In-Situ Fatigue Life Prediction with Simulated Defects for Additive Manufacturing Process

In-situIn-situ detection of defectsDefects generated during additive manufacturing (AMAdditive manufacturing (AM)) and the ability to predict part performance in real-time are areas of active research. Ideally, a real-time fatigue analysis would be performed during AMAdditive manufacturing (AM) printing, using real-time detection of defectsDefects, and stopping the print if requirements are not met. Since in-situIn-situ defectDefects detection technologyTechnology is currently still in development, an alternate approach is used here to demonstrate fatigue prediction during printing. Fatigue lifeFatigue life is calculated in-situIn-situ for the current build height, by assuming a defectDefects population that was generated from statistical data characterized by ex-situEx-situ computed tomography (CT) scans of relevant builds. The printing of an axial fatigue test coupon was used to demonstrate this approach. A large number of defectsDefects were generated with various defectDefects sizes and geometrical locations within the part. This simulated defectDefects distribution is fed layer-by-layer to a model simulating the build processProcess. The fatigue load spectra expected during service are applied to the current build height to calculate fatigue crack growthFatigue crack growth due to defectDefects until part failure. Variation of materialMaterials properties through the partially built part and their influence on service life are modelled by probabilistic fracture mechanicsProbabilistic fracture mechanics. It is shown that fatigue lifeFatigue life reduces with an increase of build height, since the number of defectsDefects increases with build height. The fatigue failure is most sensitive to large surface or near surface (sub-surface) defectsDefects located at areas experiencing high amplitude cyclic stress.