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

Article dans une revue avec comité de lecture
Author
HUET, Armand
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]
PINQUIÉ, Romain
1043141 Conception collaborative [G-SCOP_CC]
VÉRON, Philippe
58355 École Nationale Supérieure des Arts et Métiers [ENSAM]
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]
MALLET, Antoine
244560 Capgemini [Toulouse]
ccSEGONDS, Frederic
127758 Laboratoire Conception de Produits et Innovation [LCPI]
301320 École Nationale Supérieure d'Arts et Métiers [ENSAM]

URI
http://hdl.handle.net/10985/19978
DOI
10.1016/j.compind.2020.103377
Date
2021
Journal
Computers in Industry

Abstract

The design of complex engineered systems highly relies on a laborious zigzagging between computer-aided design (CAD) software and design rules prescribed by design manuals. Despite the emergence of knowledge management techniques (ontology, expert system, text mining, etc.), companies continue to store design rules in large and unstructured documents. To facilitate the integration of design rules and CAD software, we propose a knowledge graph that structures a large set of design rules in a computable format. The knowledge graph organises entities of design rules (nodes), relationships among design rules (edges), as well as contextual information. The categorisation of entities and relationships in four sub-contexts: semantic, social, engineering, and IT – facilitates the development of the data model, especially the definition of the “design context” concept. The knowledge graph paves the way to a context-aware cognitive design assistant. Indeed, connected to or embedded in a CAD software, a context-aware cognitive design assistant will capture the design context in near real time and run reasoning operations on the knowledge graph to extend traditional CAD capabilities, such as the recommendation of design rules, the verification of design solutions, or the automation of design routines. Our validation experiment shows that the current version of the context-aware cognitive design assistant is more efficient than the traditional document-based design. On average, participants using an unstructured design rules document have a precision of 0.36 whereas participants using our demonstrator obtain a 0.61 precision score. Finally, designers supported by the design assistant spend more time designing than searching for applicable design rules compared to the traditional design approach.

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