Comparison of surface extraction techniques performance in computed tomography for 3D complex micro-geometry dimensional measurements View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-07

AUTHORS

Marta Torralba, Roberto Jiménez, José A. Yagüe-Fabra, Sinué Ontiveros, Guido Tosello

ABSTRACT

The number of industrial applications of computed tomography (CT) for dimensional metrology in 100–103 mm range has been continuously increasing, especially in the last years. Due to its specific characteristics, CT has the potential to be employed as a viable solution for measuring 3D complex micro-geometries as well (i.e., in the sub-mm dimensional range). However, there are different factors that may influence the CT process performance, being one of them the surface extraction technique used. In this paper, two different extraction techniques are applied to measure a complex miniaturized dental file by CT in order to analyze its contribution to the final measurement uncertainty in complex geometries at the mm to sub-mm scales. The first method is based on a similarity analysis: the threshold determination; while the second one is based on a gradient or discontinuity analysis: the 3D Canny algorithm. This algorithm has proven to provide accurate results in parts with simple geometries, but its suitability for 3D complex geometries has not been proven so far. To verify the measurement results and compare both techniques, reference measurements are performed on an optical coordinate measuring machine (OCMM). The systematic errors and uncertainty results obtained show that the 3D Canny adapted method slightly lower systematic deviations and a more robust edge definition than the local threshold method for 3D complex micro-geometry dimensional measurements. More... »

PAGES

441-453

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00170-018-1950-9

DOI

http://dx.doi.org/10.1007/s00170-018-1950-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1101899637


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/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Centro Universitario de la Defensa", 
          "id": "https://www.grid.ac/institutes/grid.467120.6", 
          "name": [
            "Centro Universitario de la Defensa, A.G.M, Carretera Huesca s/n, 50090, Zaragoza, Spain"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Torralba", 
        "givenName": "Marta", 
        "id": "sg:person.01171650345.53", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01171650345.53"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Centro Universitario de la Defensa", 
          "id": "https://www.grid.ac/institutes/grid.467120.6", 
          "name": [
            "Centro Universitario de la Defensa, A.G.M, Carretera Huesca s/n, 50090, Zaragoza, Spain"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Jim\u00e9nez", 
        "givenName": "Roberto", 
        "id": "sg:person.0721665504.13", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0721665504.13"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Zaragoza", 
          "id": "https://www.grid.ac/institutes/grid.11205.37", 
          "name": [
            "I3A, Universidad de Zaragoza, Mar\u00eda de Luna 3, 50018, Zaragoza, Spain"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yag\u00fce-Fabra", 
        "givenName": "Jos\u00e9 A.", 
        "id": "sg:person.011421776153.43", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011421776153.43"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Autonomous University of Baja California", 
          "id": "https://www.grid.ac/institutes/grid.412852.8", 
          "name": [
            "Department of Industrial Engineering, Autonomous University of Baja California, 21460, San Fernando, Tecate, Mexico"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ontiveros", 
        "givenName": "Sinu\u00e9", 
        "id": "sg:person.010037265537.22", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010037265537.22"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Technical University of Denmark", 
          "id": "https://www.grid.ac/institutes/grid.5170.3", 
          "name": [
            "Department of Mechanical Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tosello", 
        "givenName": "Guido", 
        "id": "sg:person.015615640355.98", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015615640355.98"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/j.cirp.2016.04.069", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004004983"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.procir.2016.02.018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004328052"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cad.2015.07.015", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005665009"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.measurement.2011.10.019", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007763977"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1601-1546.2005.00115.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009854324"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1601-1546.2005.00115.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009854324"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cirpj.2013.02.007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010924315"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.precisioneng.2016.03.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012251807"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.measurement.2012.05.030", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023366161"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cirp.2014.05.011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024391724"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0957-0233/22/11/115502", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025775396"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.precisioneng.2015.12.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026568448"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.procir.2013.08.022", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029388217"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.precisioneng.2014.06.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030125966"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.optlaseng.2011.06.009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031184390"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0957-0233/21/4/045105", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032467771"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.precisioneng.2010.09.010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034297520"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0957-0233/23/12/125401", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034772761"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.csndt.2016.04.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037121532"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.procir.2016.02.123", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037178410"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.wear.2010.06.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038495866"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.proeng.2013.08.263", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039030144"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0957-0233/26/9/092003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048147109"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cirp.2013.03.016", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050950707"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cirp.2009.03.027", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052225087"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cirp.2011.05.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053448316"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1051/ijmqe/2012011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056971641"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tvcg.2007.70598", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061812927"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1074942931", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.precisioneng.2017.03.007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084534190"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.mfglet.2017.06.004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1086268166"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.precisioneng.2017.08.021", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091399393"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-07", 
    "datePublishedReg": "2018-07-01", 
    "description": "The number of industrial applications of computed tomography (CT) for dimensional metrology in 100\u2013103 mm range has been continuously increasing, especially in the last years. Due to its specific characteristics, CT has the potential to be employed as a viable solution for measuring 3D complex micro-geometries as well (i.e., in the sub-mm dimensional range). However, there are different factors that may influence the CT process performance, being one of them the surface extraction technique used. In this paper, two different extraction techniques are applied to measure a complex miniaturized dental file by CT in order to analyze its contribution to the final measurement uncertainty in complex geometries at the mm to sub-mm scales. The first method is based on a similarity analysis: the threshold determination; while the second one is based on a gradient or discontinuity analysis: the 3D Canny algorithm. This algorithm has proven to provide accurate results in parts with simple geometries, but its suitability for 3D complex geometries has not been proven so far. To verify the measurement results and compare both techniques, reference measurements are performed on an optical coordinate measuring machine (OCMM). The systematic errors and uncertainty results obtained show that the 3D Canny adapted method slightly lower systematic deviations and a more robust edge definition than the local threshold method for 3D complex micro-geometry dimensional measurements.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s00170-018-1950-9", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1043671", 
        "issn": [
          "0268-3768", 
          "1433-3015"
        ], 
        "name": "The International Journal of Advanced Manufacturing Technology", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1-4", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "97"
      }
    ], 
    "name": "Comparison of surface extraction techniques performance in computed tomography for 3D complex micro-geometry dimensional measurements", 
    "pagination": "441-453", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "d85468502627039c6580abffd2b0b796c75ffd6fd1666adbba162226be5302d1"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00170-018-1950-9"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1101899637"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00170-018-1950-9", 
      "https://app.dimensions.ai/details/publication/pub.1101899637"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T13:52", 
    "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/v90/0000000371_0000000371/records_130801_00000005.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs00170-018-1950-9"
  }
]
 

