基于的金属间合金的低周期疲劳寿命预测使用机器学习和有限元素分析
Qiwen Xu1, Guoqian Song1, Xingwu Li2
1College of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China.
Materials (Basel, Switzerland)
|May 7, 2025
概括
这项研究研究了Ti2AlNb合金的高温,低循环疲劳. 极端学习机器 (ELM) 在疲劳寿命评估方面表现出卓越的预测准确性.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算材料科学科学 计算材料科学
背景情况:
- 基于Ti2AlNb的合金对于高温结构应用至关重要.
- 了解低周期疲劳 (LCF) 对组件可靠性至关重要.
- 现有的预测模型需要对这些先进的合金进行验证和改进.
研究的目的:
- 在高温下探索Ti2AlNb合金组件的LCF行为.
- 用西格的理论和莱马特的模型与实验数据对疲劳生命模拟进行验证.
- 评估用于预测LCF寿命的机器学习算法 (LSTM,ELM,PLS).
主要方法:
- 应用西格的疲劳寿命理论和改进的莱梅特损伤进化模型.
- 高温实验性疲劳寿命测试用于验证.
- 长短期记忆 (LSTM),极端学习机器 (ELM) 和部分最小平方 (PLS) 算法的比较分析.
主要成果:
- 实验性疲劳测试证实了模拟模型的准确性.
- 与LSTM和PLS相比,ELM在预测高温LCF寿命方面表现优越.
- 该研究为Ti2AlNb合金提供了经过验证和高效的预测框架.
结论:
- 西格尔的理论和莱马特的模型,当通过实验验证时,可以准确地预测Ti2AlNb合金中的LCF寿命.
- 极端学习机器 (ELM) 为预测这些组件的LCF寿命提供了一种强大而实用的方法.
- 这项研究建立了一种有效的方法来评估基于Ti2AlNb的结构材料的疲劳耐用性.
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