The Defense Advanced Research Projects Agency (DARPA) has announced the Digital Manufacturing Analysis, Correlation and Estimation (DMACE) Challenge. A $50,000 prize is offered for the most accurate predictive model.
Advances in digital manufacturing (DM) may address cost and time constraints associated with ma nufacturing the complex components required to support the Department of Defense mission. With the ongoing development of DM, a better understanding of the capabilities and limitations of DM is needed. The Defense Advanced Research Projects Agency (DARPA) Digital Manufacturing Analysis, Correlation and Estimation (DMACE) Challenge is a competition designed specifically to use crowd sourcing to advance knowledge of the potential capabilities and limitations of DM.
Within the Challenge, competitors will develop models that predict the output properties of products created by a DM machine based on corresponding machine inputs. The Challenge could be solved by applying any of a wide variety of engineering, mathematic or other approaches to predictive modeling.
“Widespread acceptance of DM components requires first that we determine whether predictive correlations exist between DM settings and resultant product properties,” said Gill Pratt, DARPA program manager. “If a predictive correlation model is found, there is potential to change defense manufacturing significantly. If a manufacturer can predict the reliability of a component part with a high degree of certainty, DM could be used for all sorts of system components.”
The DMACE Challenge requires participants to develop the most accurate DM output predictive models given a set of input parameters for two different computer aided designs (CAD): one for a sphere (digitally manufactured with titanium) and another for a cube (digitally manufactured with polymer). Data describing the input settings for a particular digital manufacturing process and the resultant output of structural tests will be distributed by DARPA online. Input setting data may include, but is not limited to device control parameters, material composition, and CAD files. Output test data may include, but is not limited to structural load test results such as stiffness, strength, and displacement data. These data sets will be provided on the DMACE website to registered individuals and teams.