Abstract
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.
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