Shaffer J.B.,Drexel University |
Knezevic M.,Drexel University |
Knezevic M.,Scientific Forming Technologies Corporation |
Kalidindi S.R.,Drexel University
International Journal of Plasticity
Microstructure sensitive design (MSD) has thus far focused mainly on the identification of the set of microstructures that are theoretically predicted to exhibit a designer-specified combination of elastic-plastic properties. In this paper, we present the extension of the MSD methodology to process design solutions. The goal of process design is to identify a processing route to transform a given initial microstructure into a different microstructure that exhibits superior property combinations by using an arbitrary sequence of available deformation processing options (hereafter referred to as hybrid processing routes). In this paper, we have focused on orientation distribution function (i.e. the 1-point statistics of crystallographic texture in the sample) as the descriptor of microstructure, and considered only the low temperature deformation processes. We have also restricted our attention to Taylor-type crystal plasticity models. With these idealizations, it is shown that it is possible to develop efficient algorithms in the MSD framework to build texture evolution networks that cover most of the texture hull. The advantages of this approach are expounded upon in this paper with selected case studies. © 2010 Elsevier Ltd. All rights reserved. Source
Knezevic M.,Drexel University |
Knezevic M.,Scientific Forming Technologies Corporation |
Levinson A.,Drexel University |
Harris R.,Drexel University |
And 3 more authors.
This paper describes the main results from an experimental investigation into the consequences of deformation twinning in AZ31 on various aspects of plastic deformation, including the anisotropic strain-hardening rates, the tension/compression yield asymmetry, and the evolution of crystallographic texture. It was seen that AZ31 exhibited unusually high normalized strain-hardening rates compared to α-Ti that occurred beyond the strain levels where extension twins have completely altered the underlying texture. This observation challenges the validity of the generally accepted notion in the current literature that the high strain-hardening rates in AZ31 are directly caused by extension twins. It is postulated here that the thin contraction twins are very effective in strain hardening of the alloy by restricting the slip length associated with pyramidal 〈c + a〉 slip. This new hypothesis is able to explain the major experimental observations made in this study and in the prior literature. We have also presented a new hypothesis for the physical origin of the observed differences in the thicknesses of the extension and contraction twins. The stress fields in selected matrix-twin configurations were modeled using crystal plasticity finite element models. The contraction twin (01̄11)[01̄12̄] was predicted to form an internal extension twin (011̄2) [01̄11], resulting in the commonly observed "double twin" sequence. The extension twin is suggested to inhibit thickening of this double twin by loss of twin-matrix coherency. Extension twins were predicted to retain their coherency and thus thicken. © 2010 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved. Source
Agency: Department of Defense | Branch: Navy | Program: STTR | Phase: Phase II | Award Amount: 734.60K | Year: 2011
Numerical modeling tools can facilitate the process design, performance evaluation, and lifing prediction of a number of high-end components, including critical rotating jet engine parts. They can predict the component-wide state variables (such as stress, strain, strain rate, temperature) as a function of thermo-mechanical processing. These variables may then be coupled to microstructure evolution (such as grain size, precipitation). Subsequently the component, with its local variation in stresses and microstructure features, may be exposed to virtual tests (such as spin pit tests for jet engine turbine disks), and thus the performance of a component may be predicted as a function of the underlying locally specific microstructure-property relationships. Traditionally,"first order"structure-property relationships (such as strength) have be derived from average, scalar microstructure features (such as mean grain size, mean precipitate size), with encouraging success. A workpiece is initialized with an"as-received"microstructure (grain size, precipitate volume fraction), and thermo-mechanically processed. Classical microstructure models (such as Johnson-Mehl-Avrami-Kolmogorov) act on the average microstructure features and output an average microstructure result. However, higher order properties like fatigue crack initiation, fatigue life, etc require inputs that are more sophisticated than the"average"microstructure features. Whereas the average strength may be derived from the average microstructure features, lifing properties must be derived from the"worst actor"microstructure features those at the"long end of the tail"when graphed as a histogram. In order to predict these"worst actor"microstructure features, more sophisticated microstructure models, and a more robust state variable infrastructure than one which simply stores"average"values must be employed. Thus, during Phase I of this program, a proof-of-concept probabilistic model was developed to demonstrate this capability. Rather than providing scalar state variables of predicted strain, strain rate, temperature, and residual stresses, distributions of state variables, produced as a result of normal variation and uncertainties in the material and the processing conditions, were computed. These distributions then acted on distributions of microstructure features (e.g. grain size), in a probabilistic manner, thereby providing a numerical modeling tool that can compute the location-specific, probabilistic microstructure features and phenomena necessary as inputs to accurately compute higher order properties such as component life. During Phase I, Scientific Forming Technologies Corporation (SFTC) teamed with Carnegie Mellon University (CMU) to link sophisticated microstructure evolution research tools, developed at CMU, with existing FEM and microstructure modeling tools in the DEFORM code. Jet engine OEM GE Aviation provided industrial support. The proof-of-concept probabilistic modeling framework demonstrated in Phase I allows systematic analysis of the variabilities and uncertainties associated with the processing conditions, boundary conditions, material properties and incoming starting grain size distribution of the billet material. Thus, a probabilistic, location-specific microstructure response and residual stress distribution may be derived as a function of thermo-mechanical processing, and used as an input to a probabilistic lifing model. For Phase II, the team is expanding to include industrial supply chain partner Ladish, and research partner UES, to further improve the verification and validation of the thermo-mechanical processing, material modeling and property prediction methodology. Continued enhancement of the microstructure models and probabilistic infrastructure, integration into the commercial code in a user-friendly GUI, verification and validation of the model outputs, and more, are planned.
