Predicting shim gaps in aircraft assembly with machine learning and sparse sensing

no code implementations • 24 Nov 2017 • Krithika Manohar, Thomas Hogan, Jim Buttrick, Ashis G. Banerjee, J. Nathan Kutz, Steven L. Brunton

This new approach is based on the assumption that patterns exist in shim distributions across aircraft, which may be mined and used to reduce the burden of data collection and processing in future aircraft.

BIG-bench Machine Learning