Topological and Shape Optimization of Flexure Hinges for Designing Compliant Mechanisms Using the Level Set Method View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-12

AUTHORS

Benliang Zhu, Xianmin Zhang, Min Liu, Qi Chen, Hai Li

ABSTRACT

A flexure hinge is a major component in designing compliant mechanisms that offers unique possibilities in a wide range of application fields in which high positioning accuracy is required. Although various flexure hinges with different configurations have been successively proposed, they are often designed based on designers’ experiences and inspirations. This study presents a systematic method for topological optimization of flexure hinges by using the level set method. Optimization formulations are developed by considering the functional requirements and geometrical constraints of flexure hinges. The functional requirements are first constructed by maximizing the compliance in the desired direction while minimizing the compliances in the other directions. The weighting sum method is used to construct an objective function in which a self-adjust method is used to set the weighting factors. A constraint on the symmetry of the obtained configuration is developed. Several numerical examples are presented to demonstrate the validity of the proposed method. The obtained results reveal that the design of a flexure hinge starting from the topology level can yield more choices for compliant mechanism design and obtain better designs that achieve higher performance. More... »

PAGES

13

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s10033-019-0332-z

DOI

http://dx.doi.org/10.1186/s10033-019-0332-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112391059


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": "South China University of Technology", 
          "id": "https://www.grid.ac/institutes/grid.79703.3a", 
          "name": [
            "Guangdong Provincial Key Laboratory of Precision Equipment and Manufacturing Technology, South China University of Technology, 510640, Guangzhou, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhu", 
        "givenName": "Benliang", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "South China University of Technology", 
          "id": "https://www.grid.ac/institutes/grid.79703.3a", 
          "name": [
            "Guangdong Provincial Key Laboratory of Precision Equipment and Manufacturing Technology, South China University of Technology, 510640, Guangzhou, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhang", 
        "givenName": "Xianmin", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "East China Jiaotong University", 
          "id": "https://www.grid.ac/institutes/grid.440711.7", 
          "name": [
            "East China Jiaotong University, 330013, Nanchang, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Liu", 
        "givenName": "Min", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "South China University of Technology", 
          "id": "https://www.grid.ac/institutes/grid.79703.3a", 
          "name": [
            "Guangdong Provincial Key Laboratory of Precision Equipment and Manufacturing Technology, South China University of Technology, 510640, Guangzhou, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chen", 
        "givenName": "Qi", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "South China University of Technology", 
          "id": "https://www.grid.ac/institutes/grid.79703.3a", 
          "name": [
            "Guangdong Provincial Key Laboratory of Precision Equipment and Manufacturing Technology, South China University of Technology, 510640, Guangzhou, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Li", 
        "givenName": "Hai", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1006/jcph.2000.6636", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003696615"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00158-013-0912-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003924047", 
          "https://doi.org/10.1007/s00158-013-0912-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00158-013-0912-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003924047", 
          "https://doi.org/10.1007/s00158-013-0912-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/0305215x.2013.786065", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006230547"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s004190050248", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007655079", 
          "https://doi.org/10.1007/s004190050248"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0141-6359(01)00108-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007658565"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0045-7825(88)90086-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008680660"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0045-7825(88)90086-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008680660"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cma.2014.08.017", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009072229"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s001580050170", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012624452", 
          "https://doi.org/10.1007/s001580050170"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s001580050170", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012624452", 
          "https://doi.org/10.1007/s001580050170"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1016251678", 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-662-05086-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016251678", 
          "https://doi.org/10.1007/978-3-662-05086-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-662-05086-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016251678", 
          "https://doi.org/10.1007/978-3-662-05086-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.precisioneng.2009.06.008", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018512462"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.precisioneng.2009.03.004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019999341"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00419-015-1106-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021499691", 
          "https://doi.org/10.1007/s00419-015-1106-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11431-011-4324-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022393629", 
          "https://doi.org/10.1007/s11431-011-4324-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00158-012-0842-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022818137", 
          "https://doi.org/10.1007/s00158-012-0842-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00158-012-0842-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022818137", 
