A new FE post-processor for probabilistic fatigue assessment in the presence of defects and its application to AM parts. (August 2019)

• Record Type: Journal Article
• Authors: Romano, S.; Miccoli, S.; Beretta, S.
• Is Part Of: International journal of fatigue. Volume 125(2019)
• Page Start: 324
• Page End: 341
• Publication Date: 2019-08
• Subjects: Additive manufacturing -- Fatigue -- Defects -- Failure probability; Materials -- Fatigue -- Periodicals; Materials -- Fatigue; Periodicals; 620.1122
• DOI: 10.1016/j.ijfatigue.2019.04.008
• Languages: English
• ISSNs: 0142-1123

Abstract

Graphical abstract: Highlights: Novel tool for probabilistic fatigue assessment of parts with defects. Critical defect size and failure probability results validated on notched samples. The analytic model requires negligible time and computational effort. New method to evaluate the effect of both surface and internal defects presented. A fully-probabilistic analysis enables evaluating all major sources of variability. Abstract: Despite the disruptive benefits of Additive Manufacturing (AM), the application of this technology for safety-critical structural parts in aerospace is still far from being achieved and standardised. The necessity to comply with very strict reliability requirements is hindering this final step because of the large scatter and low reproducibility always associated with AM, especially in terms of fatigue strength. In this regard, manufacturing defects are the most important and complex issue, but several other sources of variability have an effect as well. The AM community and the main aerospace industries involved are starting to agree that damage-tolerant approaches are necessary and that probabilistic methods are best-suited to obtain reliable but not over-constrained assessments. To address this issue, the authors have developed ProFACE, a fully-probabilistic software that aims to robustly assess the fatigue strength and critical locations of complex components in the presence of defects. This paper presents the underlying concept, its implementation and early validation, and a simple application to a space component. The analytical formulation makes this tool ideal to evaluate very low failure probabilities with limited time and effort, which can provide valuable information to significantly improve part design and qualification.