Acquisition of 3D Data for Prediction
of Monotonic and Cyclic Properties
of Superalloys
McLean P. Echlin, William C. Lenthe, Jean-Charles Stinville,
and Tresa M. Pollock
1 Superalloys and Fatigue
Turbine engines have continuously improved in performance and efficiency due
to advances in materials and coatings, combined with the application of advanced
thermomechanical, heat transfer, and aerodynamic design methodologies. Turbine
disks are among the most safety-critical components in an aircraft engine and have
therefore been the subject of extensive development and characterization studies
[1–3]. Polycrystalline nickel-base superalloys are the typical material of choice
for turbine disks due to their high fatigue resistance and ultimate tensile strength
and good thermomechanical and thermochemical stability at elevated temperatures
[4, 5]. Powder metallurgy processing is used to produce disk components with
highly controlled grain size distributions, controlled inclusion (carbide and nitride)
content via powder stock filtering, and near net shape part geometries [1–5].
Inclusion content and grain structure have both been shown to be influential in the
fatigue life of disk alloys [6, 7]. An improved predictive capability of the mechanical
performance of these alloys is required to enhance life prediction and reliability as
well as guide the development of new alloys and processing paths.
Predicting fatigue properties of superalloys is particularly challenging, due to
the localized character of the plasticity during cycling and its strong dependence
on material structure. The schematic in Fig. 1 shows the microstructure of a
polycrystalline superalloy, containing annealing twins as well as the L1 2 γ
precipitate strengthening phase, and the approximate length scales at which they
M. P. Echlin () · W. C. Lenthe · J.-C. Stinville · T. M. Pollock
The Materials Department, University of California Santa Barbara, Santa Barbara, CA, USA
e-mail: mechlin@ucsb.edu; jeancharles_stinville@ucsb.edu; tresap@ucsb.edu
© Springer Nature Switzerland AG 2020
S. Ghosh et al. (eds.), Integrated Computational Materials Engineering (ICME),
https://doi.org/10.1007/978-3-030-40562-5_1
1
of Monotonic and Cyclic Properties
of Superalloys
McLean P. Echlin, William C. Lenthe, Jean-Charles Stinville,
and Tresa M. Pollock
1 Superalloys and Fatigue
Turbine engines have continuously improved in performance and efficiency due
to advances in materials and coatings, combined with the application of advanced
thermomechanical, heat transfer, and aerodynamic design methodologies. Turbine
disks are among the most safety-critical components in an aircraft engine and have
therefore been the subject of extensive development and characterization studies
[1–3]. Polycrystalline nickel-base superalloys are the typical material of choice
for turbine disks due to their high fatigue resistance and ultimate tensile strength
and good thermomechanical and thermochemical stability at elevated temperatures
[4, 5]. Powder metallurgy processing is used to produce disk components with
highly controlled grain size distributions, controlled inclusion (carbide and nitride)
content via powder stock filtering, and near net shape part geometries [1–5].
Inclusion content and grain structure have both been shown to be influential in the
fatigue life of disk alloys [6, 7]. An improved predictive capability of the mechanical
performance of these alloys is required to enhance life prediction and reliability as
well as guide the development of new alloys and processing paths.
Predicting fatigue properties of superalloys is particularly challenging, due to
the localized character of the plasticity during cycling and its strong dependence
on material structure. The schematic in Fig. 1 shows the microstructure of a
polycrystalline superalloy, containing annealing twins as well as the L1 2 γ
precipitate strengthening phase, and the approximate length scales at which they
M. P. Echlin () · W. C. Lenthe · J.-C. Stinville · T. M. Pollock
The Materials Department, University of California Santa Barbara, Santa Barbara, CA, USA
e-mail: mechlin@ucsb.edu; jeancharles_stinville@ucsb.edu; tresap@ucsb.edu
© Springer Nature Switzerland AG 2020
S. Ghosh et al. (eds.), Integrated Computational Materials Engineering (ICME),
https://doi.org/10.1007/978-3-030-40562-5_1
1