Abstract
The effective capture of legacy knowledge and information during all aspects of the product development cycle is one of the biggest remaining challenges in engineering companies. Life Cycle Engineering requires the capture of engineering information and knowledge created during design sessions to support knowledge reuse, product reengineering and training. In the past, many attempts have been made to determine if this is possible; however, those that are partially successful are very time consuming, expensive to implement and interrupt the engineers' creativity. This work investigates and demonstrates new and novel paradigms for knowledge and information capture by adapting and applying a well recognised knowledge capture methodology to suit the non-intrusive automated real time logging, capture and post processing of engineering knowledge using a head-mounted display virtual reality (VR) design system. This logging is accomplished during individual cable harness design tasks carried out by 12 cable harness design engineers from five industrial partners to demonstrate the effective, unobtrusive and automatic capture and representation of various forms of engineering design knowledge and information. The formats were subsequently evaluated by the engineers to determining those they consider best at conveying design knowledge and information for other engineers.
Original language | English |
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Title of host publication | ASME 2011 World Conference on Innovative Virtual Reality |
Publisher | American Society of Mechanical Engineers |
Pages | 235-242 |
Number of pages | 8 |
ISBN (Print) | 9780791844328 |
DOIs | |
Publication status | Published - 2011 |
Event | ASME 2011 World Conference on Innovative Virtual Reality - Milan, Italy Duration: 27 Jun 2011 → 29 Jun 2011 |
Conference
Conference | ASME 2011 World Conference on Innovative Virtual Reality |
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Abbreviated title | WINVR 2011 |
Country/Territory | Italy |
City | Milan |
Period | 27/06/11 → 29/06/11 |
Keywords
- Cable harness design
- Design rationale
- Design task analysis
- Knowledge capture
- Knowledge representation
- User logging
ASJC Scopus subject areas
- Artificial Intelligence