Integrated Processing and Probabilistic Lifing Models for Superalloy Turbine Disks
Small Business Information
Scientific Forming Technologies Corporat
2545 Farmers Drive Suite 200, Columbus, OH, 43235
Executive Vice President
Executive Vice President
AbstractIntegrating process modeling capabilities with probabilistic lifing methods will greatly help the jet engine industry in improving fatigue life predictions and risk assessment of jet engine disk components. Fatigue life of a nickel based superalloy disk component is greatly influenced by the bulk residual stresses resulting from prior thermo-mechanical processing, service conditions, microstructural features and material anomalies such as inclusions and pores. Using the integrated process modeling system DEFORM, it is possible to predict the evolution of critical life limiting factors during thermo-mechanical processing (cogging, forging, heat treatment and machining processes) of a jet engine disk component. Currently there is no capability available where the detailed manufacturing process modeling results can be directly used in probabilistic lifing analysis. Scientific Forming Technologies Corporation is teaming with Southwest Research Institute® in this project to develop a framework to link the process modeling system, DEFORM with the probabilistic lifing modeling system DARWIN®. At the end of phase I, we intend to demonstrate a proof of concept model for linking DEFORM and DARWIN, specifically studying the effects of residual stress predictions from DEFORM under thermo-mechanical processing conditions on probabilistic lifing predictions of DARWIN for a generic jet engine disk. We will investigate a modeling framework for process optimization in DEFORM to effectively link process modeling results with probabilistic lifing method predictions. We propose to define an infrastructure in DEFORM to include sensitivity analysis and probabilistic models, specifically to address uncertainties in processing conditions, material data and boundary conditions. Our team will work closely with all the major jet engine OEMs to develop an implementation plan so as to maximize the benefits of linking processing models with probabilistic lifing methods. BENEFIT: It is anticipated that a proposed link between process modeling results of DEFORM and DARWIN will enhance the accuracy of fatigue life and risk assessment of jet engine components, thus greatly benefiting the jet engine industry. Integrating process modeling with probabilistic lifing will provide more accurate predictions of rotor fatigue life and its variability by including location-specific descriptions of residual stress evolution resulting from prior thermo-mechanical processes as well as the service conditions along with microstructural characteristics, and material anomaly size and orientation. Building a link between manufacturing processing models and probabilistic lifing analysis will facilitate a genuine Integrated Computational Materials Engineering (ICME) methodology in analyzing the design and manufacture of a jet engine component. This would make it possible to optimize the design process and improve component performance by directly incorporating material and manufacturing variables into the assessment of component lifing and reliability. This link will also provide an improved understanding of interaction of microstructural features and residual stresses on mechanical property response under service conditions. It is expected that this link will help in understanding and optimizing the processing window during the manufacture of jet engine components to push the existing limits of jet engine performance in service. This proposed work will also serve as a launching pad for future full scale manufacturing process optimization analysis based on fatigue life of jet engine components under service conditions. It is anticipated that this would help in accelerating the insertion of new material to service through a better understanding of processing, evolution of microstructural features and mechanical property response.
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