Fatigue life prediction of additively manufactured material: Effects of surface roughness, defect size, and shape

In this paper, the effects of process‐induced voids and surface roughness on the fatigue life of an additively manufactured material are investigated using a crack closure‐based fatigue crack growth model. Among different sources of damage under cyclic loadings, fatigue because of cracks originated...

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Bibliographic Details
Published in:Fatigue & fracture of engineering materials & structures 2018-07, Vol.41 (7), p.1602-1614
Main Authors: Yadollahi, A., Mahtabi, M.J., Khalili, A., Doude, H.R., Newman, J.C.
Format: Article
Language:English
Subjects:
Additive manufacturing
Component reliability
Crack closure
crack growth
Crack propagation
Failure mechanisms
FASTRAN
Fatigue cracks
Fatigue failure
Fatigue life
fatigue life prediction
Finishes
Fracture mechanics
Growth models
laser powder bed fusion
Life prediction
Mathematical models
Nickel base alloys
Superalloys
Surface roughness
Voids
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Summary:In this paper, the effects of process‐induced voids and surface roughness on the fatigue life of an additively manufactured material are investigated using a crack closure‐based fatigue crack growth model. Among different sources of damage under cyclic loadings, fatigue because of cracks originated from voids and surface discontinuities is the most life‐limiting failure mechanism in the parts fabricated via powder‐based metal additive manufacturing (AM). Hence, having the ability to predict the fatigue behaviour of AM materials based on the void features and surface texture would be the first step towards improving the reliability of AM parts. Test results from the literature on Inconel 718 fabricated via a laser powder bed fusion (L‐PBF) method are analysed herein to model the fatigue behaviour based on the crack growth from semicircular/elliptical surface flaws. The fatigue life variations in the specimens with machined and as‐built surface finishes are captured using the characteristics of voids and surface profile, respectively. The results indicate that knowing the statistical range of defect size and shape along with a proper fatigue analysis approach provides the opportunity of predicting the scatter in the fatigue life of AM materials. In addition, maximum valley depth of the surface profile can be used as an appropriate parameter for the fatigue life prediction of AM materials in their as‐built surface condition.
ISSN:8756-758X
1460-2695
DOI:10.1111/ffe.12799