  Machine learning (ML) methods are widely used in industrial applications,
which usually require a large amount of training data. However, data collection
needs extensive time costs and investments in the manufacturing system, and
data scarcity commonly exists. Therefore, data-sharing is widely enabled among
multiple machines with similar functionality to augment the dataset for
building ML methods. However, distribution mismatch inevitably exists in their
data due to different working conditions, while the ML methods are assumed to
be built and tested on the dataset following the same distribution. Thus, an
Active Data-sharing (ADs) framework is proposed to ensure the quality of the
shared data among multiple machines. It is designed to simultaneously select
the most informative data points benefiting the downstream tasks and mitigate
the distribution mismatch among all selected data points. The proposed method
is validated on anomaly detection on in-situ monitoring data from three
additive manufacturing processes.
