Data-Driven Concept Network for Inspiring Designers' Idea Generation

Big-data mining brings new challenges and opportunities for engineering design, such as customer-needs mining, sentiment analysis, knowledge discovery, etc. At the early phase of conceptual design, designers urgently need to synthesize their own internal knowledge and wide external knowledge to solve design problems. However, on the one hand, it is time-consuming and laborious for designers to manually browse massive volumes of web documents and scientific literature to acquire external knowledge. On the other hand, how to extract concepts and discover meaningful concept associations automatically and accurately from these textual data to inspire designers' idea generation? To address the above problems, we propose a novel data-driven concept network based on machine learning to capture design concepts and meaningful concept combinations as useful knowledge by mining the web documents and literature, which is further exploited to inspire designers to generate creative ideas. Moreover, the proposed approach contains three key steps: concept vector representation based on machine learning, semantic distance quantification based on concept clustering, and possible concept combinations based on natural language processing technologies, which is expected to provide designers with inspirational stimuli to solve design problems. A demonstration of conceptual design for detecting the fault location in transmission lines has been taken to validate the practicability and effectiveness of this approach.