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图形特征 基于结构的深度学习模型用于预测AM合金的疲劳寿命
Hao Wu1, Anbin Wang1, Zhiqiang Gan1
1School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092, China.
Materials (Basel, Switzerland)
|January 11, 2025
概括
本研究引入了一种新的机器学习模型,该模型将数值数据转换为图形表示,显著改善循环负荷下关键组件的疲劳寿命预测.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算机科学 计算机科学
背景情况:
- 疲劳故障是周期性负载下组件安全的关键问题.
- 当前的机器学习 (ML) 模型在从纯数值输入中获得有限的信息中扎.
- 准确预测疲劳寿命对于可靠的工程应用是必不可少的.
研究的目的:
- 开发一种新的机器学习模型,用于增强疲劳寿命预测.
- 克服传统的ML模型的局限性,这些模型仅依赖于数值特征.
- 通过结合图形数据表示来改进疲劳失效的分析.
主要方法:
- 开发了一个基于卷积神经网络 (CNN) 的新ML模型.
- 数字特征被转化为图形表示,使用Shapley增量解释和Pearson相关系数分析.
- 整合了注意力机制,以专注于基于图像的输入中的关键数据区域.
主要成果:
- 与传统的ML模型相比,拟议的模型显示出更高的预测准确性.
- 使用激光粉床融合制造的金属实验数据的验证证实了该模型的有效性.
- 图形特征转换丰富了输入数据,导致更好的疲劳寿命预测.
结论:
- 基于CNN的ML模型在疲劳寿命预测方面取得了重大进展.
- 将数字特征转换为图形特征可以提高对疲劳失效机制的理解.
- 这种方法为评估组件的操作安全提供了更准确和更强大的方法.
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