A product-process-resource based formal modelling framework for customized manufacturing in cyber-physical production systems

ABSTRACT

The development of cyber-physical production systems (CPPSs) as the core of intelligent manufacturing systems facilitates frequent changes in product variants and batches, dynamic configurations of processing resources and relevant parameters during customized manufacturing. Production prediction, scheduling, and optimization in such a complex system require a consolidated theoretical foundation. Formal techniques, as one of the critical enablers toward this end, can be used to detect and analyse malfunctions in a CPPS based on product information before starting production. However, the inconsistency of information terminology during the interaction between heterogeneous production system components may lead to semantics-level ambiguities during the formal modelling process. This paper proposes a formal modelling framework for CPPS based on mathematical definitions of the unified ‘Product-Process-Resource’ concept. A factory formal model language (FFML) is defined and applied to the modelling of a real-world production scenario. The applicability and accuracy of the proposed framework are verified by transforming constructed formal models to coloured Petri-nets for simulations. The verification results have a certain guiding significance to the layout design and scheduling strategy decisions of production lines.

KEYWORDS:

• Cyber-physical production system
• factory formal modelling language
• product process resource
• model validation

Acknowledgments

This work was supported by the National Key Research and Development Program of China (Grant No. 2018YFB1700500), the Natural Science Foundation of Guangdong Province (Grant No. 2020A1515011206), and the Science and Technology Planning Project of Guangdong Province (Grant No. 2017B090914002).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Natural Science Foundation of Guangdong Province for Distinguished Young Scholars [2020A1515011206]; Science and Technology Planning Project of Guangdong Province [2017B090914002]; National Key Research and Development Program of China [2018YFB1700500].