Data-driven simulation of DEVS-driven Digital Twins for smart manufacturing and Industry 4.0

Traditional modelling and simulation involves a human expert to manually design and create simulation models. However, these models quickly become obsolete for systems that continually change, such as smart manufacturing systems. This creates a need to create new simulation models which is difficult and expensive. Data-driven simulation extracts simulation models from data without explicit modelling from an expert. Digital Twins, virtual representations of physical objects, are considered one of the pillars of Industry 4.0. The DEVS formalism is an ideal implementation for the underlying Digital Twin simulation models due to their capability to model heterogeneous and complex systems. However, further research is needed in the development of algorithms to automatically extract the simulation models implemented in Digital Twins. This project focusses on extracting DEVS models using data-driven simulation with motivation in DEVS-driven Digital Twin applications for smart manufacturing and Industry 4.0.

Faculty Supervisor:

Gabriel Wainer

Student:

Partner:

Karlsruher Institut für Technologie

Discipline:

Engineering

Sector:

Education

University:

Carleton University

Program: