America Makes and US Air Force Research Laboratory launch AM Modeling Challenge Series
November 30, 2019
America Makes, Youngstown, Ohio, USA, and the USA’s Air Force Research Laboratory, Materials & Manufacturing Directorate Structural Materials, Metals Branch (AFRL/RXCM), have launched an Additive Manufacturing Modeling Challenge Series comprised of four individual challenges, with $150,000 to be divided among the awardees.
America Makes stated that the goal of the AFRL AM Challenge Series is to make high-pedigree calibration data sets available to modellers to use in the calibration of the developed models, as it directly relates to predicting the internal structure and resultant performance of AM metal components.
Specifically, the series seeks to account for material heterogeneity intelligently through geometry-sensitive property prediction at both the micro- and macro-structure level. Models and simulations that can accurately account for this type of variability can be utilised by the AM design process and may be critical when designing complex parts with thin features.
By challenging both industry and academia, the organisers hope that the challenge will be influential in the development of solutions focused on validating/improving the accuracy of model predictions for metal AM.
“We are excited that the AFRL AM Modeling Challenge Series is now underway,” commented John Wilczynski, America Makes’ Executive Director. “The implications of the AFRL AM Modeling Challenge Series on the AM modelling and simulation area are substantial. We are optimistic that the Challenge Series will significantly improve the predictability and accuracy of models and simulations and the qualification of AM processes and materials.”
All four challenges will be based on the modelling of Inconel nickel-chromium alloy 625, as this material’s high corrosion resistance, elevated temperature performance, excellent fatigue and thermal-fatigue properties make it highly relevant to aerospace applications.
Challenge participants will be asked to develop models and algorithms that produce dynamic material property prediction module(s), sensitive to geometry and local processing state. The challenge is open to academia, small and large businesses, and national laboratories, both in the USA and internationally. Data packages will be publicly released by AFRL and made available to participants.
