Since the industrial revolution, the automated acquisition and reuse of expert knowledge throughout the product life-cycle has been the holy grail of engineering. Yet recent findings [1, 2] determined in discussions with industry suggest that the uptake of such systems remains low, not least due to issues regarding implementation, costs, functionality, organizational implications, and ethics, amongst others [3, 4]. Indeed the requirements of different industrial sectors mean that there are still many challenges. For those that have embraced the technology, cost in terms of implementation and usability are key issues and many of the current tools and methods are labour-intensive, timeconsuming, and difficult to embed in engineering systems. Another key barrier is the effective access to and reuse of this knowledge in a timely and convenient manner . Knowledge capture and reuse are critical to the industrial competitiveness and are at the heart of any knowledge management process . In biopharmaceutical clinical trials, Grossman and Bates  indicated that considerable time and cost savings could be achieved through automated knowledge capture. The World Bank  reported that: “The Bank lacks the ability to efficiently retrieve and share the large volume of embedded knowledge generated during preparation and implementation of lending operations.” These articles show that advanced organizations still lack well-developed mechanisms to capture and share knowledge. Another threat is that legacy knowledge can be lost when experienced engineers leave or retire from companies and, as a consequence, that lessons learnt should be easily reused to inform and educate inexperienced personnel.
|Name||Advances in Computers and Information in Engineering Research|
|Publisher||American Society of Mechanical Engineers|
- Virtual reality
- Knowledge management
- Engineering systems