The automated generation of engineering knowledge using a digital engineering tool: An industrial evaluation case study

Raymond Sung, James Millar Ritchie, Theodore Lim, Ying Liu, Zoe Kosmadoudi

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
162 Downloads (Pure)


In a knowledge-based economy, it will be crucial to capture expertise and rationale in working environments of all kinds as the need develops to understand how people are working, the intuitive processes they use as they carry out tasks and make decisions and trying to determine the most e®ective methods and rationales for solving problems. Key outputs from this will be
the capability to automate decision making activities and supporting training and learning in competitive business environments. Knowledge capture in knowledge-based economies will also be important in a wide range of sectors from the ¯nancial and business domains through to engineering and construction. In traditional expert environments, current manual knowledge capture techniques tend to be time-consuming, turgid and, if applied during an activity, interrupt
the \expert" whilst they are carrying out the task. The alternative is to do this after the event, which loses important information about the process due to the individual usually forgetting a great deal of the decisions and alternatives they have used during a task session. With the advent and widespread use of computerized technology within business, this paper contends that new opportunities exist with regard to user logging and subsequent data analysis which mean that there is considerable potential for automating or semi-automating this kind of knowledge capture.
As a case study demonstrating the possibility of attaining automated knowledge capture, this work investigates product design. Within long lifecycle products of all kinds there is a need to capture the engineering rationale, process, information and knowledge created during a design session. Once these data has been captured, in an automated and unobtrusive manner, it must be represented in a fashion which allows it to be easily accessible, understandable, stored and reused at a later date. This can subsequently be used to inform experienced engineers of decisions taken much earlier in the design process or used to train and support inexperienced engineers while they are moving up the learning curve. Having these data available is especially important in long lifecycle projects since many design decisions are made early on in the process and are then required to be understood by engineers a number of years down the line. There is also the likelihood that if an engineer were to leave during the project, any undocumented design knowledge relating to their contribution to the design process will leave with them. This paper describes research on non-intrusively capturing and formalizing product lifecycle knowledge by demonstrating the automated capture of engineering processes through user
logging using an immersive virtual reality (VR) system for cable harness design and assembly planning. Furthermore, several industrial collaborators of the project have been visited to determine what their knowledge capture practices are; these fndings are also detailed.
Computerized technology and business management systems in the knowledge-based economies of the future will require the capture of expertise as quickly and effectively as possible with minimum overhead to the company along with the formal storage and access to such key data. The application of the techniques and knowledge representations presented in this paper demonstrate the potential for doing this in both engineering and non-engineering domains.
Original languageEnglish
Article numberWSPC/195-IJITM
Pages (from-to)1250047-1:20
Number of pages20
JournalInternational Journal of Innovation and Technology Management
Issue number6
Publication statusPublished - 29 Mar 2013


  • Knowledge capture
  • knowledge representation
  • virtual reality

ASJC Scopus subject areas

  • Engineering(all)
  • Decision Sciences(all)
  • Computer Science (miscellaneous)
  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction
  • Information Systems
  • Software


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