Title: Predicting shim gaps in aircraft assembly with machine learning and sparse sensing,
Abstract: A modern aircraft may require on the order of thousands of custom shims to
fill gaps between structural components in the airframe that arise due to
manufacturing tolerances adding up across large structures. These shims are
necessary to eliminate gaps, maintain structural performance, and minimize
pull-down forces required to bring the aircraft into engineering nominal
configuration for peak aerodynamic efficiency. Gap filling is a time-consuming
process, involving either expensive by-hand inspection or computations on vast
quantities of measurement data from increasingly sophisticated metrology
equipment. Either case amounts to significant delays in production, with much
of the time spent in the critical path of aircraft assembly. This work presents
an alternative strategy for predictive shimming, based on machine learning and
sparse sensing to first learn gap distributions from historical data, and then
design optimized sparse sensing strategies to streamline data collection and
processing. 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. Specifically,
robust principal component analysis is used to extract low-dimensional patterns
in the gap measurements while rejecting outliers. Next, optimized sparse
sensors are obtained that are most informative about the dimensions of a new
aircraft in these low-dimensional principal components. We demonstrate the
success of the proposed approach, called PIXel Identification Despite
Uncertainty in Sensor Technology (PIXI-DUST), on historical production data
from 54 representative Boeing commercial aircraft. Our algorithm successfully
predicts $99\%$ of shim gaps within the desired measurement tolerance using
$3\%$ of the laser scan points typically required; all results are
cross-validated.