  Ultrasonic metal welding (UMW) is a key joining technology with widespread
industrial applications. Condition monitoring (CM) capabilities are critically
needed in UMW applications because process anomalies significantly deteriorate
the joining quality. Recently, machine learning models emerged as a promising
tool for CM in many manufacturing applications due to their ability to learn
complex patterns. Yet, the successful deployment of these models requires
substantial training data that may be expensive and time-consuming to collect.
Additionally, many existing machine learning models lack generalizability and
cannot be directly applied to new process configurations (i.e., domains). Such
issues may be potentially alleviated by pooling data across manufacturers, but
data sharing raises critical data privacy concerns. To address these
challenges, this paper presents a Federated Transfer Learning with Task
Personalization (FTL-TP) framework that provides domain generalization
capabilities in distributed learning while ensuring data privacy. By
effectively learning a unified representation from feature space, FTL-TP can
adapt CM models for clients working on similar tasks, thereby enhancing their
overall adaptability and performance jointly. To demonstrate the effectiveness
of FTL-TP, we investigate two distinct UMW CM tasks, tool condition monitoring
and workpiece surface condition classification. Compared with state-of-the-art
FL algorithms, FTL-TP achieves a 5.35%--8.08% improvement of accuracy in CM in
new target domains. FTL-TP is also shown to perform excellently in challenging
scenarios involving unbalanced data distributions and limited client fractions.
Furthermore, by implementing the FTL-TP method on an edge-cloud architecture,
we show that this method is both viable and efficient in practice. The FTL-TP
framework is readily extensible to various other manufacturing applications.
