粉末床熔融金属增材制造的疲劳萌生寿命不确定性量化

中文摘要 AI 辅助

预测疲劳寿命并量化其不确定性，对基于激光的粉末床熔融增材制造所生产关键部件的合格鉴定至关重要。我们提出一种框架，可将微观结构与缺陷不确定性直接传播至特定部件的疲劳萌生寿命分布。具体而言，通过电子背散射衍射和显微计算机断层扫描扫描数据获取微观结构与缺陷表征，这些数据进而为三个基于物理的模拟提供信息，生成影响疲劳的量：弹性能量释放率、裂纹路径处的表面能以及疲劳指示器参数。考虑到这些量的不确定性，以及因共享基础微观结构而产生的它们之间的高度相关性，我们推导得出疲劳萌生寿命的闭式概率密度函数。这提供了一种解析分布，而非保守的确定性预测，能为增材制造部件的合格鉴定与部署提供更明智的决策依据。将该框架应用于EOS M290激光粉末床熔融机生产的316L不锈钢部件，我们发现晶粒尺寸和孔洞分布均会影响疲劳萌生寿命分布。具体而言，在总孔洞体积分数固定的情况下，较大的晶粒尺寸会导致疲劳萌生寿命略有降低；而对于相同的总体积，大量小孔洞的存在比少量大孔洞更有利于延长疲劳寿命。

英文摘要

Predicting fatigue life with quantified uncertainties is essential for the qualification of critical components produced by laser-based powder bed fusion additive manufacturing. We present a framework that propagates microstructure and defect uncertainties directly to a fatigue initiation life distribution for a specific part. In particular, microstructure and defect characterizations are obtained from electron backscatter diffraction and micro-computed tomography scan data, which in turn inform three physics-based simulations yielding the fatigue-affecting quantities: the elastic energy release rate, the surface energy along the crack path, and the fatigue indicator parameter. Accounting for the uncertainties in these quantities and the high correlations among them due to the shared underlying microstructure, we derive a closed-form probability density function for the fatigue initiation life. This provides an analytical distribution instead of conservative deterministic predictions and enables more informed decision making for the qualification and deployment of additively manufactured components. Applying the framework to 316L stainless steel parts produced by an EOS M290 laser powder bed fusion machine, we find that both grain sizes and void distributions influence the fatigue initiation life distribution. Specifically, for a fixed total void volume fraction, larger grain sizes cause a marginal reduction in fatigue initiation life, and a population of many small voids is more favorable for fatigue life than fewer, larger voids of equivalent total volume.

发表机构

• University of California San Diego（加州大学圣地亚哥分校）
• University of Michigan（密歇根大学）

机构由 AI 辅助整理，请以论文原文为准。