Developing Alloy Compositions Conducive to Additive Manufacturing

Navy SBIR 21.1 - Topic N211-085
ONR - Office of Naval Research - Ms. Lore-Anne Ponirakis -
Opens: January 14, 2021 - Closes: February 18, 2021 (12:00pm EDT)

N211-085 TITLE: Developing Alloy Compositions Conducive to Additive Manufacturing

RT&L FOCUS AREA(S): Machine Learning/AI; General Warfighting Requirements

TECHNOLOGY AREA(S): Air Platforms; Ground / Sea Vehicles; Materials / Processes

OBJECTIVE: Develop alloy compositions that enable additive manufacturing (AM) processes to produce properties that are not currently achievable such as materials with a preferred crystallographic orientation, dispersion-forming alloys that can either form the dispersion during AM or after AM through heat treatment. Alloy compositions should reduce defects in components, thus promoting them to be more resistant to fatigue, with potential increases in strength.

DESCRIPTION: Additive work is being done on current cast and wrought alloy compositions trying to achieve original alloy properties. Achieving such properties is difficult because of the varying solidification conditions inherent in different AM processes and part designs. AM alloys have a complex thermal history involving directional heat build-up and repeated melting and rapid solidification. AM usually results in a finer microstructure than conventional processing which gives the AM material better fatigue properties, but debited creep properties. Modifying alloy compositions to take advantage of AM solidification variables could take advantage of AM to improve alloy properties. The interaction of alloying elements is recognized in promoting desirable microstructural phases and solid-solution effects for development of properties. The use of Integrated Computational Materials Engineering (ICME) should relate AM processability with alloy chemistry in order to develop models that are able to predict alloy chemistry that minimizes defects while maintaining base alloy properties. This could be done by linking materials data sets, modeling, and AM variables in a machine learning framework to achieve properties. The condition under which solidification takes place determines the structural features that affect the physical and mechanical properties of an alloy. Melting and solidification are generally well-understood during casting processes, but melting and solidification profiles, effect of contamination, and alloy chemistry control during cyclic AM processing, particularly for complex and thicker components, are also not well characterized.

PHASE I: Explore the literature to determine the relationship of wrought alloys chemical compositions and the chemistries of its cast alloy corollaries, understand the underlining reasons for the different chemistries to enable an alloy to be similar by each process. The company should select an AM process which has a good understanding of the heat transfer, solidification variables, and factors which cause defects. Focus on IN 718 or Alloy 230 with the goal of producing properties equivalent to or greater than achieved by the wrought alloy. Develop conceptual models/algorithms that link alloy chemistry to AM processes and resulting alloy microstructure and subsequent mechanical properties. Company needs to show that alloy chemistry models can consistently predict the alloy physical and mechanical properties for the AM process selected. Consider powder chemistry and size distribution. Analysis of the defects is suggested to be done by non-destructive processes such as optical tomography, in-situ thermographic analysis, ultrasonic monitoring or x-ray tomography. ICME should link to AM process parameters with defect frequency and distribution in the component design, employ and prove feasibility of an approach for a metal AM method. Develop a Phase II plan.

PHASE II: Based upon Phase I effort, apply ICME tools to optimize metal AM processing and to predict design and processing parameter limits for a more complex component. Consider computational models and relevant databases. Since most AM metal processes are layer--by-layer, work need to model the change in heat transfer as the layers are added to previous layers in an effort to minimize microstructural changes within the component. Determine relative sensitivity of different chemistry variables within a property model; and determine which variable is "most important" in controlling property value. Work to optimize the alloy chemistry/processing/property model by selecting another nickel-base alloy or an iron-based alloy to explore an alloy family. Collaborate with a powder manufacturer for powder size distributions for AM systems. Ensure that the program provides a means for capturing, sharing, and transforming materials data into a structured format that is amenable to transformation to other formats for use by ICME and other computational programs, modeling, and simulation methods. Demonstrate the functionality of this framework.

PHASE III DUAL USE APPLICATIONS: The AM process and alloy chemistries that are suited specifically for AM processes offers the opportunity of conformal, and unique design not possible with more conventional fabrication processes. Proven AM process optimization leading to a minimization of process - and materials - derived defects would improve acceptance of AM for producing component for the Navy and for private industry. The use of AM could lead to more innovative designs capable of more efficiently removing heat because such designs could eliminate or severely reduce joints. AM processing of components that are qualified for Navy use could also be applied to commercial use. The use of AM could lead to more innovative designs capable meet ever-increasing demands on components for the Navy as such designs could eliminate or severely reduce joints. AM processing of components that are qualified for Navy use could also be applied to commercial use more quickly. Engage with the Government and/or public, commercial, company, or professional technical societies that retain materials databases. Interface with a software company that promotes and delivers materials computational programs to explore and develop an integration pathway for the database discriminating program with their software. The outcome of this technology development program will be a commercial suite of informatics-derived tools that can be able to reliably analyze and discriminate various sources of materials databases to optimize the capability for materials prediction. Transition the material production methodology to a suitable industrial material producer. The ICME code needs to be transitioned to the commercial entity for potential incorporation of a more comprehensive ICME code. Commercialize the alloys for use in DoD and commercial markets.


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KEYWORDS: Additive manufacturing; alloys; Integrated Computational Materials Engineering; ICME; solidification; processing; alloy chemistry; heat transfer; defects


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