Atch 2 - SDAMPT On-Site TO1 SOO.pdf

FA8650-21-S-5002

Attachment 2

2/10/2021

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FOUO

STATEMENT OF OBJECTIVES

Task Order 1

Alloys and Metallic Material Processing Early Applied Research (AMP-EAR)

1.0 Objective/Scope

The AMP-EAR task order of the SDAMPT contract shall advance the state-of-the-art for metals processing science (MPS) and alloy discovery and design (ADD).

The focus of this task order is to execute basic and applied research in order to develop and transition new and/or improve metallic materials (including metallic alloys, hybrid, nano-composite, and composite structures) used in aerospace applications to include, but not limited to, propulsion systems, airframe structure, sustainment, and space applications. The research efforts necessary to achieve these goals are expected to include synthesis, processing, modeling, simulation, characterization, inspection, and preliminary evaluation of material performance. Air Force systems benefitting from this research may include conventional aerospace propulsion and airframe structures and components, hypersonic vehicle structures, liquid rocket engine structures, thermal protection systems, structural energetics and munitions materials, airframes, and components for propulsion applications.

This effort is expected to pursue metallic materials systems and processes that are of current and future potential interest to the Air Force through close collaborative and frequent interaction with AFRL government civilian and military scientists and engineers, as well as interactions with other DoD contractors and other US Government entities (including the Department of Energy, National Aeronautics and Space Administration, and the Federal Aviation Administration) in support of USAF and USSF science and engineering priorities. Due to the evolving needs of the USAF, the technical effort under this contract will also evolve over time as the research progresses and new information becomes available. These are likely to include tasks involving alloy development, thermomechanical processing, process modeling, simulation of microstructure evolution during processing, modeling of phase transformations, dynamics of solid, liquid, and gaseous defects, thermodynamic modeling, microstructure characterization, and simulations that predict kinetic pathways for phase transformations.

In addition to classical and conventional approaches, emerging computational tools from the fields of data-science, informatics, and machine learning should be utilized to when advantageous in solving complex problems like modeling of transient processes, prediction of residual stresses, unsupervised automated experimentation, thermomechanical process route optimization, alloy discovery and design, and/or optimization of additive manufacturing processes. A multidisciplinary approach is desired which takes inspiration from and advantage of cutting-edge promising technologies such as deep learning, neural networks, big data, graphical processing unit (GPU) computing, robotics, quantum computing, and natural language

-2-processing toward the advancement of the state-of-the-art for metallic materials. The complexity of the research may drive the development of new experimental methods to make dynamic measurements of material state. Similarly, the complexity of the process may drive a requirement to perform real-time sensing and/or analysis of the machine state during processing, which may or may not make decisions in a closed-loop control system for processes like additive manufacturing.

2.0 Technical Requirements

This task order involves providing technical support to ongoing AFRL research and development efforts in areas of substantial AFRL interest due to high potential reward in the advancement of the state of the art of metallic materials and processing. Most research efforts would be targeted at technology readiness levels (TRL) 1-4. It is not expected that work would be performed simultaneously on all of the following technical requirements, but it is expected that some work would be performed to advance the state of the art in each of these areas over the contract period. The balance of work over these areas will be influenced by agency needs and funding sources. All technical requirements listed below may also involve collaborations across AFRL, industry, academia, and other government laboratories, as outlined in the SOO of the basic contract. These activities should be supported by a robust research management system that integrates work order management and task progress tracking with easy, intuitive tracking of specimens, results from characterization and testing tasks, simulation results, raw data storage, and data analyses.

2.1 Automation of testing, characterization, and processing workflows

2.1.1 Artificial intelligence and machine learning for quantitative characterization of metallic microstructures. This activity involves development of accurate automated methods for identifying the size, shape, orientation, location, and spatial distributions of the phases and defects in a microstructure which affect its properties and useful life, replacing the need for tedious human labor. The activity also may involve development and implementation of stereological techniques to quantify the likely 3D parameters describing the feature set from 2D observations.

Validation of approaches may involve development and application of 3D characterization techniques. It is expected that this research activity will find pervasive application across all metals research efforts, and will lead to a substantial increase in research productivity, as the task of microstructure segmentation consumes a substantial number of person-hours for each micrograph. Improved methodologies must be established as such with authoritative evidence. Working alongside government scientists and engineers, a substantial effort will be made to transition improvements to standards organizations, the FAA, and to encourage widespread adoption or acceptance within the USAF supply chain.

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Relevant metallic materials systems include but are not limited to alloys of nickel, cobalt, aluminum, titanium, and iron.

2.1.2 Machine learning for correlation of local thermomechanical history with formation of defects and anomalous microstructure features. This effort involves using established computational techniques for identifying the correlations between complex thermomechanical history data with multimodal fused characterization and simulation data (in 2D or 3D) to develop predictive models for desirable/undesirable microstructure feature development during processing and develop novel methodologies for microstructure control during additive manufacturing.

While this effort is generally expected to primarily target additive manufacturing technologies, it may also find application in conventional thermomechanical processing routes.

2.1.3 Autonomous experiments for rapid assessment and screening of alloys.

This effort involves the development of specialized custom test equipment capable of, for example, iteratively applying heat treatments, oxidation weight gain measurements, hardness measurements, optical microscopy, and/or surface profilometry to screen systematically varying alloy compositions for best performance. Integral to this effort will be identifying combinations of low fidelity experiments with strong correlations to key performance parameters for a material in a given application, and identifying when higher fidelity experimentation is required for further assessment. The effort also may involve developing autonomous experimentation in which the machine must learn the best approaches for preparing and testing specimens. Alloy systems of interest include, but are not limited to, refractory complex concentrated alloys.

