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

Abstract

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.

Recent Work in Connectivism

“…"The connectivistic view includes elements like emotions, recent experiences, beliefs, and the surrounding environment (and) online social networks which are adaptive, fluid, and readily scalable in size and scope." (Johansson et al, 2018)…”

CACDA: A knowledge graph for a context-aware cognitive design assistant

“…SQWRL), and software (e.g. Protégé) for structuring and reasoning about domain-specific information such as geometry and topology (Tessier and Wang, 2013;Sanya and Shehab, 2014), feature recognition (Wang and Yu, 2014), generative modelling (Skarka, 2007), connectivistic design reuse (Johansson et al, 2018) or nuclear design rules (Fortineau et al, 2014). Nevertheless, knowledge graphs are extremely time-consuming to be developed and managed.…”

“…It is therefore interesting to automate the acquisition and processing of knowledge. For example, Johansson et al (Johansson et al, 2018) automate the extraction of structured engineering knowledge from spreadsheets, knowledge-based engineering programs and CAD models. Natural language processing and text mining techniques can be used to process unstructured information sources (Shi et al, 2017;Kang et al, 2015).…”

Review and Trend Analysis of Knowledge Graphs for Crop Pest and Diseases

“…At present, most search engines at home and abroad have been improved to semantic search based on knowledge map. Semantic search based on knowledge graph technology enables computers to truly understand user's needs and provide accurate answers rather than related link sequences [78]. When a user enters a query question, the system first processes the sentence, including entity recognition, syntactic analysis, and semantic analysis.…”