Paper Info
Title
A function-based computational method for design concept evaluation.
Abstract
Concept generation is an indispensable step of innovation design. However, the limited knowledge and design thinking fixation of designers often impede the generation of novel design concepts. Computational tools can be a necessary supplement for designers. They can generate a big number of design concepts based on an existing knowledge base. For filtering these design concepts, this work presents a computational measurement of novelty, feasibility and diversity based on 500,000 granted patents. First, about 1700 functional terms (terminologies) are mapped to high dimensional vectors (100 dimensional space) by word embedding technique. The resulted database is knowledge base-I (KB-I). Then, we adopt circular convolution to convert patents into high dimensional vectors. The resulted database is KB-II. Based on the two knowledge bases, the computational definitions of novelty, feasibility and diversity are developed. We conduct six experiments based on KB-II, a random dataset and a real product dataset, and the results show that these metrics can be used to roughly filter a big number of design concepts, and then expert-based method can be further used. This work provides a computational framework for measuring the novelty, feasibility and diversity of design concept.
Year
DOI
Venue
2017
10.1016/j.aei.2017.03.002
Advanced Engineering Informatics
Keywords
Field
DocType
Concept generation,Evaluation metrics,Function basis,Word-embedding
Data mining,Computer science,Design thinking,Filter (signal processing),Circular convolution,Artificial intelligence,Novelty,Word embedding,Knowledge base,Concept evaluation,Machine learning
Journal
Volume
ISSN
Citations 
32
1474-0346
2
PageRank 
References 
Authors
0.36
11
3
Name
Order
Citations
PageRank
Jia Hao193.67
Qiangfu Zhao221462.36
Yan Yan3145.76