Learning the Optimal Product Design Through History View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2015

AUTHORS

Victor Parque , Tomoyuki Miyashita

ABSTRACT

The search for novel and high-performing product designs is a ubiquitous problem in science and engineering: aided by advances in optimization methods the conventional approaches usually optimize a (multi) objective function using simulations followed by experiments. However, in some scenarios such as vehicle layout design, simulations and experiments are restrictive, inaccurate and expensive. In this paper, we propose an alternative approach to search for novel and high-performing product designs by optimizing not only a proposed novelty metric, but also a performance function learned from historical data. Computational experiments using more than twenty thousand vehicle models over the last thirty years shows the usefulness and promising results for a wider set of design engineering problems. More... »

PAGES

382-389

References to SciGraph publications

  • 2013. Reinforced Explorit on Optimizing Vehicle Powertrains in NEURAL INFORMATION PROCESSING
  • 2010-07. Generalised design for optimal product configuration in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • 2003-08. A methodology for evolutionary product design in ENGINEERING WITH COMPUTERS
  • Book

    TITLE

    Neural Information Processing

    ISBN

    978-3-319-26531-5
    978-3-319-26532-2

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-26532-2_42

    DOI

    http://dx.doi.org/10.1007/978-3-319-26532-2_42

    DIMENSIONS

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