Ontologies for Industry 4.0

The current fourth industrial revolution, or ‘Industry 4.0’ (I4.0), is driven by digital data, connectivity, and cyber systems, and it has the potential to create impressive/new business opportunities. With the arrival of I4.0, the scenario of various intelligent systems interacting reliably and securely with each other becomes a reality which technical systems need to address. One major aspect of I4.0 is to adopt a coherent approach for the semantic communication in between multiple intelligent systems, which include human and artificial (software or hardware) agents. For this purpose, ontologies can provide the solution by formalizing the smart manufacturing knowledge in an interoperable way. Hence, this paper presents the few existing ontologies for I4.0, along with the current state of the standardization effort in the factory 4.0 domain and examples of real-world scenarios for I4.0.Peer ReviewedPostprint (published version

Ontology based semantic engineering framework and tool for reconfigurable automation systems integration

Digital factory modelling based on virtual design and simulation is now emerging as a part of mainstream engineering activities, and it is typically geared towards reducing the product design cycle time. Reconfigurable manufacturing systems can benefit from reusing the existing knowledge in order to decrease the required skills and design time to launch new product generations. The various industrial simulation systems are currently integrating product design, matching processes and resource requirements to decrease the required skills and design time to launch new products. However, the main focus of current reconfigurable manufacturing systems has been modular production lines to support different manufacturing tasks. Additionally, the design data is not transferrable from various domain-specific software to a collaborative and intelligent platform, which is required to capture and reuse design knowledge. Product design is still dependent on the knowledge of designers and does not link to the existing knowledge on processes and resources, which are in separate domains. To address these issues, this research developed an integration method based on semantic technologies and product, process, resource and requirements (PPRR) ontologies called semantic-ontology engineering framework (SOEF). SOEF transferred original databases to an ontology-based automation data structure with a semantic analysis engine. A pre-defined semantic model is developed to recognise custom requirement and map existing knowledge with processing data in the automation assembly aspect. The main research contribution is using semantic technology to process automation documentation and map semantic data to the PPRR ontology structure. Furthermore, this research also contributes to the automatic modification of system simulation based on custom requirements. The SOEF uses a JAVA-based command-line user interface to present semantic analysis results and import ontology outputs to the vueOne system simulation tool for system evaluation

Automatic data representation analysis for reconfigurable systems integration

Digital factory modelling based on virtual design and simulation has emerged as part of the mainstream activities geared manufacturing digitalisation and Industry 4.0. Some commercial product and manufacturing process modelling tools are currently integrated via semantic modelling technologies to match product with required processes and resources to achieve production requirements such as volume, cost etc. Despite these achievements, product design is still dependent on the knowledge of designers and do not benefit from existing process and resource knowledge, which are in separate domains. This paper therefore presents an integration method based on semantic technologies and PPR ontologies to reuse existing process and resource knowledge and automatically map it with product attributes and production requirements. The approach will help in improving efficiency of the response to frequently changing products and production requirements