In the Digital Manufacturing Analysis, Correlation and Estimation 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, the Pentagon's Defense Advanced Research Projects Agency said.
"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."
DARPA explained that advances in digital manufacturing may address cost and time constraints associated with manufacturing 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 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: one for a sphere (digitally manufactured with titanium) and another for a cube (digitally manufactured with polymer).
Data to enable correlation model development by competitors are being incrementally released on the DMACE Web site through Dec. 1.
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