Model-Driven Development of a Digital Twin for Injection Molding

This presentation was created for the virtual CAiSE 2020 and describes the paper "Model-Driven Development of a Digital Twin for Injection Molding" written by Pascal Bibow, Manuela Dalibor, Christian Hopmann, Ben Mainz, Bernhard Rumpe, David Schmalzing, Mauritius Schmitz, and Andreas Wortmann.
Abstract:
Digital Twins (DTs) of Cyber-Physical Production Systems
(CPPSs) enable the smart automation of production processes, collection
of data, and can thus reduce manual efforts for supervising and controlling
CPPSs. Realizing DTs is challenging and requires significant efforts
for their conception and integration with the represented CPPS. To mitigate
this, we present an approach to systematically engineering DTs
for injection molding that supports domain-specific customizations and
automation of essential development activities based on a model-driven
reference architecture. In this approach, reactive CPPS behavior is de-
ned in terms of a Domain-Specific Language (DSL) for specifying events
that occur in the physical system. The reference architecture connects to
the CPPS through a novel DSL for representing OPC-UA bindings. We
have evaluated this approach with a DT of an injection molding machine
that controls the machine to optimize the Design of Experiment (DoE)
parameters between experiment cycles before the products are molded.
Through this, our reference implementation of the DT facilitates the
time-consuming setup of a DT and the subsequent injection molding activities.
Overall, this facilitates to systematically engineer digital twins
with reactive behavior that help to optimize machine use.