Building a Design-Rationale-Centric Knowledge Network to Realize the Internalization of Explicit Knowledge

Abstract

A large number of publicly available documents, including patent documents and journal articles, can provide designers with creative stimuli, which could facilitate product innovation and collaborative design. As an important tacit knowledge, the acquisition, sharing, and reuse of design rationale (DR) is of great value to designers, which could help designers to better understand design intentions and ideas, support design automation, and promote better collaborative design. However, due to the fragmentation of DR in documentation, this hinders designer acquisition and reuse. If the DR fragments could be automatically extracted from the technical documents to build an interconnected knowledge network system, the problem would be effectively solved, which would further promote the development and utilization of digital archives. To address this issue, this study proposes a three-dimensional design knowledge network metamodel, Design Knowledge Semantic Network (DKSN), and a DKSN-based knowledge fusion method for the construction of a Design Knowledge Network (DKN). We set up an empirical experiment to verify the feasibility and performance of the method. Patent documents and open access research articles are used as sample documents, and a product data dictionary imported from ISO/TS 23768-1 is used as the predefined artifact dictionary. The results further confirm the feasibility and good application prospects of the proposed method.

Enhanced Rule Generation in Product Design Through Rough Set Theory and Ant Colony Optimization

“…In today's competitive market environment, the success of product design is directly related to a company's competitiveness [1]. However, the problems faced in the product design process are increasingly complex, and designers often face challenges such as information overload [2], knowledge fragmentation [3], and decision-making uncertainty [4]. Designers need to make efficient and precise decisions amidst this complexity [5] to meet market demands and maintain competitiveness.…”