Detection of local matrix cracks in composite beam using modal data and modular radial basis neural networks View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-11

AUTHORS

G. Sarangapani, Ranjan Ganguli

ABSTRACT

Dynamic characteristics such as natural frequencies and mode shapes are used to identify the location of damage and the damage level in a laminated composite beam with localized matrix cracks. Such cracks can be the result of low velocity impact damage and are hard to detect visually. Natural frequencies are used in conjunction with modular radial basis function neural networks for damage detection. The mode shapes are utilized to obtain a damage indicator called curvature damage factor (CDF). A matrix crack based damage model is integrated with a beam finite element model to simulate the damaged composite beam structure. In the matrix crack model, the stiffness of the beam is degraded by a reduction in A, B and D matrices to simulate the damage and the damage level is represented by matrix crack density. It is found that the combination of modular radial basis neural networks with natural frequencies and CDF can be used as robust damage detection tools for localized matrix cracks in composite beams. More... »

PAGES

127-140

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s41683-017-0013-z

DOI

http://dx.doi.org/10.1007/s41683-017-0013-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1091893930


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/2008/0912", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Materials Engineering", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/09", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Engineering", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Vikram Sarabhai Space Centre", 
          "id": "https://www.grid.ac/institutes/grid.450282.9", 
          "name": [
            "Vikram Sarabhai Space Centre, Indian Space Research Organisation, 695022, Thiruvananthapuram, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sarangapani", 
        "givenName": "G.", 
        "id": "sg:person.016611005153.06", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016611005153.06"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Indian Institute of Science Bangalore", 
          "id": "https://www.grid.ac/institutes/grid.34980.36", 
          "name": [
            "Department of Aerospace Engineering, Indian Institute of Science, 560012, Bangalore, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ganguli", 
        "givenName": "Ranjan", 
        "id": "sg:person.014206373615.51", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014206373615.51"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/s1359-835x(02)00081-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000953943"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0022-460x(91)90595-b", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001371649"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0022-460x(91)90595-b", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001371649"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0964-1726/18/2/025002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002534914"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0020-7683(96)00156-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002767759"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0964-1726/12/2/311", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003286726"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.compstruc.2009.08.017", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006501257"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0168-874x(02)00227-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008982474"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.compstruct.2016.03.027", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011113791"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jsv.2009.05.030", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011144113"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/15376490903056577", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011337173"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ymssp.2006.10.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012149320"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.proeng.2011.07.390", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012399372"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.commatsci.2009.12.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012438130"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.compstruct.2015.07.082", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013068726"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.compstruct.2015.07.082", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013068726"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.compstruct.2015.07.082", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013068726"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.compstruct.2015.07.082", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013068726"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0964-1726/10/3/319", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014413978"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.compscitech.2004.09.021", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015441266"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/10759419608945865", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018372744"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00739291", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021222396", 
          "https://doi.org/10.1007/bf00739291"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00739291", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021222396", 
          "https://doi.org/10.1007/bf00739291"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jsv.2004.01.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021499651"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1006/jsvi.1999.2295", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024231195"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/107594199305511", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032359593"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.compositesb.2015.06.010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033437200"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2514/6.2008-1735", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033535559"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/15376494.2011.621838", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034459427"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0020-7683(93)90110-s", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034940910"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0020-7683(93)90110-s", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034940910"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.compstruc.2007.05.034", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035680309"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0141-0296(96)00149-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039616551"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.compositesb.2014.12.032", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040656068"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ress.2010.02.004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047278642"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jsv.2016.06.047", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047297456"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.compstruct.2006.05.026", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052858468"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0964-1726/13/4/029", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052924553"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1106/3c65-marc-9xtm-345x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060842664"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1106/3c65-marc-9xtm-345x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060842664"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/002199838902301102", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063624555"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/002199838902301102", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063624555"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1243/03093247v142049", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064446866"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1243/03093247v142049", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064446866"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1243/03093247v142049", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064446866"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.12989/sss.2008.4.1.099", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064865327"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.12989/sss.2010.6.1.039", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064865419"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.12989/sss.2011.8.2.191", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064865519"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.12989/sss.2012.9.4.335", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064865586"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2017-11", 
    "datePublishedReg": "2017-11-01", 
    "description": "Dynamic characteristics such as natural frequencies and mode shapes are used to identify the location of damage and the damage level in a laminated composite beam with localized matrix cracks. Such cracks can be the result of low velocity impact damage and are hard to detect visually. Natural frequencies are used in conjunction with modular radial basis function neural networks for damage detection. The mode shapes are utilized to obtain a damage indicator called curvature damage factor (CDF). A matrix crack based damage model is integrated with a beam finite element model to simulate the damaged composite beam structure. In the matrix crack model, the stiffness of the beam is degraded by a reduction in A, B and D matrices to simulate the damage and the damage level is represented by matrix crack density. It is found that the combination of modular radial basis neural networks with natural frequencies and CDF can be used as robust damage detection tools for localized matrix cracks in composite beams.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s41683-017-0013-z", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1290428", 
        "issn": [
          "2509-7989", 
          "2509-7997"
        ], 
        "name": "ISSS Journal of Micro and Smart Systems", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "6"
      }
    ], 
    "name": "Detection of local matrix cracks in composite beam using modal data and modular radial basis neural networks", 
    "pagination": "127-140", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "dd5257c26643fe90ea6a1744e1e6a0c3fbcd1956fd74a175d09a78d65dc614f7"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s41683-017-0013-z"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1091893930"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s41683-017-0013-z", 
      "https://app.dimensions.ai/details/publication/pub.1091893930"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T21:41", 
    "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_8687_00000535.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs41683-017-0013-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.10.1007/s41683-017-0013-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.10.1007/s41683-017-0013-z'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s41683-017-0013-z'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s41683-017-0013-z'


