数据,为传统方法提供了可行的替代方案.
- 该框架加速了聚合物和其他柔性材料的高循环疲劳设计过程.
结论:
- 一个先进的建模框架结合了构成性数学建模和人工智能,加速了高周期疲劳设计.
- 这种方法为实验性疲劳测试提供了一个计算效率高,数据驱动的替代方案.
- 该研究表明了将基于物理的模型与ML集成到材料设计优化中的潜力.
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