Columbus, OH, United States

Scientific Forming Technologies Corporation

www.deform.com
Columbus, OH, United States

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Grant
Agency: Department of Defense | Branch: Navy | Program: STTR | Phase: Phase I | Award Amount: 79.99K | Year: 2012

Superplastic forming process (SPF) takes advantage of unique ability of certain materials such as titanium alloys that exhibit exceptionally high tensile ductility beyond its normal limits of plastic deformation at elevated temperatures and low strain rates. Within a narrow window of a combination of elevated temperatures and low strain rates, titanium alloys can withstand elongation as high as 300%. One of the critical factors that determine the effectiveness and the optimum design of a SPF part is the quality of incoming sheet material used for the manufacture of the part. The key microstructural features of alpha-beta titanium sheet material that needs to be optimized include alpha grain size, grain aspect ratio, grain size distribution, relative volume fraction of alpha and beta phase and texture of the alpha phase. While there are established models to simulate cogging, rolling and SPF processes, there is still no single integrated material modeling tool that would predict the microstructural evolution during the primary processing of alpha-beta titanium alloys. Understanding how the key process variables and material behavior impact the microstructural evolution of sheet products during the rolling process will be paramount task in this project. Scientific Forming Technologies Corporation (SFTC) is teaming with the University of Texas at Austin, Timet and Boeing for the Phase I of this project. The objective of this project is to develop a modeling framework that enables the prediction of microstructure evolution leading to optimum design of sheet material for the SPF of titanium structural components. The various rolling, cross rolling and pack rolling schedules that a titanium plate material undergoes in its conversion process to sheet material need to be modeled and optimized for ideal microstructural features in the finished titanium sheet product. With the sensitivity analysis framework in DEFORM system, the user will be able to systematically analyze the variabilities and uncertainties associated with the processing conditions, boundary conditions, material properties and incoming starting grain size distribution of the plate material, thus providing a robust design of material for the SPF processes. At the end of phase I, our team would complete characterization of Ti 6-4 and Ti 54M plate, intermediate plate-sheet and final sheet product. Our team will complete modeling of lab scale rolling, cross rolling and pack rolling processes and track material history of selected locations across the various thermo-mechanical processing operations. Our team will investigate appropriate alpha lath spheroidization models as well as recrystallization and grain growth models for Ti 6-4 material that are available in the literature. Our team will work closely with Navy to develop an implementation and a validation plan for subsequent Phase 2 activities. It is envisioned that the implementation and validation of microstructure evolution models will be undertaken in the phase II of this project.


Grant
Agency: Department of Defense | Branch: Navy | Program: STTR | Phase: Phase I | Award Amount: 79.96K | Year: 2016

We are proposing to identify an ICME architecture that will enable the multi-scale modeling of additive manufacturing (AM) process at both the component level as well as at the meso-scale level such that the final part quality and performance can be predicted accurately. At the component level, the proposed ICME framework would help in predicting residual stresses, distortion and the necessary support fixtures needed to minimize distortion, while considering optimal build conditions such as laser energy, the laser path and other relevant processing conditions. At the meso-scale level, the objective of the proposed ICME framework is to identify a computationally efficient methodology to predict local temperature distribution, molten pool shape, porosity and other relevant microstructural features. It is envisioned that the proposed ICME architecture would support surrogate models such as phenomenological models that can predict microstructural features as a function of processing parameters. By extension, the same ICME framework should be able to support surrogate microstructure to property models using either Neural network models or Bayesian models. Existing sensitivity analysis and probabilistic modeling techniques along with uncertainty quantification methods can be extended to model AM processes which would help in rapid qualification of additive manufacturing process and parts.


Grant
Agency: Department of Defense | Branch: Defense Logistics Agency | Program: SBIR | Phase: Phase I | Award Amount: 149.89K | Year: 2014

Forging process is widely used in the manufacture of critical mission sensitive components that require high strength and better consistent performance in service conditions. Process modeling for forging processes has been very successful in the last three decades. Modeling forging process serves us a virtual tryout tool and it offers lot more details about the forging process and parts than an expensive, time consuming shop floor trial would. Forging modeling results provide vital information regarding material flow, die fill, potential defect formation, tool failure and microstructure evolution. With increasing complexities of the forged geometries and push for near net shape forging, it is challenging to design an optimum forging progression that will result in reduced material and processing cost while maximizing the quality and robustness of the forged component. Optimization techniques can be effectively used in forging process modeling to achieve the desired goal of reducing the cost while maximizing the quality of the forged product. Sensitivity analysis will help in understanding how variabilities and uncertainties associated with the key processing variables and material properties will impact the forging process design. While manufacturing process modeling capabilities are mature, forging process optimization and sensitivity analysis to evaluate the robustness of forging process and part design is still at a nascent stage. Scientific Forming Technologies Corporation (SFTC) develops and supports forging process modeling system, DEFORM, which is widely used by the forging industry around the globe for the past 20 years. In this project, SFTC is proposing to systematically extend optimization techniques and sensitivity analysis to forging process modeling. During Phase I, SFTC will investigate a modeling framework that enables optimization of the forging processes which will help in minimizing the overall cost of the forging including material input weight and processing cost. SFTC will also investigate the application of sensitivity analysis for forging process modeling, paving the way for robust process design, which may lead to reduced scrap and rework cost. Phase I tasks will demonstrate the technical viability and commercialization potential of forging process optimization methods.


