A Smart System for Haptic Quality Control: A Knowledge-Based Approach to Formalize the Sense of Touch View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018-11-14

AUTHORS

Bruno Albert , François De Bertrand De Beuvron , Cecilia Zanni-Merk , Jean-Luc Maire , Maurice Pillet , Julien Charrier , Christophe Knecht

ABSTRACT

The field of quality control has seen over the last decades a variety of studies and innovations turned towards the improvement of the perceptions rendered through manufactured products. Thus, quality checks do not only rely on technical control, but on a diversity of controls which correspond to the senses involved when interacting with a product. However, the quality specifications and in particular the vocabulary used for their description are still very specific to each product or industrial domain. With the perspective of simplifying and standardizing perceived quality control, this study aims at providing a Smart System based on knowledge modelling methods which is capable of guiding manufacturers in the process of structuring, generalizing and eventually automatizing the control process related to perceived quality and touch in particular. This paper presents a general framework for the Smart System as well as an ontological structure for the representation of perceived quality knowledge. The specificities of the sense of touch are detailed and led to the proposition of novel formalized description and conceptual model of haptic perceptions. More... »

PAGES

173-190

References to SciGraph publications

  • 2005-07. Pacinian representations of fine surface texture in ATTENTION, PERCEPTION, & PSYCHOPHYSICS
  • 2009-10. Haptic perception: A tutorial in ATTENTION, PERCEPTION, & PSYCHOPHYSICS
  • 2010. UML Style Graphical Notation and Editor for OWL 2 in PERSPECTIVES IN BUSINESS INFORMATICS RESEARCH
  • 2011-06-17. An Introduction to Ontologies and Ontology Engineering in ONTOLOGIES IN URBAN DEVELOPMENT PROJECTS
  • Book

    TITLE

    Knowledge Discovery, Knowledge Engineering and Knowledge Management

    ISBN

    978-3-319-99700-1
    978-3-319-99701-8

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-99701-8_8

    DOI

    http://dx.doi.org/10.1007/978-3-319-99701-8_8

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1109933856


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