          "https://doi.org/10.1007/s00158-012-0842-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.4948924", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023591480"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cma.2006.09.021", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026624959"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.precisioneng.2015.04.007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030494223"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.precisioneng.2007.05.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031627792"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3390/mi7020023", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042227433"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jcp.2003.09.032", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045900935"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00158-016-1470-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047921212", 
          "https://doi.org/10.1007/s00158-016-1470-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00158-016-1470-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047921212", 
          "https://doi.org/10.1007/s00158-016-1470-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.precisioneng.2016.12.012", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048041565"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11431-013-5446-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049347607", 
          "https://doi.org/10.1007/s11431-013-5446-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/1757-899x/10/1/012196", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050941057"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.1147635", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057675921"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.1494855", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057712522"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.3137074", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057915456"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.4818522", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058081032"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/1612-2011/13/11/115205", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059146262"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tmech.2014.2361271", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061693403"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1115/1.2936902", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062099175"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1115/1.3086796", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062101053"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1115/1.3086796", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062101053"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1115/1.4004441", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062144779"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1115/1.4007917", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062148241"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1115/1.4026097", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062151413"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1115/1.4028791", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062154102"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1115/1.4030990", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062156268"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1115/1.4030994", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062156272"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/0954406216671346", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063885508"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/0954406216671346", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063885508"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.optlaseng.2016.12.018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1074247994"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.mechmachtheory.2017.02.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083873276"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1201/9781420040272", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095904701"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1115/1.4039975", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1105734212"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1106879637", 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-12", 
    "datePublishedReg": "2019-12-01", 
    "description": "A flexure hinge is a major component in designing compliant mechanisms that offers unique possibilities in a wide range of application fields in which high positioning accuracy is required. Although various flexure hinges with different configurations have been successively proposed, they are often designed based on designers\u2019 experiences and inspirations. This study presents a systematic method for topological optimization of flexure hinges by using the level set method. Optimization formulations are developed by considering the functional requirements and geometrical constraints of flexure hinges. The functional requirements are first constructed by maximizing the compliance in the desired direction while minimizing the compliances in the other directions. The weighting sum method is used to construct an objective function in which a self-adjust method is used to set the weighting factors. A constraint on the symmetry of the obtained configuration is developed. Several numerical examples are presented to demonstrate the validity of the proposed method. The obtained results reveal that the design of a flexure hinge starting from the topology level can yield more choices for compliant mechanism design and obtain better designs that achieve higher performance.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1186/s10033-019-0332-z", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1297527", 
        "issn": [
          "0577-6686", 
          "2192-8258"
        ], 
        "name": "Chinese Journal of Mechanical Engineering", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "32"
      }
    ], 
    "name": "Topological and Shape Optimization of Flexure Hinges for Designing Compliant Mechanisms Using the Level Set Method", 
    "pagination": "13", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "4e9d5ad86d268d1847ce45c5c03fe5bf5b8e623d4a55ba8c6338c30c36230b59"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s10033-019-0332-z"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1112391059"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s10033-019-0332-z", 
      "https://app.dimensions.ai/details/publication/pub.1112391059"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T10:34", 
    "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/v90/0000000349_0000000349/records_113661_00000005.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1186%2Fs10033-019-0332-z"
  }
]
 