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/s00170-018-1950-9'

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/s00170-018-1950-9'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00170-018-1950-9'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00170-018-1950-9'


 

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

190 TRIPLES      21 PREDICATES      58 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00170-018-1950-9 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N20c3b3a6e06144fabc96e847daa1bed4
4 schema:citation https://app.dimensions.ai/details/publication/pub.1074942931
5 https://doi.org/10.1016/j.cad.2015.07.015
6 https://doi.org/10.1016/j.cirp.2009.03.027
7 https://doi.org/10.1016/j.cirp.2011.05.006
8 https://doi.org/10.1016/j.cirp.2013.03.016
9 https://doi.org/10.1016/j.cirp.2014.05.011
10 https://doi.org/10.1016/j.cirp.2016.04.069
11 https://doi.org/10.1016/j.cirpj.2013.02.007
12 https://doi.org/10.1016/j.csndt.2016.04.003
13 https://doi.org/10.1016/j.measurement.2011.10.019
14 https://doi.org/10.1016/j.measurement.2012.05.030
15 https://doi.org/10.1016/j.mfglet.2017.06.004
16 https://doi.org/10.1016/j.optlaseng.2011.06.009
17 https://doi.org/10.1016/j.precisioneng.2010.09.010
18 https://doi.org/10.1016/j.precisioneng.2014.06.006
19 https://doi.org/10.1016/j.precisioneng.2015.12.003
20 https://doi.org/10.1016/j.precisioneng.2016.03.001
21 https://doi.org/10.1016/j.precisioneng.2017.03.007
22 https://doi.org/10.1016/j.precisioneng.2017.08.021
23 https://doi.org/10.1016/j.procir.2013.08.022
24 https://doi.org/10.1016/j.procir.2016.02.018
25 https://doi.org/10.1016/j.procir.2016.02.123
26 https://doi.org/10.1016/j.proeng.2013.08.263
27 https://doi.org/10.1016/j.wear.2010.06.001
28 https://doi.org/10.1051/ijmqe/2012011
29 https://doi.org/10.1088/0957-0233/21/4/045105
30 https://doi.org/10.1088/0957-0233/22/11/115502
31 https://doi.org/10.1088/0957-0233/23/12/125401
32 https://doi.org/10.1088/0957-0233/26/9/092003
33 https://doi.org/10.1109/tvcg.2007.70598
34 https://doi.org/10.1111/j.1601-1546.2005.00115.x
35 schema:datePublished 2018-07
36 schema:datePublishedReg 2018-07-01
37 schema:description The number of industrial applications of computed tomography (CT) for dimensional metrology in 100–103 mm range has been continuously increasing, especially in the last years. Due to its specific characteristics, CT has the potential to be employed as a viable solution for measuring 3D complex micro-geometries as well (i.e., in the sub-mm dimensional range). However, there are different factors that may influence the CT process performance, being one of them the surface extraction technique used. In this paper, two different extraction techniques are applied to measure a complex miniaturized dental file by CT in order to analyze its contribution to the final measurement uncertainty in complex geometries at the mm to sub-mm scales. The first method is based on a similarity analysis: the threshold determination; while the second one is based on a gradient or discontinuity analysis: the 3D Canny algorithm. This algorithm has proven to provide accurate results in parts with simple geometries, but its suitability for 3D complex geometries has not been proven so far. To verify the measurement results and compare both techniques, reference measurements are performed on an optical coordinate measuring machine (OCMM). The systematic errors and uncertainty results obtained show that the 3D Canny adapted method slightly lower systematic deviations and a more robust edge definition than the local threshold method for 3D complex micro-geometry dimensional measurements.
38 schema:genre research_article
39 schema:inLanguage en
40 schema:isAccessibleForFree true
41 schema:isPartOf N97381dc1693f4a2587484085dba4c1dd
42 N9ad42998299d45e59e63f26d18926177
43 sg:journal.1043671
44 schema:name Comparison of surface extraction techniques performance in computed tomography for 3D complex micro-geometry dimensional measurements
45 schema:pagination 441-453
46 schema:productId N24ff28b733aa4a138684db21793a47aa
47 N8c2d64e265ef4d538e591c6d05e7e80e
48 Na8bde45cf6f146ca9e9b0d19b90e85f6
49 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101899637
50 https://doi.org/10.1007/s00170-018-1950-9
51 schema:sdDatePublished 2019-04-11T13:52
