Year: 2015
Editor: Christian Weber, Stephan Husung, Gaetano Cascini, Marco CantaMESsa, Dorian Marjanovic, Francesca Montagna
Author: Quintana-Amate, Santiago (1,2); Bermell-Garcia, Pablo (2); Balcazar, Luis (2); Tiwari, Ashutosh (1)
Series: ICED
Institution: 1: Cranfield University, United Kingdom; 2: Airbus Group Innovations, United Kingdom
Section: Innovation and Creativity
Page(s): 111-120
ISBN: 978-1-904670-71-1
ISSN: 2220-4334


Knowledge-Based Engineering (KBE) has been traditionally used to source engineering knowledge by integrating software and expertise, thus automating repetitive tasks and speeding up the engineering design process. However, to adequately perform the knowledge sourcing process it is a must to carry out an efficient capture, manage and reuse engineering knowledge.

In this regard, this paper presents a Knowledge Sourcing Framework (KSF) to methodologically source engineering knowledge. This research makes a novel contribution to current knowledge sourcing practices thanks to the proposed integration of expert and machine knowledge in a common environment. In doing so, a better link between knowledge acquisition and KBE is delivered. To achieve the main aim of this work, research efforts were focussed on: (i) identifying AI tools to extract engineering knowledge more efficiently; (ii) adopting a widely used methodology to allow the systematic capture and reuse of engineering knowledge. Finally, a case study has been successfully realised in the context of the aerospace industry, supporting the assumptions made in this research.

Keywords: Knowledge Management, Decision Making, Design Learning, Research Methodologies And Methods

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