  Digital Twins are part of the vision of Industry 4.0 to represent, control,
predict, and optimize the behavior of Cyber-Physical Production Systems
(CPPSs). These CPPSs are long-living complex systems deployed to and configured
for diverse environments. Due to specific deployment, configuration, wear and
tear, or other environmental effects, their behavior might diverge from the
intended behavior over time. Properly adapting the configuration of CPPSs then
relies on the expertise of human operators. Digital Twins (DTs) that reify this
expertise and learn from it to address unforeseen challenges can significantly
facilitate self-adaptive manufacturing where experience is very specific and,
hence, insufficient to employ deep learning techniques. We leverage the
explicit modeling of domain expertise through case-based reasoning to improve
the capabilities of Digital Twins for adapting to such situations. To this
effect, we present a modeling framework for self-adaptive manufacturing that
supports modeling domain-specific cases, describing rules for case similarity
and case-based reasoning within a modular Digital Twin. Automatically
configuring Digital Twins based on explicitly modeled domain expertise can
improve manufacturing times, reduce wastage, and, ultimately, contribute to
better sustainable manufacturing.