Grant
Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase I | Award Amount: 149.96K | Year: 2014

Our team will investigate a multiscale modeling framework to predict the final microstructure of a dual phase Ti 6-4 component as a function of thermo-mechanical processing. The microstructure properties will vary locally throughout the part, and will include features such as grain size, phase volume fraction, morphology of grains and phases, and the data structure representations will be scalable and expandable as the need for future microstructure modeling descriptors arise. The local evolution of microstructural features will be tightly coupled to the computation of a component wide process simulation, and will be based upon fundamental physics, not simple phenomenological models. The proposed work will investigate expanding existing single-phase physics based constitutive models into dual-phase models for titanium alloys. Crystallographic texture information will be stored locally at each element of the FEM simulation, and will evolve via physics-based texture evolution models. Typical crystal plasticity formulations including the Taylor model and the Visco Plastic Self Consistent (VPSC) model will be studied in Phase I. A method to represent crystal texture at every element within a macroscopic component utilizing the Rodrigues texture representation will be used. Texture representations that consider grain size and morphology (Grain Size Orientation Distribution Function) will also be investigated.


Grant
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.


Grant
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.


Grant
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.


Grant
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase I | Award Amount: 99.92K | Year: 2010

Integrating 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.


Grant
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase I | Award Amount: 149.95K | Year: 2012

ABSTRACT: Manufacturing of jet engine and aerospace structural components involve a series of thermo-mechanical processes such as forging, heat treatment, machining and joining processes. During thermo-mechanical processing, bulk residual stresses in the components evolve which may lead to part distortion. Surface residual stresses impact fatigue life. Managing residual stresses in the part during processing and under service condition is therefore critical to optimizing component performance. As the aerospace industry embarks on introducing new material system and starts to push the performance limits on the components, design and material engineers would need robust, reliable and validated predictions of residual stress distributions in the part. In this proposed Phase I program, Scientific Forming Technologies Corporation (SFTC) is teaming up with Hill Engineering LLC and Proto Manufacturing Inc to work closely with AFRL and jet engine OEMs including Pratt and Whitney, GE Aviation and Rolls Royce to establish a framework for quality assurance procedure to link residual stress modeling predictions with measurements. DEFORM residual stress modeling predictions need to account for uncertainties in processing conditions, material data and boundary conditions. It is critical to identify factors impacting bulk and surface residual stress measurement accuracy, repeatability and reliability of measurements and quantifying measurement errors and variations. A proof of concept procedure for a combined residual stress modeling and measurements quality assurance plan will be demonstrated. A detailed verification and validation plan for residual stress quality assurance program will be developed during Phase I in consultation with AFRL and OEMs which will be executed in subsequent Phase II part of this program. It is anticipated that a validated residual stress quality assurance program linking modeling predictions and measurements will be beneficial to OEMs in qualifying first article forgings and verifying periodic cutups from the perspective of optimizing fatigue life and managing part distortion during thermo-mechanical processing of components. BENEFIT: It is anticipated that the proposed work will result in establishing a residual stress assurance plan linking residual stress modeling results with both bulk and surface residual stress measurements. This proposed work aims to bridge the gap between residual stress modeling predictions and measurements. It is anticipated that a quality assurance procedure linking residual stress modeling and measurements will help in qualifying first article forgings through an improved, reliable understanding of part distortion during subsequent machining process, fatigue life and risk assessment This project will lead to robust, reliable and validated predictions and measurements of residual stresses. This will lead to better design and control of thermo-mechanical processes which in future may help to push the existing limits of jet engine performance in service conditions. This project will help in improved understanding of the interaction of residual stresses on fatigue life and part distortion during processing. Finally this project may play a small but significant role in accelerating the insertion of new material through a better understanding of processing, evolution of residual stresses and fatigue life.


Grant
Agency: Department of Defense | Branch: Navy | Program: STTR | Phase: Phase I | Award Amount: 99.94K | Year: 2010

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.

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