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase II | Award Amount: 1.50M | Year: 2011
Currently there is no commercial software modeling capability which correlates the details of a manufacturing process to the probabilistic lifing analysis of a component. Since fatigue life of a nickel base superalloy disk component is greatly influenced by bulk residual stresses, which themselves are functions of prior thermo-mechanical processing (TMP), service conditions, and microstructure features (such as grain size) and material anomalies (such as inclusions and pores), modeling of the evolution of these features during forming and in service will greatly improve fatigue life prediction. Using the integrated process modeling system DEFORM, it is possible to predict the evolution of critical life limiting factors during TMP (e.g. cogging, forging, heat treatment, and machining). Thus, integration of process modeling to probabilistic lifing methods will greatly help the jet engine industry, by improving fatigue life predictions and risk assessments of jet engine components. During Phase I of this project, Scientific Forming Technologies Corporation teamed with Southwest Research Institute 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 demonstrated a proof of concept model for linking DEFORM and DARWIN, specifically studying the effects of residual stress predictions from DEFORM generated during thermo-mechanical processing and service conditions on probabilistic lifing predictions of DARWIN for a generic jet engine disk. We investigated a modeling framework in DEFORM to effectively link process modeling results with probabilistic lifing method predictions. During Phase II of this project, our team will develop and implement modeling tools that will integrate location-specific grain size predictions, material anomaly orientation and residual stress profiles from processing models with probabilistic lifing methods. Proposed efforts are targeted towards developing techniques to link processing modeling results from DEFORM with the probabilistic component life prediction code, DARWIN. At the end of this program, it is envisioned that an infrastructure in DEFORM will be available to conduct sensitivity analysis specifically to address variabilities in processing conditions, material data and boundary conditions. It is proposed that DARWIN will be enhanced to take into account location specific grain size and material anomaly orientation in its life predictions and risk assessments. Our team is working 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 the link implemented in Phase II between process modeling results from DEFORM to 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 in Phase I has demonstrated the sensitivity of lifing results to residual stresses evolved during thermo-mechanical processing and service. Adding location-specific descriptions of grain size, material anomaly orientation and residual stress variability due to changes in processing conditions, material properties and boundary conditions is expected to provide more accurate predictions of component fatigue life and its variability. Building this 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 will 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 a tool to enhance understanding of the interaction between 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. The work proposed in Phase II 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 materials to service through a better understanding of processing, evolution of microstructural features and mechanical property response.
Agency: Department of Defense | Branch: Air Force | Program: STTR | Phase: Phase II | Award Amount: 749.87K | Year: 2010
Jet engine disk components are increasingly subjected to higher operating temperatures. To meet the demands of increasing thrust and higher operating temperatures, a newer generation of nickel based superalloys such as LSHR, Alloy 10, Rene104 and RR1000 are being processed with dual microstructure distributions. Fine grain, high strength, fatigue resistant bore properties are contrasted with coarser grain, creep resistant rim properties. In order to optimize bore and rim properties of the engine disk, innovative dual microstructure heat treatment methods (DMHT) are employed where the bore is heated and cooled from sub-solvus temperature while the rim is heated and cooled from super-solvus temperature. The reliability of jet engine disks processed via DMHT method are evaluated by traditional spin testing where the disk is subjected to cyclical loading. Of particular interest is the performance within the transition zone between the bore and rim of the disk as it transitions between a supersolvus coarse microstructure to a subsolvus fine microstructure. The proposed work focuses on developing and enhancing DEFORM system to model turbine disk spin testing and potentially in-service performance with location specific material properties. Models developed will have the ability to consider location specific bulk residual stresses and microstructure features induced from prior manufacturing processes. The effects of thermal loading, cyclic loading, gravity, centrifugal forces, creep, and precipitation coarsening can be coupled to predict the evolution of residual stresses, resulting distortion and microstructure evolution if necessary. In this project, it is proposed to develop and implement appropriate strength and creep models that can link the evolution of microstructural features to property response during thermo-mechanical processing as well as spin test and service conditions. Models developed will be validated against LSHR and Alloy 10 disk spin test experiments conducted by NASA Glenn. BENEFIT: Currently, there is no modeling system available to the industry which would take location specific material properties including microstructural features into consideration in predicting the disk behavior during spin test. The industry lacks a modeling system that is capable of predicting mechanical property response such as strength, creep, flow stress and fatigue resistance due to prior thermo-mechanical processing, accompanying microstructural changes and exposure to service conditions. The proposed work will address the shortcomings of the current capability and the needs of the industry in modeling spin tests. It is anticipated that after successful implementation of the proposed features in the DEFORM system, analytical models will be able to take into account the thermal transients and the cyclical loading conditions in the disk during spin testing to analyze the effects of grain size and precipitate size on plastic strain, tensile strength and residual stress redistribution. As a result of this proposed work, jet engine OEMs would be able to have a better understanding of the interaction of microstructural features and disk behavior under service conditions. The modeling infrastructure and methodology developed in this program will serve as a solid platform to develop microstructure and property prediction models during thermo-mechanical processing and performance under service conditions. Integrating these models into a thermodynamically and kinetically bounded simulation tool, which accounts implicitly for microstructure variability due to process variability, can assist in the accelerated insertion of materials into the jet engine industry.