Probabilistic Prediction of Location-Specific Microstructure in Turbine Disks Navy STTR FY2010.A Sol No.: Navy STTR FY2010.A Topic No.: N10A-T028 Topic Title: Probabilistic Prediction of Location-Specific Microstructure in Turbine Disks Proposal No.: N10A-028-0201 Firm: Scientific Forming Technologies Corporation 2545 Farmers Drive Suite 200 Columbus, Ohio 43235 Contact: Wei-Tsu Wu Phone: (614) 451-8322 Web Site: www.deform.com Abstract: While there are established methods available in determining the fatigue life of critical rotating components, there is still room for improvement for better understanding and prediction of life limiting factors. Improved risk assessment of jet engine disk components would require probabilistic modeling capability of the evolution of microstructural features, residual stresses and material anomalies as the disk components undergo thermo-mechanical processing. Currently, the integrated process modeling system DEFORM can only predict the evolution of microstructure deterministically during thermo-mechanical processing. Scientific Forming Technologies Corporation is teaming with Carnegie Mellon University in this project. The objective of this project is to develop a probabilistic modeling framework that enables probabilistic prediction of microstructure evolution and bulk residual stresses due to thermo-mechanical processing. The probabilistic modeling framework in DEFORM will enable the user to systematically analyze the variabilities and uncertainties associated with the processing conditions, boundary conditions, material properties and incoming starting grain size distribution of the billet material, thus providing a probabilistic location specific microstructure response which can be used as an input to the probabilistic lifing model. At the end of phase I, we intend to demonstrate a proof of concept models for probabilistic grain size evolution and residual stresses as a result of thermo-mechanical processing. Our team will work closely with a major jet engine OEM, GE Aviation to develop an implementation and a validation plan. It is envisioned that the implementation and validation of probabilistic modeling of microstructure evolution will be undertaken in the phase II of this project. Benefits: It is anticipated that a probabilistic process modeling framework with in DEFORM system would help in capturing the realistic process and material variations and uncertainties in predicting microstructural evolution during thermo-mechanical processing. The practice of life prediction and reliability evaluation by assuming the location and orientation of material defects are randomly distributed within the component, without considering its evolution kinetics during the manufacturing processes, tends to predict over-conservative useful life. The cost to produce the part increases as the components retire or maintain sooner than it should. It is expected that the probabilistic microstructure modeling will enhance the accuracy of fatigue life and risk assessment of jet engine components, thus greatly benefiting the jet engine industry. This project will also provide an improved understanding of the evolution of microstructure and will aid in a better design and control of thermo-mechanical processes. It will also help in a more reliable prediction of mechanical property response thus helping the OEMs to push the existing limits of jet engine performance and lifing. Occurrence of large grains in a component often ends up as an initiation site for fatigue crack. It is therefore critical to identify the presence, the size and the distribution of "as large as" (ALA) grains in the nickel based super alloy disk that may occur due to incomplete recrystallization and / or abnormal grain growth. In this project, our team will focus on the probabilistic evolution of ALA grains during thermo-mechanical processing. The established framework developed under this project will enable future implementation of the evolution of other life limiting factors such as micro-voids, micro-cracks, inclusions etc. In the long run, this project will provide a building block to facilitate Integrated Computational Materials Engineering (ICME) methodology, thus helping the industry in the accelerated insertion of new materials into service. Return