The R2 program seeks to enable the manufacture of critical structures using indigenously available, processed feedstock materials at the time of need, in contested logistics environments.
While existing initiatives for forward structures production are advanced and innovative, they operate under the assumption that pristine raw materials will be readily available, and they operate with a fixed design and a fixed material, eliminating the flexibility required for forward and supply chain-denied production.
The envisioned R2 system aims to include hardware for the conversion of scavenged feedstock to usable materials, an analysis toolset to determine the material properties of the new material with sufficient error margin, and an analysis toolset to understand the system-level impact of component design changes or modifications.
The objective of R2 is to work with continually more diverse and unpredictable materials while simultaneously increasing the complexity and performance of end state structures. R2 seeks to create a solution that is both flexible and capable of meeting existing and future point of need manufacturing considerations.
Developments in this program will be in three focus areas:
- Convert to enable usable form factors with highest possible material property performance. R2 will overcome current limitations in processing diverse, complex, or contaminated indigenous feedstock by developing tooling and processing approaches that can accommodate widely variable inputs. Utilizing insights from current material conversion along with advances in tooling design and process control, conversion systems will transform into material processing units for readily available scavenged feedstock.
- Characterize: establish error reduction to provide useable material property prediction for design. R2 will update and develop material informatics models to predict minimum material properties of diverse material streams with high confidence. The primary objective is not to fully characterize material properties, but to efficiently identify a lower bound design value for which system level effects can be captured. Balancing improved material properties with error minimization is expected to present a broad solution space.
- Adaptive Design: trained, low C-SWaP (Computational – Size, Weight, and Power) adaptive design framework. R2 will efficiently update a baseline design to enable structural changes for components with the newly predicted material properties that meet or exceed the minimum programmatic metrics. Multiple evolving technologies of interest include change propagation analysis, and machine learning/artificial intelligence (ML/AI)-assisted finite element analysis (FEA).