Journal
INTERNATIONAL JOURNAL OF FATIGUE
Volume 160, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ijfatigue.2022.106884
Keywords
Extremum value statistics; Size effect; Fatigue strength; Additive manufacturing; Defect-tolerant assessment
Funding
- National Natural Science Foundation of China [11972110]
- Sichuan Science and Technology Program [2022JDJQ0024]
- Guangdong Basic and Applied Basic Research Foundation [2021B1515140030]
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Despite the advantages of rapid prototyping, Additive Manufacturing (AM) technique faces limitations in engineering applications due to weak fatigue performance and lacking of design rules. This study explores the size effect and fatigue performance scatter in AM materials using defect data from CT scans and experimental data. Predictive fatigue curves for AM materials are extrapolated based on the relationship among defects, fatigue strength, and fatigue curves. The method is validated through testing different AlSi10Mg specimens and performing defect-tolerant assessments for AM parts.
Despite the significant advantages such as rapid prototyping of complex structures, engineering application of Additive Manufacturing (AM) technique has been limited due to the weak fatigue performance and lacking of fatigue design rules of AM parts. Particularly, this application relies on the improvement of AM processing technique and integrity assessment of AM parts, the latter usually demands costly and time-consuming fatigue tests, especially for full-scale tests. Accordingly, the size effect and fatigue performance scatter in AM materials are explored on basis of defect data scanned by CT and experimental data in this work. Firstly, the distribution of maximum defects of AM materials under size effect is extrapolated via extreme value statistics theory; then a strength altering factor is proposed to characterize the effect of defects on fatigue scatter and size effect. From essential structure of fatigue curves, the relationship among defects, fatigue strength and fatigue curves are quantitatively analysed to extrapolate the probabilistic fatigue curves for AM materials; finally, three series of AlSi10Mg specimens with different processing parameters and gauge volumes prove the method effectiveness that the location and scale of fatigue curves is successfully predicted, and defect-tolerant assessment for AM parts is performed by employing the predicted fatigue curves.
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