  The digitization of the productive ecosystem related to human-machine
interaction has become a priority for small and medium-sized enterprises.
Particularly to face the challenges of Industry 4.0 and advanced digital skills
in the workplace. From the research point of view, digitization opens a global
scenario for the generation of opportunities for learning in existing
manufacturing systems. Mainly, traditional environments must deal with
competitive pressures to incorporate new technologies and adapt the skills of
workers. In this paper is presented the iDIGIT4L project, which was envisaged
to research and develop a digitization ecosystem where people and systems
interact in order to transform industrial processes in an intelligent and
predictive way. The project contributes with a three-tier human-machine
learning methodology that provides augmented and bi-directional interaction in
a traditional manufacturing scenario. It is based on the implementation of a
non-intrusively integrated digital twin, characterizing an old industrial
milling machine for learning through knowledge models supported by the
experience of skilled workers. As a result, it has been possible to
simultaneously update the functionalities of the industrial system and the
digital skills of the workers, becoming an integral part of the digital twin.
