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

    https://app.dimensions.ai/details/publication/pub.1009782981


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0103", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Numerical and Computational Mathematics", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Mathematical Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Waseda University", 
              "id": "https://www.grid.ac/institutes/grid.5290.e", 
              "name": [
                "Department of Modern Mechanical Engineering, Waseda University"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Parque", 
            "givenName": "Victor", 
            "id": "sg:person.014301210607.12", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014301210607.12"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Waseda University", 
              "id": "https://www.grid.ac/institutes/grid.5290.e", 
              "name": [
                "Department of Modern Mechanical Engineering, Waseda University"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Miyashita", 
            "givenName": "Tomoyuki", 
            "id": "sg:person.01021106734.61", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01021106734.61"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1145/235815.235821", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001929346"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00366-003-0261-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004904819", 
              "https://doi.org/10.1007/s00366-003-0261-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1080/09544828.2012.720015", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014713584"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1080/09544828.2012.668614", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026243874"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-42042-9_72", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026446104", 
              "https://doi.org/10.1007/978-3-642-42042-9_72"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/comjnl/16.1.30", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039782248"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ins.2012.02.011", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052864471"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-009-2397-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053708673", 
              "https://doi.org/10.1007/s00170-009-2397-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-009-2397-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053708673", 
              "https://doi.org/10.1007/s00170-009-2397-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-009-2397-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053708673", 
              "https://doi.org/10.1007/s00170-009-2397-9"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2015", 
        "datePublishedReg": "2015-01-01", 
        "description": "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.", 
        "editor": [
          {
            "familyName": "Arik", 
            "givenName": "Sabri", 
            "type": "Person"
          }, 
          {
            "familyName": "Huang", 
            "givenName": "Tingwen", 
            "type": "Person"
          }, 
          {
            "familyName": "Lai", 
            "givenName": "Weng Kin", 
            "type": "Person"
          }, 
          {
            "familyName": "Liu", 
            "givenName": "Qingshan", 
            "type": "Person"
          }
        ], 
        "genre": "chapter", 
        "id": "sg:pub.10.1007/978-3-319-26532-2_42", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": {
          "isbn": [
            "978-3-319-26531-5", 
            "978-3-319-26532-2"
          ], 
          "name": "Neural Information Processing", 
          "type": "Book"
        }, 
        "name": "Learning the Optimal Product Design Through History", 
        "pagination": "382-389", 
        "productId": [
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/978-3-319-26532-2_42"
            ]
          }, 
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "2e9b1421a7b22c95cfc9db17e3099f375d0d383335b86f0bd0c53d8b9e5c4b6c"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1009782981"
            ]
          }
        ], 
        "publisher": {
          "location": "Cham", 
          "name": "Springer International Publishing", 
          "type": "Organisation"
        }, 
        "sameAs": [
          "https://doi.org/10.1007/978-3-319-26532-2_42", 
          "https://app.dimensions.ai/details/publication/pub.1009782981"
        ], 
        "sdDataset": "chapters", 
        "sdDatePublished": "2019-04-15T21:56", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8693_00000249.jsonl", 
        "type": "Chapter", 
        "url": "http://link.springer.com/10.1007/978-3-319-26532-2_42"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-26532-2_42'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-26532-2_42'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-26532-2_42'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-26532-2_42'


     

    This table displays all metadata directly associated to this object as RDF triples.

