在微观结构表示上使用图形神经网络进行材料疲劳预测
Akhil Thomas1,2, Ali Riza Durmaz3,4, Mehwish Alam5
1Fraunhofer Institute for Mechanics of Materials, Freiburg, Germany. akhil.thomas@iwm.fraunhofer.de.
Scientific reports
|August 2, 2023
概括
在多晶体中预测疲劳损伤是具有挑战性的. 图形神经网络有效地识别铁钢中易受损坏的颗粒,优于其他模型,并揭示疲劳失败的微观结构驱动因素.
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:
- 预测高循环疲劳下的多晶体疲劳损伤是一个持续的挑战.
- 识别在循环负荷下容易发生塑性变形的颗粒对于理解材料故障至关重要.
- 现有的方法难以准确地捕捉复杂的微观结构中的局部损伤启动.
研究的目的:
- 开发一种用于预测多晶体局部疲劳损伤的新方法.
- 利用图形神经网络 (GNN) 进行铁钢的粒度损伤分类.
- 确定有效的数据表示和GNN模型,以了解疲劳损伤机制.
主要方法:
- 来自实验数据的微纹理和损伤地图被转录为微结构图形表示.
- 谷物被表示为节点,边缘连接相邻的谷物.
- 图形卷积网络 (GCNs) 应用于二进制粒度损伤分类.
主要成果:
- 图形卷积网络实现了0.72的平衡精度和0.34.1的F1得分.
- GCN显著优于现象学晶体可塑性 (+68%) 和传统机器学习 (+17%) 模型.
- 解释性分析突出了影响疲劳损伤开始的关键粒度和特征.
结论:
- 在多晶体中,GNN为预测疲劳损伤的启动提供了一个强大的工具.
- 这种方法可以揭示底层的微观结构驱动力和疲劳失败的机制.
- 微结构图框架有助于将先进的机器学习技术应用于材料科学问题.
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