Ontology model for process level capabilities of manufacturing resources

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The promise of distributed cloud manufacturing (CM) is to manufacture products using shared resources, both asset-full (machines, tools, vehicles) and asset-light (design, analysis, inspection, management, maintenance), which can be provisioned flexibly and rapidly with minimal management and service provider interaction. One of the key enabler of CM are virtual enterprises (VE), which offer manufacturing resources as virtual services (SaaS, HaaS, PaaS) in a cloud based marketplace. Currently, virtualization and provisioning of diverse array of manufacturing resources face challenges from the heterogeneity in representation and communication protocols, as well as lack of integration with legacy practices in the organizations. Aiming to increase interoperability, a number of formal ontologies were developed by researchers in the past, to leverage on semantic data integration and validation. In spite of their success in providing axiomatic description and common taxonomy to classify manufacturing resources from different domains, models of representing the capabilities of the resources (i.e. expected quality of services they offer) were often overlooked. In this research, we present an ontology model to represent capabilities of manufacturing machine-tools at the process level, often called process boundaries (measured by process capability index) in industries. The definition of the capability is derived based on the foundational ontology called 'Basic Formal Ontology' (BFO). The primary contribution of this extension is a set of OWL axioms which can be used to assert facts about modal future (possibilia) - ultimately enabling us to associate process specific performance metrics to the semantic models of virtual machine-tools.




Sarkar, A., & Šormaz, D. (2019). Ontology model for process level capabilities of manufacturing resources. In Procedia Manufacturing (Vol. 39, pp. 1889–1898). Elsevier B.V. https://doi.org/10.1016/j.promfg.2020.01.244

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