在增材制造中通过最大后期优化实现基于原子的疲劳性质规范化
Mustafa Awd1,2, Lobna Saeed3, Frank Walther4
1Institute for Informatics and Automation (IIA), Bremen City University of Applied Sciences (HSB), Flughafenallee 10, 28199 Bremen, Germany.
Materials (Basel, Switzerland)
|July 30, 2025
概括
这项研究引入了一个新的框架来预测3D打印金属的疲劳强度,如AlSi10Mg和Ti-6Al-4V. 它通过考虑材料微观结构和缺陷来准确预测疲劳性能,这对于增材制造至关重要. 关键词:疲劳强度,3D打印金属,增材制造,微观结构.
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:
- 像激光粉床融合 (L-PBF) 这样的增材制造 (AM) 工艺引入了独特的微观结构特征和缺陷 (多孔性,残余应力).
- 预测AM合金中的疲劳强度是具有挑战性的,因为它具有复杂的工艺结构和性能关系.
- 现有的模型往往难以捕捉AM材料固有的异质性.
研究的目的:
- 开发一个多尺度,微观结构意识的框架,用于预测AM合金中的疲劳强度分布.
- 量化AM特有的微结构特征对疲劳性能的影响.
- 为AM金属的疲劳设计提供一个数据高效和物理可解释的途径.
主要方法:
- 密度函数理论 (DFT) 的整合,用于统一的能量计算.
- 在模量测量中使用仪器缩入.
- 应用贝叶斯推理和基于MAP的统计模型来预测疲劳.
- 与实验高周期和非常高周期疲劳 (HCF/VHCF) 数据的验证.
主要成果:
- 该框架准确地预测了L-PBF AlSi10Mg和Ti-6Al-4V的疲劳强度分布,并得到了验证的结果.
- 预测的沃勒 (S-N) 曲线和巴黎裂生长参数涵盖了超过92%的实验数据.
- 全球灵敏度分析确定孔隙性和残留应力是疲劳强度差异的主要贡献者 (>70%).
结论:
- 开发的框架提供了一个强大的,准确的方法来预测增材制造金属的疲劳强度.
- 它强调了工艺诱导的缺陷和微结构异质性在AM疲劳性能中的关键作用.
- 该方法可扩展到其他AM合金和工艺变体,促进微观结构知情设计.
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