Capturing engineering designers’ knowledge and experience on the design of an artefact is important as this knowledge can explain why the artefact has been designed as it is, how key decisions have been made and what important issues have been considered. This tacit design knowledge enables designers to make informed decisions and improve efficiency in similar projects in the future. However, the capture and reuse of this kind of knowledge reminds to be great challenge, as it often exists in designers’ brains and is difficult to codify. Previous research is predominantly focused on the explicit knowledge of design objects that can be codified rather than the underlying tacit knowledge which explains the problem-solving strategies and decision-making processes. Additionally, engineering design is increasingly conducted in a collaborative working environment enable by the state-of-the-art information technologies. This trend has highly influenced the ways of knowledge capture and reuse, while is not well addressed by existing research. To fill these gaps, this research aims to explore new systematic methods and knowledge models for the capture and reuse of design knowledge as well as for the development of the next-generation knowledge management systems for engineering design. The development and application of these systematic methods and knowledge models requires a good understanding of the new requirements of knowledge management for engineering design, involving interdisciplinary research work across engineering and computing science. Thus, a comprehensive methodology is employed in this research, which consists of three parts. Firstly, a requirement analysis is undertaken through a literature review and a survey study to identify designers’ information needs and information-seeking behaviours within the new context. Secondly, the characteristics of engineering design knowledge are analysed, and on this basis a knowledge framework and a knowledge representation model are developed to support knowledge categorisation and representation. Thirdly, a methodology for applying these methods is analysed in order to design and develop a prototype system for implementation. Through the evaluation of both the proposed methods and system in a number of engineering design projects, the models have been proved to be capable and efficient in capturing design knowledge for better reuse, while the system not only proves the feasibility of the proposed methods but also provides the prototype of the next-generation collaborative knowledge management system.
|Date of Award||2016|
|Supervisor||Hongwei Wang (Supervisor), Hom Dhakal (Supervisor) & Jie Tong (Supervisor)|