  As manufacturing processes become increasingly automated, so should tool
condition monitoring (TCM) as it is impractical to have human workers monitor
the state of the tools continuously. Tool condition is crucial to ensure the
good quality of products: Worn tools affect not only the surface quality but
also the dimensional accuracy, which means higher reject rate of the products.
Therefore, there is an urgent need to identify tool failures before it occurs
on the fly. While various versions of intelligent tool condition monitoring
have been proposed, most of them suffer from a cognitive nature of traditional
machine learning algorithms. They focus on the how to learn process without
paying attention to other two crucial issues: what to learn, and when to learn.
The what to learn and the when to learn provide self regulating mechanisms to
select the training samples and to determine time instants to train a model. A
novel tool condition monitoring approach based on a psychologically plausible
concept, namely the metacognitive scaffolding theory, is proposed and built
upon a recently published algorithm, recurrent classifier (rClass). The
learning process consists of three phases: what to learn, how to learn, when to
learn and makes use of a generalized recurrent network structure as a cognitive
component. Experimental studies with real-world manufacturing data streams were
conducted where rClass demonstrated the highest accuracy while retaining the
lowest complexity over its counterparts.
