Supporting connectivism in knowledge based engineering with graph theory, filtering techniques and model quality assurance

[EN] Mass-customization has forced manufacturing companies to put significant efforts to digitize and automate their engineering and production processes. When new products are to be developed and introduced the production is not alone to be automated. The application of knowledge regarding how the product should be designed and produced based on customer requirements also must be automated. One big academic challenge is helping industry to make sure that the background knowledge of the automated engineering processes still can be understood by its stakeholders throughout the product life cycle. The research presented in this paper aims to build an infrastructure to support a connectivistic view on knowledge in knowledge based engineering. Fundamental concepts in connectivism include network formation and contextualization, which are here addressed by using graph theory together with information filtering techniques and quality assurance of CAD-models. The paper shows how engineering knowledge contained in spreadsheets, knowledge-bases and CAD-models can be penetrated and represented as filtered graphs to support a connectivistic working approach. Three software demonstrators developed to extract filtered graphs are presented and discussed in the paper.The work presented has evolved during the IMPACT project, funded by the Swedish Knowledge Foundation, and has been partly presented on three conferences [8-10]. The three conference papers show the rendering of graphs for CAD-models, spread sheets and KBE-rules together with the first case example in this article. The work has also been partially supported by grant DPI2017-84526-R (MINECO/AEI/FEDER, UE), project CAL-MBE.Johansson, J.; Contero, M.; Company, P.; Elgh, F. (2018). Supporting connectivism in knowledge based engineering with graph theory, filtering techniques and model quality assurance. Advanced Engineering Informatics. 38:252-263. https://doi.org/10.1016/j.aei.2018.07.005S2522633

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