A microstructure-based creep model for additively manufactured nickel-based superalloys. (1st February 2022)

• Record Type: Journal Article
• Authors: Wu, S.; Song, H.Y.; Peng, H.Z.; Hodgson, P.D.; Wang, H.; Wu, X.H.; Zhu, Y.M.; Lam, M.C.; Huang, A.J.
• Is Part Of: Acta materialia. Volume 224(2022)
• Publication Date: 2022-02-01
• Subjects: Creep -- Modelling -- Cavitation -- Additive manufacturing -- Nickel-based superalloy; Materials -- Periodicals; Materials science -- Periodicals; Materials -- Mechanical properties -- Periodicals; Metallurgy -- Periodicals; Chemistry, Inorganic -- Periodicals; 620.112
• DOI: 10.1016/j.actamat.2021.117528
• Languages: English
• ISSNs: 1359-6454

Abstract

Abstract: Creep fracture is a general failure mode for nickel-based superalloy components made by additive manufacturing (AM). However, the existing creep models originally built for conventional superalloys cannot explain the creep rate acceleration caused by fast cavitation, which relates to the AM-specific microstructural features, in particular the porosity, columnar grain structure and compositional inhomogeneity. In this work, a microstructure-based creep model is developed for additively manufactured Ni-based superalloys that specifically considers the correlation between cavity formation kinetics and creep deformation behaviour. The applications to IN718 and IN738LC demonstrate the model can well simulate the creep process at all stages and the creep lifetime for a wide range of microstructures. After calibration, the effects of the aforementioned AM-produced microstructure features on creep performance were discussed. The simulated data in cooperation with experimental results shows a significant increment in creep rate and a decrement in lifetime due to fast cavitation. The application of the model can simulate the creep performance of AM alloys and provide a quantitative method to estimate the influence of AM-produced microstructure features on creep performance. Moreover, the time-consuming creep testing procedure can be reduced once the model is calibrated with experimental data. Graphical abstract: Image, graphical abstract