  Digital Twins have been described as beneficial in many areas, such as
virtual commissioning, fault prediction or reconfiguration planning. Equipping
Digital Twins with artificial intelligence functionalities can greatly expand
those beneficial applications or open up altogether new areas of application,
among them cross-phase industrial transfer learning. In the context of machine
learning, transfer learning represents a set of approaches that enhance
learning new tasks based upon previously acquired knowledge. Here, knowledge is
transferred from one lifecycle phase to another in order to reduce the amount
of data or time needed to train a machine learning algorithm. Looking at common
challenges in developing and deploying industrial machinery with deep learning
functionalities, embracing this concept would offer several advantages: Using
an intelligent Digital Twin, learning algorithms can be designed, configured
and tested in the design phase before the physical system exists and real data
can be collected. Once real data becomes available, the algorithms must merely
be fine-tuned, significantly speeding up commissioning and reducing the
probability of costly modifications. Furthermore, using the Digital Twin's
simulation capabilities virtually injecting rare faults in order to train an
algorithm's response or using reinforcement learning, e.g. to teach a robot,
become practically feasible. This article presents several cross-phase
industrial transfer learning use cases utilizing intelligent Digital Twins. A
real cyber physical production system consisting of an automated welding
machine and an automated guided vehicle equipped with a robot arm is used to
illustrate the respective benefits.
