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Nickel-based superalloys (Ni-alloys) are widely used in flight-critical aeroengine components because of their excellent material properties at high temperatures such as yield strength, ductility, and creep resistance. However, these desirable high-temperature properties also make Ni-alloys very difficult to machine. This paper provides an overview and benchmarking of various constitutive models to provide the process modeling community with an objective comparison between various calibrated material models, to increase the accuracy of process model predictions for machining of Ni-alloys. Various studies involving the Johnson-Cook model and the calibration of its constants in finite element simulations are discussed. Significant discrepancies exist between researchers' approaches to calibrating constitutive models. Moreover, this paper provides a comprehensive overview of pedigreed physical material properties for a range of Ni-alloys. In this context, the variation of thermal properties and thermally induced stresses over machining temperature regimes are modeled for a variety of Ni-alloys. The chemical compositions and applications for a range of relevant Ni-alloys are also explored. Overall, this manuscript identifies the need for more comprehensive analysis and process-specific characterization of thermomechanical properties for difficult-to-machine Ni-alloys to improve machining performance and aeroengine component quality.
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Physics-Informed Uncertainty Quantification in Modeling of Machining-Induced Residual Stress
Machining processes involve various sources of uncertainty which lead to inaccurate interpretation of results in the surface integrity of machined products. This work presents a physics-informed, data-driven modeling framework for achieving comprehensive uncertainty quantification (UQ) of the impact of process and material variability on machining-induced residual stress (RS). Uncertainty due to the variation in bulk material properties and model input parameters in machining are considered. Preliminary results showed that variations in calibration parameters have a substantial effect on modeling RS, while the variation in material properties has a smaller effect. Further research directions for UQ in machining are also outlined.
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- Award ID(s):
- 2143806
- PAR ID:
- 10488566
- Publisher / Repository:
- Procedia CIRP, Elsevier
- Date Published:
- Journal Name:
- Procedia CIRP
- Volume:
- 117
- Issue:
- C
- ISSN:
- 2212-8271
- Page Range / eLocation ID:
- 139 to 144
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.procir.2023.03.025
