PhD in Engineering Design (2014-now, expected 2019 July)
Binyang Song is a Ph.D. student at Engineering Product Development pillar, Singapore University of Technology and Design. She received his B.S. degree in Automotive Engineering from Tsinghua University (THU), Beijing, China, in 2011, and M.S. degree in power engineering and engineering thermophysics from Tsinghua University, in 2014.
Her research interests are focused on intelligent data-driven engineering design, including:
Patent Precedent Retrieving
We propose an iterative and heuristic methodology to comprehensively search for patents as precedents of the design of a specific technology or product for data-driven design. The patent retrieval methodology integrates the mining of patent texts, citation relationships and inventor information to identify relevant patents; particularly, the search keyword set, citation network, and inventor set are expanded through the designer’s heuristic learning from the patents identified in prior iterations. The method relaxes the requirement for initial search keywords while improving patent retrieval completeness and accuracy.
Design Knowledge Base Expansion
introduces a network-based methodology for visualizing and analyzing the structure and expansion trajectories of the design knowledge base of a given technology domain. The methodology is centered on overlaying the total technology space, represented as a network of all known technologies based on patent data, with the specific knowledge positions and estimated expansion paths of a specific domain as a subgraph of the total network. We demonstrate the methodology via a case study of hybrid electric vehicles. The methodology may help designers understand the technology evolution trajectories of their domain and suggest next design opportunities or directions.
Impact of Knowledge Relatedness on Ideation Outcomes
Prior studies on design ideation have demonstrated the efficacy of using patents as stimuli for concept generation. However, the following questions remain: (a) From which part of the large patent database can designers identify stimuli? (b) What are their implications on ideation outcomes? This research aims to answer these questions through a design experiment of searching and identifying patent stimuli to generate new concepts of spherical rolling robots. We position the identified patent stimuli in the home, near and far fields defined in the network of patent technology classes, according to the network’s community structure and the knowledge proximity of the stimuli to the spherical rolling robot design. Significant findings are: designers are most likely to find patent stimuli in the home field, whereas most patent stimuli are identified in the near field; near-field patents stimulate the most concepts, which exhibit a higher average novelty; combined home- and far-field stimuli are most beneficial for high concept quality.
Platform Design Based on Network Analysis
we propose a data-driven method to draw the boundary of a platform, complementing other platform design approaches and assisting designers in the architecting process. The method generates a network of functions through relationships of their co-occurrences in prior designs of a product domain and uses a network analysis algorithm to identify an optimal core-periphery structure. Functions identified in the network core co-occur cohesively and frequently with one another in prior designs, and thus are suggested for inclusion in the potential platform to be shared across a variety of product-systems with peripheral functions. We apply the method to identifying the platform functions for spherical rolling robots, based on patent data.
Refereed Journal Papers (published or accepted)
Refereed Journal Papers (submitted and under review)
Refereed Conference Papers
Graduate Teaching Assistant, SUTD