  Future manufacturing requires complex systems that connect simulation
platforms and virtualization with physical data from industrial processes.
Digital twins incorporate a physical twin, a digital twin, and the connection
between the two. Benefits of using digital twins, especially in manufacturing,
are abundant as they can increase efficiency across an entire manufacturing
life-cycle. The digital twin concept has become increasingly sophisticated and
capable over time, enabled by rises in many technologies. In this paper, we
detail the cognitive digital twin as the next stage of advancement of a digital
twin that will help realize the vision of Industry 4.0. Cognitive digital twins
will allow enterprises to creatively, effectively, and efficiently exploit
implicit knowledge drawn from the experience of existing manufacturing systems.
They also enable more autonomous decisions and control, while improving the
performance across the enterprise (at scale). This paper presents graph
learning as one potential pathway towards enabling cognitive functionalities in
manufacturing digital twins. A novel approach to realize cognitive digital
twins in the product design stage of manufacturing that utilizes graph learning
is presented.
