Towards a Generalised Information Modell: A Bayesian Network Approach for PPR-Representation

The advancement of Industry 4.0 necessitates the abstraction of production processes to de-velop software-defined value streams. Existing approaches often suffer from limitations such as inflexibility and lack of scalability when individually modelling complex, heterogeneous production processes. These limitations hinder effective integration and interoperability within the Industry 4.0 framework. Given these challenges, this approach introduces an innovative "one size fits all" information model, enabling comprehensive capability matching across di-verse production systems. The proposed graph-based methodology enables the probabilistic description of products and their features resulting from various production processes. This al-so enables the representation and investigation of their part-specific dependencies. The devel-oped graph-based representation will be demonstrated on a complex product through ele-mental manufacturing processes. This standardises the description format of these production technologies. This crucial standardisation enables addressing the complexities of modern in-dustrial processes. It also supports deploying software-defined value streams, optimising effi-ciency and adaptability in smart manufacturing environments