A substance-field ontology to support the TRIZ thinking approach

An ideal TRIZ reasoning environment should support TRIZ fundamental concepts and simulate its thinking process. In this paper, an advanced TRIZ methodology is analysed in this perspective: the substance-field analysis. Previously, it has been shown that the TRIZ knowledge can be modelled and managed in an object-oriented ontology for computer aided problem formulation. A new ontological model based on this previous work is proposed in order to simulate the TRIZ problem solving stage: from the generation of a general solution to the interpretation phase linking the abstract field of general solution to the real field of physic.

[1]  Wolfgang Beitz,et al.  Engineering Design: A Systematic Approach , 1984 .

[2]  Semyon Savransky,et al.  Engineering of Creativity: Introduction to TRIZ Methodology of Inventive Problem Solving , 2000 .

[3]  Daniel Kayser.  La représentation des connaissances , 1997 .

[4]  Bohuslav Busov,et al.  Computer support of innovation´s Creativity , 2001 .

[5]  Ernest Friedman-Hill,et al.  Jess in action : rule-based systems in Java , 2003 .

[6]  Darrell Mann,et al.  Hands-on systematic innovation , 2002 .

[7]  Denis Cavallucci,et al.  CONVERGING IN PROBLEM FORMULATION : A DIFFERENT PATH IN DESIGN , 2002 .

[8]  Hideaki Takeda,et al.  Physical concept ontology for the knowledge intensive engineering framework , 2004, Adv. Eng. Informatics.

[9]  Martin Rogers,et al.  Electre and decision support , 1999 .

[10]  Nicola Guarino,et al.  Formal ontology, conceptual analysis and knowledge representation , 1995, Int. J. Hum. Comput. Stud..

[11]  Henrik Eriksson,et al.  Knowledge modeling at the millennium : The design and evolution of Protégé-2000 , 1999 .

[12]  Altshuller.  Creativity As an Exact Science , 1984 .

[13]  Martin G. Moehrle,et al.  How Combinations of TRIZ Tools are Used in Companies - Results of a Cluster Analysis , 2005 .

[14]  Ali Shanian,et al.  A material selection model based on the concept of multiple attribute decision making , 2006 .

[15]  Alon Y. Halevy,et al.  CARIN: A Representation Language Combining Horn Rules and Description Logics , 1996, ECAI.

[16]  Bernhard Nebel,et al.  Reasoning and Revision in Hybrid Representation Systems , 1990, Lecture Notes in Computer Science.

[17]  Michael A. Orloff,et al.  Inventive Thinking through TRIZ: A Practical Guide , 2006 .

[18]  Jean-Louis Laurière,et al.  SNARK: A Language to Represent Declarative Knowledge and an Inference Engine which Uses Heuristics , 1986, IFIP Congress.

[19]  Asunción Gómez-Pérez,et al.  METHONTOLOGY: From Ontological Art Towards Ontological Engineering , 1997, AAAI 1997.