Improving Failure Prediction in Aircraft Fastener Assembly Using Synthetic Data in Imbalanced Datasets
Journal:
arXiv
Published Date:
May 6, 2025
Abstract
Automating aircraft manufacturing still relies heavily on human labor due to
the complexity of the assembly processes and customization requirements. One
key challenge is achieving precise positioning, especially for large aircraft
structures, where errors can lead to substantial maintenance costs or part
rejection. Existing solutions often require costly hardware or lack
flexibility. Used in aircraft by the thousands, threaded fasteners, e.g.,
screws, bolts, and collars, are traditionally executed by fixed-base robots and
usually have problems in being deployed in the mentioned manufacturing sites.
This paper emphasizes the importance of error detection and classification for
efficient and safe assembly of threaded fasteners, especially aeronautical
collars. Safe assembly of threaded fasteners is paramount since acquiring
sufficient data for training deep learning models poses challenges due to the
rarity of failure cases and imbalanced datasets. The paper addresses this by
proposing techniques like class weighting and data augmentation, specifically
tailored for temporal series data, to improve classification performance.
Furthermore, the paper introduces a novel problem-modeling approach,
emphasizing metrics relevant to collar assembly rather than solely focusing on
accuracy. This tailored approach enhances the models' capability to handle the
challenges of threaded fastener assembly effectively.