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.10.1186/s10033-019-0332-z'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s10033-019-0332-z'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s10033-019-0332-z'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s10033-019-0332-z'


 

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

229 TRIPLES      21 PREDICATES      72 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s10033-019-0332-z schema:about anzsrc-for:01
2 anzsrc-for:0103
3 schema:author Na83f6ac9a06840dcaea20779b2d6dae0
4 schema:citation sg:pub.10.1007/978-3-662-05086-6
5 sg:pub.10.1007/s00158-012-0842-0
6 sg:pub.10.1007/s00158-013-0912-y
7 sg:pub.10.1007/s00158-016-1470-x
8 sg:pub.10.1007/s001580050170
9 sg:pub.10.1007/s00419-015-1106-4
10 sg:pub.10.1007/s004190050248
11 sg:pub.10.1007/s11431-011-4324-1
12 sg:pub.10.1007/s11431-013-5446-4
13 https://app.dimensions.ai/details/publication/pub.1016251678
14 https://app.dimensions.ai/details/publication/pub.1106879637
15 https://doi.org/10.1006/jcph.2000.6636
16 https://doi.org/10.1016/0045-7825(88)90086-2
17 https://doi.org/10.1016/j.cma.2006.09.021
18 https://doi.org/10.1016/j.cma.2014.08.017
19 https://doi.org/10.1016/j.jcp.2003.09.032
20 https://doi.org/10.1016/j.mechmachtheory.2017.02.005
21 https://doi.org/10.1016/j.optlaseng.2016.12.018
22 https://doi.org/10.1016/j.precisioneng.2007.05.002
23 https://doi.org/10.1016/j.precisioneng.2009.03.004
24 https://doi.org/10.1016/j.precisioneng.2009.06.008
25 https://doi.org/10.1016/j.precisioneng.2015.04.007
26 https://doi.org/10.1016/j.precisioneng.2016.12.012
27 https://doi.org/10.1016/s0141-6359(01)00108-8
28 https://doi.org/10.1063/1.1147635
29 https://doi.org/10.1063/1.1494855
30 https://doi.org/10.1063/1.3137074
31 https://doi.org/10.1063/1.4818522
32 https://doi.org/10.1063/1.4948924
33 https://doi.org/10.1080/0305215x.2013.786065
34 https://doi.org/10.1088/1612-2011/13/11/115205
35 https://doi.org/10.1088/1757-899x/10/1/012196
36 https://doi.org/10.1109/tmech.2014.2361271
37 https://doi.org/10.1115/1.2936902
38 https://doi.org/10.1115/1.3086796
39 https://doi.org/10.1115/1.4004441
40 https://doi.org/10.1115/1.4007917
41 https://doi.org/10.1115/1.4026097
42 https://doi.org/10.1115/1.4028791
43 https://doi.org/10.1115/1.4030990
44 https://doi.org/10.1115/1.4030994
45 https://doi.org/10.1115/1.4039975
46 https://doi.org/10.1177/0954406216671346
47 https://doi.org/10.1201/9781420040272
48 https://doi.org/10.3390/mi7020023
49 schema:datePublished 2019-12
50 schema:datePublishedReg 2019-12-01
51 schema:description A flexure hinge is a major component in designing compliant mechanisms that offers unique possibilities in a wide range of application fields in which high positioning accuracy is required. Although various flexure hinges with different configurations have been successively proposed, they are often designed based on designers’ experiences and inspirations. This study presents a systematic method for topological optimization of flexure hinges by using the level set method. Optimization formulations are developed by considering the functional requirements and geometrical constraints of flexure hinges. The functional requirements are first constructed by maximizing the compliance in the desired direction while minimizing the compliances in the other directions. The weighting sum method is used to construct an objective function in which a self-adjust method is used to set the weighting factors. A constraint on the symmetry of the obtained configuration is developed. Several numerical examples are presented to demonstrate the validity of the proposed method. The obtained results reveal that the design of a flexure hinge starting from the topology level can yield more choices for compliant mechanism design and obtain better designs that achieve higher performance.
52 schema:genre research_article
53 schema:inLanguage en
54 schema:isAccessibleForFree false
55 schema:isPartOf N92b6359a74a4407ebf451a1338db281a
56 Ne65dcb47709649f89577bda1afff3dc7
57 sg:journal.1297527
58 schema:name Topological and Shape Optimization of Flexure Hinges for Designing Compliant Mechanisms Using the Level Set Method
59 schema:pagination 13
60 schema:productId N6c633b17489d4fd989729b206cba406b
61 Na140b9f7caef4945b248483225a4ac07
62 Naf059888dbcc4278921c5530f39665e5
63 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112391059
64 https://doi.org/10.1186/s10033-019-0332-z
65 schema:sdDatePublished 2019-04-11T10:34
66 schema:sdLicense https://scigraph.springernature.com/explorer/license/
67 schema:sdPublisher N20a4a1c1e00842528a86aaac3df8b6b2
68 schema:url https://link.springer.com/10.1186%2Fs10033-019-0332-z
69 sgo:license sg:explorer/license/
70 sgo:sdDataset articles
71 rdf:type schema:ScholarlyArticle
72 N12f5ff1450014f2d8363fce73b6f25e2 rdf:first N796a881e198946268f9ebba904402f67