52 schema:sdLicense https://scigraph.springernature.com/explorer/license/
53 schema:sdPublisher Ndc82ed0bc8a94b5cb86c4de470ad6d39
54 schema:url https://link.springer.com/10.1007%2Fs00170-018-1950-9
55 sgo:license sg:explorer/license/
56 sgo:sdDataset articles
57 rdf:type schema:ScholarlyArticle
58 N20c3b3a6e06144fabc96e847daa1bed4 rdf:first sg:person.01171650345.53
59 rdf:rest Na804ca86c323453bbf16e494894ed984
60 N24ff28b733aa4a138684db21793a47aa schema:name dimensions_id
61 schema:value pub.1101899637
62 rdf:type schema:PropertyValue
63 N3e884a71b8d642ae8f3d20a1540a99f1 rdf:first sg:person.015615640355.98
64 rdf:rest rdf:nil
65 N527f72699c144618930ad2f8fd94bc4b rdf:first sg:person.011421776153.43
66 rdf:rest N8ed0cd55b5c640888e51dbd645d0c0ef
67 N8c2d64e265ef4d538e591c6d05e7e80e schema:name readcube_id
68 schema:value d85468502627039c6580abffd2b0b796c75ffd6fd1666adbba162226be5302d1
69 rdf:type schema:PropertyValue
70 N8ed0cd55b5c640888e51dbd645d0c0ef rdf:first sg:person.010037265537.22
71 rdf:rest N3e884a71b8d642ae8f3d20a1540a99f1
72 N97381dc1693f4a2587484085dba4c1dd schema:volumeNumber 97
73 rdf:type schema:PublicationVolume
74 N9ad42998299d45e59e63f26d18926177 schema:issueNumber 1-4
75 rdf:type schema:PublicationIssue
76 Na804ca86c323453bbf16e494894ed984 rdf:first sg:person.0721665504.13
77 rdf:rest N527f72699c144618930ad2f8fd94bc4b
78 Na8bde45cf6f146ca9e9b0d19b90e85f6 schema:name doi
79 schema:value 10.1007/s00170-018-1950-9
80 rdf:type schema:PropertyValue
81 Ndc82ed0bc8a94b5cb86c4de470ad6d39 schema:name Springer Nature - SN SciGraph project
82 rdf:type schema:Organization
83 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
84 schema:name Information and Computing Sciences
85 rdf:type schema:DefinedTerm
86 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
87 schema:name Artificial Intelligence and Image Processing
88 rdf:type schema:DefinedTerm
89 sg:journal.1043671 schema:issn 0268-3768
90 1433-3015
91 schema:name The International Journal of Advanced Manufacturing Technology
92 rdf:type schema:Periodical
93 sg:person.010037265537.22 schema:affiliation https://www.grid.ac/institutes/grid.412852.8
94 schema:familyName Ontiveros
95 schema:givenName Sinué
96 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010037265537.22
97 rdf:type schema:Person
98 sg:person.011421776153.43 schema:affiliation https://www.grid.ac/institutes/grid.11205.37
99 schema:familyName Yagüe-Fabra
100 schema:givenName José A.
101 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011421776153.43
102 rdf:type schema:Person
103 sg:person.01171650345.53 schema:affiliation https://www.grid.ac/institutes/grid.467120.6
104 schema:familyName Torralba
105 schema:givenName Marta
106 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01171650345.53
107 rdf:type schema:Person
108 sg:person.015615640355.98 schema:affiliation https://www.grid.ac/institutes/grid.5170.3
109 schema:familyName Tosello
110 schema:givenName Guido
111 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015615640355.98
112 rdf:type schema:Person
113 sg:person.0721665504.13 schema:affiliation https://www.grid.ac/institutes/grid.467120.6
114 schema:familyName Jiménez
115 schema:givenName Roberto
116 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0721665504.13
117 rdf:type schema:Person
118 https://app.dimensions.ai/details/publication/pub.1074942931 schema:CreativeWork
119 https://doi.org/10.1016/j.cad.2015.07.015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005665009
120 rdf:type schema:CreativeWork
121 https://doi.org/10.1016/j.cirp.2009.03.027 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052225087
122 rdf:type schema:CreativeWork
123 https://doi.org/10.1016/j.cirp.2011.05.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053448316
124 rdf:type schema:CreativeWork
125 https://doi.org/10.1016/j.cirp.2013.03.016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050950707
126 rdf:type schema:CreativeWork
127 https://doi.org/10.1016/j.cirp.2014.05.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024391724
128 rdf:type schema:CreativeWork
129 https://doi.org/10.1016/j.cirp.2016.04.069 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004004983
130 rdf:type schema:CreativeWork
131 https://doi.org/10.1016/j.cirpj.2013.02.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010924315