2.1.4 Machine learning for synthetic microstructure generation. This activity involves generation of digital datasets that are either indistinguishable in terms of simulated properties or indistinguishable to a trained expert eye from real experimental data. The purpose of this effort is to develop model inputs which can be used for determining the optimum microstructure for a given application in service. Relevant metallic materials systems include but are not limited to nickel alloys, titanium alloys, quench-and-temper steels, and structural energetics.

2.1.5 Metamorphic manufacturing and robotic blacksmithing. This activity involves installation, programming, safety assessments, and development of best practices for robotic manipulation of workpieces during open die forging. The objective is to use machine feedback of forging pressures, workpeice geometry, and thermal profile to design processes in which reheat, hammer rate, and strain limit decisions for multi-step forming operations are made by a computer algorithm.

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2.2 Optimization of composition and processing route based on multiple performance objectives

2.2.1 Microstructure modeling of non-isothermal and non-isochronal multi-step thermomechanical processing routes. Microstructure develops from thermomechanical processing history and composition, therefore processing route optimization is required. Most work done to develop and calibrate microstructure evolution models has focused on isothermal or isochronal behavior. In this effort, models will be extended and/or developed to account for the non-isothermal and non-isochronal situations that materials are exposed to both during real-world processing and under operating environment conditions. Metallic material systems of interest in this area include, but are not limited to, alloys of nickel, titanium, and iron.

2.2.2 Physics-aided computational design of alloy composition. To broaden the array of available alloys for a given application and ensure that a material selection decision can be made to place the right material for high performance in any given structural application, it is necessary to tailor microstructure for performance. Microstructure and performance are dependent on composition and processing. Under this effort, computational tools for exploration of composition space will be constructed and exercised toward the discovery of novel alloys with unique property combinations. Alloy systems of interest include, but are not limited to, refractory complex concentrated alloys.

2.2.3 Integrated computational materials engineering for the development of processing to achieve microstructure feature topologies enabling structural energetic alloys and fragmentation control. In this effort, an integrated computational-experimental approach will be employed to design microstructures and behavior of structural energetic alloys and materials with controlled crack propagation properties upon high strain rate loading.

2.2.4 Model parameterization using dynamic in-situ measurements of thermomechanical and environmental transient behavior.

This effort involves the development of integrated experimental and computational techniques for interpreting in-situ dynamic measurements of transient phenomena for the parameterization of models of microstructure evolution with fewer experiments than are required under classical approaches. Metallic material systems of interest in this area include, but are not limited to, alloys of nickel, titanium, and iron.

2.2.5 Synchrotron diffraction studies to elucidate an improved understanding of mechanisms of microstructure evolution. High energy beamline diffraction techniques are emerging as key tools for furthering our understanding of materials responses. Under this effort, synchrotron facilities will be leveraged toward improving our understanding of

-5-microstructure development and materials behavior. Metallic material systems of interest in this area include, but are not limited to, alloys of nickel, titanium, and iron.

2.3 Development of rapid qualification and microstructure prediction methodologies

2.3.1 Novel microstructure evolution model development inclusive of physics of strain accommodation. Under this requirement, novel models that account for dislocation and disconnection effects on flow stresses and interfacial behavior will be developed to improve predictions of microstructure as a result of thermomechanical processing. This will most likely be applied to titanium and nickel alloys currently used in service in propulsion and airframe applications.

2.3.2 Microstructure prediction in additive manufacturing. Under this requirement, research shall be performed to understand the development of residual stresses and defects and other microstructural features in additive manufacturing processes for the purposes of assisting in the design of rapid qualification approaches and inspection techniques.

2.3.3 Microstructure prediction for solidification science. Under this requirement, simulation of microstructure and defect formation during metal solidification shall be performed, incorporating state-of-the-art multiphysics techniques when applicable. Example applications of development in this area may be investment casting of aerospace components or developing defect probability, grain morphology, or texture predictions for additive manufacturing.

2.3.4 Non-destructive microstructure assessments. This effort will involve the development, improvement, verification, and validation of methodologies for rapidly assessing microstructure state without performing destructive sectioning or chemical preparation of specimens, toward applications as a screening tool for alloy selection and process design. Of particular interest is integration of non-destructive techniques within metals processing operations for in-situ monitoring. Metallic material systems of interest in this area include, but are not limited to, alloys of nickel, titanium, and iron.

2.3.5 Accurate rapid screening and risk assessment methodologies.

Under this requirement, research and development activity shall be undertaken to develop methodologies for predicting processing regimes in which detrimental microstructures are likely to be formed. The applications may include linking composition to oxidation rates, ductile-to-brittle transition temperatures, embrittling phases; or may include linking stress-strain paths to high texture intensities or proclivities for abnormal grain growth; or may include heat and fluid flow conditions that lead to solidification defects.

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Deliverables:

CDRLS A001, A002, A003, A004, A005, A006 and Hardware (Finished research samples and residual test materials).

Operations Security (OPSEC) Requirements:

The contractor shall participate in all activities associated with the disciplines of the organization’s Industrial Security, Information Security, Personnel Security, Operations Security (OPSEC), and Antiterrorism programs, following appropriate measures in each program as required for this particular contract. Security measures are required to reduce program vulnerability from successful adversary collection, exploitation of critical information, and violations of export control requirements. The prime contractor shall ensure all subcontractors, if applicable, conform to these requirements as required by the prime contractor.

Program Protection Plan (PPP):

Any potential critical program information (CPI) generated as part of this effort will be reviewed to determine the need for a PPP or to be included as part of an existing PPP.