73 rdf:rest N34cc41b9a3704d32b57a2ab174158508
74 N20a4a1c1e00842528a86aaac3df8b6b2 schema:name Springer Nature - SN SciGraph project
75 rdf:type schema:Organization
76 N2110c48440bb410b8f95a16caf038c57 schema:affiliation https://www.grid.ac/institutes/grid.440711.7
77 schema:familyName Liu
78 schema:givenName Min
79 rdf:type schema:Person
80 N34cc41b9a3704d32b57a2ab174158508 rdf:first N2110c48440bb410b8f95a16caf038c57
81 rdf:rest N741862e036e44ef7ad5551d5cc436092
82 N63ada3b82881447ca1fe5952f444a290 rdf:first Ndb72155f97494cafa1b612d83ab60bfb
83 rdf:rest rdf:nil
84 N6c633b17489d4fd989729b206cba406b schema:name readcube_id
85 schema:value 4e9d5ad86d268d1847ce45c5c03fe5bf5b8e623d4a55ba8c6338c30c36230b59
86 rdf:type schema:PropertyValue
87 N741862e036e44ef7ad5551d5cc436092 rdf:first N945b027ce197427b9d5ff4cfbcea0468
88 rdf:rest N63ada3b82881447ca1fe5952f444a290
89 N796a881e198946268f9ebba904402f67 schema:affiliation https://www.grid.ac/institutes/grid.79703.3a
90 schema:familyName Zhang
91 schema:givenName Xianmin
92 rdf:type schema:Person
93 N92b6359a74a4407ebf451a1338db281a schema:volumeNumber 32
94 rdf:type schema:PublicationVolume
95 N945b027ce197427b9d5ff4cfbcea0468 schema:affiliation https://www.grid.ac/institutes/grid.79703.3a
96 schema:familyName Chen
97 schema:givenName Qi
98 rdf:type schema:Person
99 Na140b9f7caef4945b248483225a4ac07 schema:name dimensions_id
100 schema:value pub.1112391059
101 rdf:type schema:PropertyValue
102 Na57f69e725994c17985445ffc68f255b schema:affiliation https://www.grid.ac/institutes/grid.79703.3a
103 schema:familyName Zhu
104 schema:givenName Benliang
105 rdf:type schema:Person
106 Na83f6ac9a06840dcaea20779b2d6dae0 rdf:first Na57f69e725994c17985445ffc68f255b
107 rdf:rest N12f5ff1450014f2d8363fce73b6f25e2
108 Naf059888dbcc4278921c5530f39665e5 schema:name doi
109 schema:value 10.1186/s10033-019-0332-z
110 rdf:type schema:PropertyValue
111 Ndb72155f97494cafa1b612d83ab60bfb schema:affiliation https://www.grid.ac/institutes/grid.79703.3a
112 schema:familyName Li
113 schema:givenName Hai
114 rdf:type schema:Person
115 Ne65dcb47709649f89577bda1afff3dc7 schema:issueNumber 1
116 rdf:type schema:PublicationIssue
117 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
118 schema:name Mathematical Sciences
119 rdf:type schema:DefinedTerm
120 anzsrc-for:0103 schema:inDefinedTermSet anzsrc-for:
121 schema:name Numerical and Computational Mathematics
122 rdf:type schema:DefinedTerm
123 sg:journal.1297527 schema:issn 0577-6686
124 2192-8258
125 schema:name Chinese Journal of Mechanical Engineering
126 rdf:type schema:Periodical
127 sg:pub.10.1007/978-3-662-05086-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016251678
128 https://doi.org/10.1007/978-3-662-05086-6
129 rdf:type schema:CreativeWork
130 sg:pub.10.1007/s00158-012-0842-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022818137
131 https://doi.org/10.1007/s00158-012-0842-0
132 rdf:type schema:CreativeWork
133 sg:pub.10.1007/s00158-013-0912-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1003924047
134 https://doi.org/10.1007/s00158-013-0912-y
135 rdf:type schema:CreativeWork
136 sg:pub.10.1007/s00158-016-1470-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1047921212
137 https://doi.org/10.1007/s00158-016-1470-x
138 rdf:type schema:CreativeWork
139 sg:pub.10.1007/s001580050170 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012624452
140 https://doi.org/10.1007/s001580050170
141 rdf:type schema:CreativeWork
142 sg:pub.10.1007/s00419-015-1106-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021499691
143 https://doi.org/10.1007/s00419-015-1106-4
144 rdf:type schema:CreativeWork
145 sg:pub.10.1007/s004190050248 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007655079
146 https://doi.org/10.1007/s004190050248
147 rdf:type schema:CreativeWork
148 sg:pub.10.1007/s11431-011-4324-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022393629
149 https://doi.org/10.1007/s11431-011-4324-1
150 rdf:type schema:CreativeWork
151 sg:pub.10.1007/s11431-013-5446-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049347607
152 https://doi.org/10.1007/s11431-013-5446-4
153 rdf:type schema:CreativeWork
154 https://app.dimensions.ai/details/publication/pub.1016251678 schema:CreativeWork
155 https://app.dimensions.ai/details/publication/pub.1106879637 schema:CreativeWork
156 https://doi.org/10.1006/jcph.2000.6636 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003696615
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1016/0045-7825(88)90086-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008680660
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1016/j.cma.2006.09.021 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026624959
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1016/j.cma.2014.08.017 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009072229