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1016/j.csndt.2016.04.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037121532
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1016/j.measurement.2011.10.019 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007763977
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1016/j.measurement.2012.05.030 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023366161
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1016/j.mfglet.2017.06.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1086268166
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1016/j.optlaseng.2011.06.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031184390
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1016/j.precisioneng.2010.09.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034297520
144 rdf:type schema:CreativeWork
145 https://doi.org/10.1016/j.precisioneng.2014.06.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030125966
146 rdf:type schema:CreativeWork
147 https://doi.org/10.1016/j.precisioneng.2015.12.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026568448
148 rdf:type schema:CreativeWork
149 https://doi.org/10.1016/j.precisioneng.2016.03.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012251807
150 rdf:type schema:CreativeWork
151 https://doi.org/10.1016/j.precisioneng.2017.03.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084534190
152 rdf:type schema:CreativeWork
153 https://doi.org/10.1016/j.precisioneng.2017.08.021 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091399393
154 rdf:type schema:CreativeWork
155 https://doi.org/10.1016/j.procir.2013.08.022 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029388217
156 rdf:type schema:CreativeWork
157 https://doi.org/10.1016/j.procir.2016.02.018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004328052
158 rdf:type schema:CreativeWork
159 https://doi.org/10.1016/j.procir.2016.02.123 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037178410
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1016/j.proeng.2013.08.263 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039030144
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1016/j.wear.2010.06.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038495866
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1051/ijmqe/2012011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056971641
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1088/0957-0233/21/4/045105 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032467771
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1088/0957-0233/22/11/115502 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025775396
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1088/0957-0233/23/12/125401 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034772761
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1088/0957-0233/26/9/092003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048147109
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1109/tvcg.2007.70598 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061812927
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1111/j.1601-1546.2005.00115.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1009854324
178 rdf:type schema:CreativeWork
179 https://www.grid.ac/institutes/grid.11205.37 schema:alternateName University of Zaragoza
180 schema:name I3A, Universidad de Zaragoza, María de Luna 3, 50018, Zaragoza, Spain
181 rdf:type schema:Organization
182 https://www.grid.ac/institutes/grid.412852.8 schema:alternateName Autonomous University of Baja California
183 schema:name Department of Industrial Engineering, Autonomous University of Baja California, 21460, San Fernando, Tecate, Mexico
184 rdf:type schema:Organization
185 https://www.grid.ac/institutes/grid.467120.6 schema:alternateName Centro Universitario de la Defensa
186 schema:name Centro Universitario de la Defensa, A.G.M, Carretera Huesca s/n, 50090, Zaragoza, Spain
187 rdf:type schema:Organization
188 https://www.grid.ac/institutes/grid.5170.3 schema:alternateName Technical University of Denmark
189 schema:name Department of Mechanical Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark
190 rdf:type schema:Organization
 




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


...