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Transformative Aeronautics Concepts Program

Development of an Additive Manufacturing Ecosystem for Qualification of Additive Manufacturing Processes and Materials in Aviation

Completed Technology Project

Project Description

The figure shows an example of how the defect structure in laser powder bed fusion machines varies in a systematic but highly non-linear manner across power-speed space. In one corner, lack of overlap of melt pools induces lack-of-fusion porosity. In the opposite corner, excessive keyhole depth results in instability and keyhole porosity. In between these two limits there is a region of high density that nevertheless can be disrupted if the combination of high speed & power results in too-long melt pools and the bead-up problem. Inside these limits there is a process window within which one expect within which one expect near full density.

The major challenges associated with additive manufacturing (AM) are an ability to qualify parts and the costs associated with the technology. Our team will study and mature technologies as detailed below to develop an ecosystem for the qualification of AM machines, which in turn supports the certification of part production.

Additive manufacturing offers unique opportunities for the aviation industry in the fabrication of original components and replacement parts.  Aggressive use of metals AM has, for example, allowed the rapid development and production of new launch vehicle designs, at substantially reduced costs.  Aviation has unique challenges, such as higher production volumes, but the potential value of integrating AM into aviation manufacturing is clear.

To implement the ecosystem for AM qualification, the team will run a set of six multi-disciplinary projects. Each of these projects will address a current barrier to AM process qualification, and efficient production.

  1. AM Flaw Management: Flaw (dominated by pore structure) management is currently the most important need in the fabrication of aviation components subjected to fatigue. This project will define the processing window to achieve flaw/porosity control within defined limits and further demonstrate how process optimization can control porosity levels within that processing window. Mechanical properties such as fatigue will be used to quantify the effects of porosity and build the necessary data portfolio for process qualification.
  2. Qualification Aware Process Maps: There is a concern in the aero industry that any changes in process variables require a full re-qualification of an AM process and this is leading to qualification efforts focusing on a single process variable set (usually defined by a machine manufacturer). This project will address this concern by defining multiple process variable points within the process window (Project #1) and developing data for each of them for qualification.
  3. Qualification Aware Post Processing: Post processing of an aviation part can easily cost as much as the additive fabrication itself, yet little science has been applied to post processing of AM parts. In particular, there is an important coupled relationship between AM processing and post processing to achieve optimal cost and performance. This project will investigate and implement more efficient post-processing methods that support qualification.
  4. Database Analytics: This project will compile data from all members on process-structure- property relationships, with a focus on porosity and fatigue. This project will apply data science to develop a model for qualification that will be used in training and education (next project).
  5. Training and Education: We will disseminate project results across University, Small Business and Partner Company and Government Laboratory team members and will train small businesses looking to become Tier 1 AM suppliers.  Dissemination will occur through student and industry employee exchanges executed at the academic team member sites.  We will also train potential AM component suppliers (subcontractors) to achieve various defined levels (tiers) of AM expertise and thus qualify their processes using the results of these projects.
  6. Scaling to Production: A critical barrier to widespread use of AM in aviation manufacturing is the scaling from research-based component fabrication to small-scale production at the rate of hundreds or thousands of parts per year. This project will investigate optimal configurations of combined pre-processing, processing and post-processing cells that exploit robotic automation and its integration with human workers.
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