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1016/j.jcp.2003.09.032 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045900935
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1016/j.mechmachtheory.2017.02.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083873276
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1016/j.optlaseng.2016.12.018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1074247994
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1016/j.precisioneng.2007.05.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031627792
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1016/j.precisioneng.2009.03.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019999341
173 rdf:type schema:CreativeWork
174 https://doi.org/10.1016/j.precisioneng.2009.06.008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018512462
175 rdf:type schema:CreativeWork
176 https://doi.org/10.1016/j.precisioneng.2015.04.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030494223
177 rdf:type schema:CreativeWork
178 https://doi.org/10.1016/j.precisioneng.2016.12.012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048041565
179 rdf:type schema:CreativeWork
180 https://doi.org/10.1016/s0141-6359(01)00108-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007658565
181 rdf:type schema:CreativeWork
182 https://doi.org/10.1063/1.1147635 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057675921
183 rdf:type schema:CreativeWork
184 https://doi.org/10.1063/1.1494855 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057712522
185 rdf:type schema:CreativeWork
186 https://doi.org/10.1063/1.3137074 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057915456
187 rdf:type schema:CreativeWork
188 https://doi.org/10.1063/1.4818522 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058081032
189 rdf:type schema:CreativeWork
190 https://doi.org/10.1063/1.4948924 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023591480
191 rdf:type schema:CreativeWork
192 https://doi.org/10.1080/0305215x.2013.786065 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006230547
193 rdf:type schema:CreativeWork
194 https://doi.org/10.1088/1612-2011/13/11/115205 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059146262
195 rdf:type schema:CreativeWork
196 https://doi.org/10.1088/1757-899x/10/1/012196 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050941057
197 rdf:type schema:CreativeWork
198 https://doi.org/10.1109/tmech.2014.2361271 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061693403
199 rdf:type schema:CreativeWork
200 https://doi.org/10.1115/1.2936902 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062099175
201 rdf:type schema:CreativeWork
202 https://doi.org/10.1115/1.3086796 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062101053
203 rdf:type schema:CreativeWork
204 https://doi.org/10.1115/1.4004441 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062144779
205 rdf:type schema:CreativeWork
206 https://doi.org/10.1115/1.4007917 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062148241
207 rdf:type schema:CreativeWork
208 https://doi.org/10.1115/1.4026097 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062151413
209 rdf:type schema:CreativeWork
210 https://doi.org/10.1115/1.4028791 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062154102
211 rdf:type schema:CreativeWork
212 https://doi.org/10.1115/1.4030990 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062156268
213 rdf:type schema:CreativeWork
214 https://doi.org/10.1115/1.4030994 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062156272
215 rdf:type schema:CreativeWork
216 https://doi.org/10.1115/1.4039975 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105734212
217 rdf:type schema:CreativeWork
218 https://doi.org/10.1177/0954406216671346 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063885508
219 rdf:type schema:CreativeWork
220 https://doi.org/10.1201/9781420040272 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095904701
221 rdf:type schema:CreativeWork
222 https://doi.org/10.3390/mi7020023 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042227433
223 rdf:type schema:CreativeWork
224 https://www.grid.ac/institutes/grid.440711.7 schema:alternateName East China Jiaotong University
225 schema:name East China Jiaotong University, 330013, Nanchang, China
226 rdf:type schema:Organization
227 https://www.grid.ac/institutes/grid.79703.3a schema:alternateName South China University of Technology
228 schema:name Guangdong Provincial Key Laboratory of Precision Equipment and Manufacturing Technology, South China University of Technology, 510640, Guangzhou, China
229 rdf:type schema:Organization
 




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


...