SDC, Data Science and Knowledge

Pei Zhang

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PhD student in the SDC team (formerly BFO team) of the ICube laboratory of the University of Strasbourg since October 2012.


ICube Laboratory
Télécom Physique Strasbourg
300 bd Sébastien Brant - CS 10413
F - 67412 Illkirch cedex


PhD Thesis

Title: Capitalisation of experience in inventive design studies

Promotor: Cecilia Zanni-Merk (Senior Tenured Associate Professor, ICube-SDC)

Co-advisor: CAVALLUCCI (Professor, LGeco)

Funding: Grant from the China Scholarship Council

Overview: This PhD will be dedicated to build, test and implement new algorithms for investigating large quantities of data coming from the web and extracting out of it useful information in order to support engineers when inventing new objects at the early stages of innovation pipeline.

The emergence of norms (ISO) related to innovation is now likely to appear worldwide. How will the R&D department (re)organize itself to systematically produce inventions upstream of the innovation chain? How to create new tools that can support teams in charge of breakthrough projects? The LGéCo (Design Engineering Lab.) is interested in the theories of invention (such as TRIZ) and how they could, in the age of Big Data and FabLabs, serve as a link between knowledge that humanity continuously produce and the way its use could assist idea generation followed by quick prototyping. This new practical and pragmatic way of inventing, theoretically grounded but governed by performance rules and efficiency expectations, is the core target of our researches in Inventive Design. Knowledge management in Inventive Design, as defined by our laboratories, is crucial to assist engineers when inventing new objects in the innovation pipeline. It has specific characteristics and requires the selection of certain p i e c e s o f knowledge which can induce evolutions; it produces the reformulation of the initial problem in order to build an abstract model of the concerned object, and includes three main steps: • The “formulation” phase, where the expert uses different tools to express the problem in the form of a contradiction network or another model. • The “abstract solution finding” phase, where access to different knowledge bases is made to get one or more solution models. Generally, in this step, TRIZ users are required to have wide experience on the TRIZ knowledge sources. They need to be capable of choosing the accurate abstract solution according to the current abstract problem. • The “interpretation” phase, where these solution models are instantiated with the help of the scientific-engineering effects knowledge base, to get one or more solutions to be implemented in the real world. Different knowledge sources exist in order to solve different types of inventive problems, such as the 40 inventive principles for eliminating the technical contradictions and the 11 separation principles for eliminating the physical contradictions. These knowledge sources are all built independently of the application field, and their levels of abstraction are very different, making their use quite complicated.

Our previous works have developed a framework for a new architecture (knowledge and rules) for managing data (currently semi-automatically) and partially filter the appropriate one compliant with specific engineering studies. The outcomes of this project will permit the finalization of this general architecture, by the incorporation of experience and of meta-knowledge to guide the use of the domain knowledge, the rules and the experience for completely managing data, populating the inventive design ontology and test the impact of this new knowledge on inventive studies.