Awd Mustafa, Saeed Lobna, Walther Frank
Institute for Informatics and Automation (IIA), Bremen City University of Applied Sciences (HSB), Flughafenallee 10, 28199 Bremen, Germany.
Testia GmbH, Airbus Group, Cornelius-Edzard-Straße 15, 28199 Bremen, Germany.
Materials (Basel). 2025 Jul 15;18(14):3332. doi: 10.3390/ma18143332.
This work presents a multiscale, microstructure-aware framework for predicting fatigue strength distributions in additively manufactured (AM) alloys-specifically, laser powder bed fusion (L-PBF) AlSi10Mg and Ti-6Al-4V-by integrating density functional theory (DFT), instrumented indentation, and Bayesian inference. The methodology leverages principles common to all 3D printing (additive manufacturing) processes: layer-wise material deposition, process-induced defect formation (such as porosity and residual stress), and microstructural tailoring through parameter control, which collectively differentiate AM from conventional manufacturing. By linking DFT-derived cohesive energies with indentation-based modulus measurements and a MAP-based statistical model, we quantify the effect of additive-manufactured microstructural heterogeneity on fatigue performance. Quantitative validation demonstrates that the predicted fatigue strength distributions agree with experimental high-cycle and very-high-cycle fatigue (HCF/VHCF) data, with posterior modes and 95 % credible intervals of σ^fAlSi10Mg=86-7+8MPa and σ^fTi-6Al-4V=115-9+10MPa, respectively. The resulting Woehler (S-N) curves and Paris crack-growth parameters envelop more than 92 % of the measured coupon data, confirming both accuracy and robustness. Furthermore, global sensitivity analysis reveals that volumetric porosity and residual stress account for over 70 % of the fatigue strength variance, highlighting the central role of process-structure relationships unique to AM. The presented framework thus provides a predictive, physically interpretable, and data-efficient pathway for microstructure-informed fatigue design in additively manufactured metals, and is readily extensible to other AM alloys and process variants.
这项工作提出了一个多尺度、微观结构感知框架，用于预测增材制造（AM）合金中的疲劳强度分布，具体而言，是通过整合密度泛函理论（DFT）、仪器化压痕和贝叶斯推理来预测激光粉末床熔融（L-PBF）的AlSi10Mg和Ti-6Al-4V合金中的疲劳强度分布。该方法利用了所有3D打印（增材制造）工艺共有的原理：逐层材料沉积、工艺诱导的缺陷形成（如孔隙率和残余应力）以及通过参数控制进行微观结构定制，这些因素共同将增材制造与传统制造区分开来。通过将DFT推导的内聚能与基于压痕的模量测量以及基于MAP的统计模型相联系，我们量化了增材制造的微观结构异质性对疲劳性能的影响。定量验证表明，预测的疲劳强度分布与实验高周和超高周疲劳（HCF/VHCF）数据一致，AlSi10Mg合金的疲劳强度后验模态和95%可信区间为σ^fAlSi10Mg = 86 - 7 + 8MPa，Ti-6Al-4V合金为σ^fTi-6Al-4V = 115 - 9 + 10MPa。所得的威勒（S-N）曲线和巴黎裂纹扩展参数涵盖了超过92%的实测试样数据，证实了其准确性和稳健性。此外，全局敏感性分析表明，体积孔隙率和残余应力占疲劳强度方差的70%以上，突出了增材制造特有的工艺-结构关系的核心作用。因此，所提出的框架为增材制造金属中基于微观结构的疲劳设计提供了一条可预测、具有物理可解释性且数据高效的途径，并且很容易扩展到其他增材制造合金和工艺变体。
