不同压力比率的疲劳寿命数据融合方法,基于应变能量密度
Changyin Wang1, Jianyao Yao1, Xu Zhang1
1College of Aerospace Engineering, Chongqing University, Chongqing 400044, China.
Materials (Basel, Switzerland)
|June 27, 2024
概括
一种新的数据融合方法改善了对数据有限的材料的疲劳寿命预测. 这种方法结合了各种应力比率下的扭力疲劳数据,提高了准确性并减少了用于概率材料表征的数据分散.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 疲劳分析 疲劳分析
背景情况:
- 准确的疲劳寿命预测对于材料设计至关重要.
- 小样本大小和不同的应力比率使概率性疲劳分析复杂化.
- 现有的方法在数据分散和有限的数据集方面扎.
研究的目的:
- 开发一种数据融合方法,用于不同应力比率下的扭力疲劳寿命.
- 用小样本数据增强对概率性疲劳特征的评估.
- 为了提高疲劳寿命预测模型的准确性.
主要方法:
- 利用有限元分析计算疲劳应变能量密度.
- 进行扭力疲劳测试,以创建一个全面的数据库.
- 开发了Wt-Nf曲线和P-Wt-Nf预测模型.
- 应用了基于能源方法的数据融合技术.
主要成果:
- 在不同的应力比率下,疲劳寿命可以用单个Wt-Nf曲线表示.
- 数据融合在有的标本中将标准偏差降低了高达38.5%.
- 实现了更准确的P-Wt-Nf曲线,反映了概率性质.
- 超过85%的数据点位于±2的散射频段内.
结论:
- 拟议的数据融合方法有效地扩展了小样本数据,并减少了分散.
- 这种方法可以更可靠地进行材料疲劳性质的概率性表征.
- 跨应力比率的Wt-Nf模型的同质性支持数据融合.
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