    114 TRIPLES      23 PREDICATES      35 URIs      20 LITERALS      8 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/978-3-319-26532-2_42 schema:about anzsrc-for:01
    2 anzsrc-for:0103
    3 schema:author N657f0d8cb152433a8ff752d5494b7530
    4 schema:citation sg:pub.10.1007/978-3-642-42042-9_72
    5 sg:pub.10.1007/s00170-009-2397-9
    6 sg:pub.10.1007/s00366-003-0261-3
    7 https://doi.org/10.1016/j.ins.2012.02.011
    8 https://doi.org/10.1080/09544828.2012.668614
    9 https://doi.org/10.1080/09544828.2012.720015
    10 https://doi.org/10.1093/comjnl/16.1.30
    11 https://doi.org/10.1145/235815.235821
    12 schema:datePublished 2015
    13 schema:datePublishedReg 2015-01-01
    14 schema:description 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.
    15 schema:editor N3da9767ac9234021b80ab296d2d8a013
    16 schema:genre chapter
    17 schema:inLanguage en
    18 schema:isAccessibleForFree false
    19 schema:isPartOf Nf3be5709555f4060a4055ea0e4534723
    20 schema:name Learning the Optimal Product Design Through History
    21 schema:pagination 382-389
    22 schema:productId N275affc5caf04611836d7377508bdc57
    23 N7400b9505faf4beca00209ea581e4ed7
    24 Na4f9c540f4f0405297402d965dc0612b
    25 schema:publisher Nf566c489519e4d85bbd51cd4d8a8f1c9
    26 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009782981
    27 https://doi.org/10.1007/978-3-319-26532-2_42
    28 schema:sdDatePublished 2019-04-15T21:56
    29 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    30 schema:sdPublisher N25dc9de4ef5d4b48a8bfd69f77507284
    31 schema:url http://link.springer.com/10.1007/978-3-319-26532-2_42
    32 sgo:license sg:explorer/license/
    33 sgo:sdDataset chapters
    34 rdf:type schema:Chapter
    35 N12e8cfa03cd5472c9a87ff8fba485d94 schema:familyName Arik
    36 schema:givenName Sabri
    37 rdf:type schema:Person
    38 N25dc9de4ef5d4b48a8bfd69f77507284 schema:name Springer Nature - SN SciGraph project
    39 rdf:type schema:Organization
    40 N275affc5caf04611836d7377508bdc57 schema:name readcube_id
    41 schema:value 2e9b1421a7b22c95cfc9db17e3099f375d0d383335b86f0bd0c53d8b9e5c4b6c
    42 rdf:type schema:PropertyValue
    43 N3da9767ac9234021b80ab296d2d8a013 rdf:first N12e8cfa03cd5472c9a87ff8fba485d94
    44 rdf:rest N8d0b8911b18b4422bee634fd7502960e
    45 N580f28568a8d4c0abf1e7085f1ecacf6 rdf:first sg:person.01021106734.61
    46 rdf:rest rdf:nil
    47 N6209f4877215426284e2b0e0e00d9d65 schema:familyName Liu
    48 schema:givenName Qingshan
    49 rdf:type schema:Person
    50 N657f0d8cb152433a8ff752d5494b7530 rdf:first sg:person.014301210607.12
    51 rdf:rest N580f28568a8d4c0abf1e7085f1ecacf6
    52 N7400b9505faf4beca00209ea581e4ed7 schema:name dimensions_id
    53 schema:value pub.1009782981
    54 rdf:type schema:PropertyValue
    55 N756380c72d6b4eae9ce6507703df5670 schema:familyName Lai
    56 schema:givenName Weng Kin
    57 rdf:type schema:Person
    58 N8d0b8911b18b4422bee634fd7502960e rdf:first N9af1f03428fa49ef835d1e2595585713
    59 rdf:rest Nd18c26e38d4b4a959d3d3591d645b46b
    60 N9af1f03428fa49ef835d1e2595585713 schema:familyName Huang
    61 schema:givenName Tingwen
    62 rdf:type schema:Person
    63 N9cc99c31ac5247bcad9541bdf1a40f46 rdf:first N6209f4877215426284e2b0e0e00d9d65
    64 rdf:rest rdf:nil
    65 Na4f9c540f4f0405297402d965dc0612b schema:name doi
    66 schema:value 10.1007/978-3-319-26532-2_42
    67 rdf:type schema:PropertyValue
    68 Nd18c26e38d4b4a959d3d3591d645b46b rdf:first N756380c72d6b4eae9ce6507703df5670
    69 rdf:rest N9cc99c31ac5247bcad9541bdf1a40f46
    70 Nf3be5709555f4060a4055ea0e4534723 schema:isbn 978-3-319-26531-5
    71 978-3-319-26532-2
    72 schema:name Neural Information Processing
    73 rdf:type schema:Book
    74 Nf566c489519e4d85bbd51cd4d8a8f1c9 schema:location Cham
    75 schema:name Springer International Publishing
    76 rdf:type schema:Organisation
    77 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
    78 schema:name Mathematical Sciences
    79 rdf:type schema:DefinedTerm
    80 anzsrc-for:0103 schema:inDefinedTermSet anzsrc-for:
    81 schema:name Numerical and Computational Mathematics
    82 rdf:type schema:DefinedTerm
    83 sg:person.01021106734.61 schema:affiliation https://www.grid.ac/institutes/grid.5290.e
    84 schema:familyName Miyashita
    85 schema:givenName Tomoyuki
    86 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01021106734.61
    87 rdf:type schema:Person
    88 sg:person.014301210607.12 schema:affiliation https://www.grid.ac/institutes/grid.5290.e
    89 schema:familyName Parque
    90 schema:givenName Victor
    91 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014301210607.12
    92 rdf:type schema:Person
    93 sg:pub.10.1007/978-3-642-42042-9_72 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026446104
    94 https://doi.org/10.1007/978-3-642-42042-9_72
    95 rdf:type schema:CreativeWork
    96 sg:pub.10.1007/s00170-009-2397-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053708673
    97 https://doi.org/10.1007/s00170-009-2397-9
    98 rdf:type schema:CreativeWork
    99 sg:pub.10.1007/s00366-003-0261-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004904819
    100 https://doi.org/10.1007/s00366-003-0261-3
    101 rdf:type schema:CreativeWork
    102 https://doi.org/10.1016/j.ins.2012.02.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052864471
    103 rdf:type schema:CreativeWork
    104 https://doi.org/10.1080/09544828.2012.668614 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026243874
    105 rdf:type schema:CreativeWork
    106 https://doi.org/10.1080/09544828.2012.720015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014713584
    107 rdf:type schema:CreativeWork
    108 https://doi.org/10.1093/comjnl/16.1.30 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039782248
    109 rdf:type schema:CreativeWork
    110 https://doi.org/10.1145/235815.235821 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001929346
    111 rdf:type schema:CreativeWork
    112 https://www.grid.ac/institutes/grid.5290.e schema:alternateName Waseda University
    113 schema:name Department of Modern Mechanical Engineering, Waseda University
    114 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...