Tridello A, Boursier Niutta C, Rossetto M, Berto F, Paolino D S
Department of Mechanical and Aerospace Engineering, Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129, Turin, Italy.
Department of Chemical Engineering, Materials and Environment, Università La Sapienza, 00185, Rome, Italy.
Sci Rep. 2023 Sep 15;13(1):15335. doi: 10.1038/s41598-023-40249-8.
The fatigue response of additively manufactured (AM) specimens is mainly driven by manufacturing defects, like pores and lack of fusion defects, which are mainly responsible for the large variability of fatigue data in the S-N plot. The analysis of the results of AM tests can be therefore complex: for example, the influence of a specific factor, e.g. the building direction, can be concealed by the experimental variability. Accordingly, appropriate statistical methodologies should be employed to safely and properly analyze the results of fatigue tests on AM specimens. In the present paper, a statistical methodology for the analysis of the AM fatigue test results is proposed. The approach is based on shifting the experimental failures to a reference number of cycles starting from the estimated P-S-N curves. The experimental variability of the fatigue strength at the reference number of cycles is also considered by estimating the profile likelihood function. This methodology has been validated with literature datasets and has proven its effectiveness in dealing with the experimental scatter typical of AM fatigue test results.
增材制造（AM）试样的疲劳响应主要由制造缺陷驱动，如孔隙和未熔合缺陷，这些缺陷是S-N图中疲劳数据存在较大离散性的主要原因。因此，对增材制造试验结果的分析可能很复杂：例如，特定因素（如构建方向）的影响可能会被试验的离散性所掩盖。相应地，应采用适当的统计方法来安全、恰当地分析增材制造试样的疲劳试验结果。在本文中，提出了一种用于分析增材制造疲劳试验结果的统计方法。该方法基于从估计的P-S-N曲线开始，将试验失效次数转换为参考循环次数。通过估计轮廓似然函数，还考虑了参考循环次数下疲劳强度的试验离散性。该方法已通过文献数据集进行了验证，并已证明其在处理增材制造疲劳试验结果典型的试验离散性方面的有效性。
