Speaker
Description
One of the central questions of the digital transformation is how Industry 4.0 can be utilised to fulfil all the production system requirements while following state-of-the-art developments. Effective information management is critical for the improvement of manufacturing processes. This research high- lights that ontologies are suitable for manufacturing management and recommends the multilayer-network based interpretation and analysis of ontology- based databases. A wire harness assembly-based case study will serve as an illustrative example to demonstrate how ontology-based modelling can be utilised with network science tools for system analysis.
Our first goal is to provide an overview of describing the production standards using the most critical elements of semantics & syntax. Furthermore, provide the necessary theoretical background to understand the specific semantic models used in ontologies and description methods of a manufacturing sys- tem. The main principles of semantic modelling are the RDF (Resource Description Framework) [1] and OWL (Web Ontology Language) [2]. The RDF Schema provides interoperability between applications that exchange machine- understandable information on the Web. OWL can develop domain-specific schemas and ontologies (so-called meta-models) and represent the meaning of terms in vocabularies and the relationships between such terms. RDF triples can be utilised to extend a graph between unique data instances, collect general data as well as express semantics, attributes, and hierarchies [3].
The second goal is to investigate the defined descriptive and influential factors with data queries and analyses. For data extraction, we applied SPARQL (Structured Protocol and RDF Query Language) queries, which most significant benefit is creating a structured version from the data stored in the ontology or extracting data from RDF, which is an excellent source to manage basic production analysis. However, if a more in-depth investigation is needed or the production ontology has high complexity, the tools of Data Science can provide more accurate solutions.
Because of the outstanding network analysis and visualisation capabilities, it is worth generating labelled multilayer networks from ontology models and graph databases [4, 5]. The MuxViz software application [6, 7] has been used to create multilayer graph representations to explore further analytical possibilities. The methodology of RDF triples how to link data is the same as in the case of bipartite graphs (which are used during the multilayer formation). Studies are published to formalise the bipartite graphs as an intermediate model for RDF with a goal of graph-based notions in querying and storage [8]. Further- more, an RDF database can be simultaneously analysed as layers of a multilayer network [9], providing a solution for Production Flow Analysis.
And finally, as part of the multilayer system analysis the effective serialisation of nodes and clustering of data points can be utilised in a wide range of applications, such as optimal allocation or analysing complex data-sets to discover patterns and internal information in networks.
We created a benchmark model using ontologies, which can serve as an effective methodology and also provide critical messages during the development for industrial and research parties as well. The reproducible industrial case study (based on wire harness assembly manufacturing process) can give guidance on understanding the ontological modeling of a manufacturing process. We performed an assembly line balancing utilising data science tools based on these data and methodology, which contain further potential analysis and development methods. Based on the results, we concluded that detectable clusters and communities within these networks can facilitate the formation of production cells or grouping resources in the process.
References
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[2] D. L. McGuinness, F. Van Harmelen, et al., Owl web ontology language overview, W3C recommendation 10 (10) (2004) 2004.
[3] A. Brodt, D. Nicklas, B. Mitschang, Deep integration of spatial query processing into native rdf triple stores, in: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2010, pp. 33–42.
[4] T. Ruppert, G. Honti, J. Abonyi, Multilayer network-based production flow analysis, Complexity 1 (2018).
[5] G. Honti, G. Dorgo, J. Abonyi, Network analysis dataset of system dynamics models, Data in brief 27 (2019).
[6] M. De Domenico, M. A. Porter, A. Arenas, Muxviz: a tool for multilayer analysis and visualization of networks, Journal of Complex Networks 3 (2) (2015) 159–176.
[7] M. D. Domenico, Muxviz - the multilayer analysis and visualization platform., https://muxviz.net/ (2020).
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[9] G. Honti, J. Abonyi, Frequent itemset mining and multi-layer network-based analysis of rdf
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