Discovery and digital model generation for manufacturing systems with assembly operations

Industry 4.0 determined the emergence of technologies which allow for data-based production planning and control approaches. Digital twins can be used to take decisions based on the current system state. Hence, their performance strictly depends on the capability to correctly represent their physical counterparts at any time. The development of digital twins for manufacturing systems can be significantly accelerated by automated model generation techniques. However, production systems including assembly stations suffer from event records with multiple part identifiers, resulting in the wrong finding of the system structure. In this paper, we define the problem of the proper discovery of assembly operations. Then, we describe an algorithm to generate a complete digital model exploiting the new concept of object-centric process mining. In a case study, a flow shop including assembly stations is successfully discovered, allowing for the automated building of a simulation model with the proper logical behavior.