 

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

189 TRIPLES      21 PREDICATES      66 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s41683-017-0013-z schema:about anzsrc-for:09
2 anzsrc-for:0912
3 schema:author N00214445df4e42cfbabc2f140e9407e6
4 schema:citation sg:pub.10.1007/bf00739291
5 https://doi.org/10.1006/jsvi.1999.2295
6 https://doi.org/10.1016/0020-7683(93)90110-s
7 https://doi.org/10.1016/0022-460x(91)90595-b
8 https://doi.org/10.1016/j.commatsci.2009.12.003
9 https://doi.org/10.1016/j.compositesb.2014.12.032
10 https://doi.org/10.1016/j.compositesb.2015.06.010
11 https://doi.org/10.1016/j.compscitech.2004.09.021
12 https://doi.org/10.1016/j.compstruc.2007.05.034
13 https://doi.org/10.1016/j.compstruc.2009.08.017
14 https://doi.org/10.1016/j.compstruct.2006.05.026
15 https://doi.org/10.1016/j.compstruct.2015.07.082
16 https://doi.org/10.1016/j.compstruct.2016.03.027
17 https://doi.org/10.1016/j.jsv.2004.01.003
18 https://doi.org/10.1016/j.jsv.2009.05.030
19 https://doi.org/10.1016/j.jsv.2016.06.047
20 https://doi.org/10.1016/j.proeng.2011.07.390
21 https://doi.org/10.1016/j.ress.2010.02.004
22 https://doi.org/10.1016/j.ymssp.2006.10.002
23 https://doi.org/10.1016/s0020-7683(96)00156-4
24 https://doi.org/10.1016/s0141-0296(96)00149-6
25 https://doi.org/10.1016/s0168-874x(02)00227-5
26 https://doi.org/10.1016/s1359-835x(02)00081-7
27 https://doi.org/10.1080/10759419608945865
28 https://doi.org/10.1080/107594199305511
29 https://doi.org/10.1080/15376490903056577
30 https://doi.org/10.1080/15376494.2011.621838
31 https://doi.org/10.1088/0964-1726/10/3/319
32 https://doi.org/10.1088/0964-1726/12/2/311
33 https://doi.org/10.1088/0964-1726/13/4/029
34 https://doi.org/10.1088/0964-1726/18/2/025002
35 https://doi.org/10.1106/3c65-marc-9xtm-345x
36 https://doi.org/10.1177/002199838902301102
37 https://doi.org/10.1243/03093247v142049
38 https://doi.org/10.12989/sss.2008.4.1.099
39 https://doi.org/10.12989/sss.2010.6.1.039
40 https://doi.org/10.12989/sss.2011.8.2.191
41 https://doi.org/10.12989/sss.2012.9.4.335
42 https://doi.org/10.2514/6.2008-1735
43 schema:datePublished 2017-11
44 schema:datePublishedReg 2017-11-01
45 schema:description Dynamic characteristics such as natural frequencies and mode shapes are used to identify the location of damage and the damage level in a laminated composite beam with localized matrix cracks. Such cracks can be the result of low velocity impact damage and are hard to detect visually. Natural frequencies are used in conjunction with modular radial basis function neural networks for damage detection. The mode shapes are utilized to obtain a damage indicator called curvature damage factor (CDF). A matrix crack based damage model is integrated with a beam finite element model to simulate the damaged composite beam structure. In the matrix crack model, the stiffness of the beam is degraded by a reduction in A, B and D matrices to simulate the damage and the damage level is represented by matrix crack density. It is found that the combination of modular radial basis neural networks with natural frequencies and CDF can be used as robust damage detection tools for localized matrix cracks in composite beams.
46 schema:genre research_article
47 schema:inLanguage en
48 schema:isAccessibleForFree false
49 schema:isPartOf N50638d236c22498a90c53bb850b652db
50 N64ec116487064e3fbe132f3e6c4dfa2e
51 sg:journal.1290428
52 schema:name Detection of local matrix cracks in composite beam using modal data and modular radial basis neural networks
53 schema:pagination 127-140
54 schema:productId N1c4ac6d879594120927a6be7b255239e
55 N36ec3f56f5af42ffbf692ed1688ae7ec
56 N860aee46a4ce47d0aa84bccc93ce7c96
57 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091893930
58 https://doi.org/10.1007/s41683-017-0013-z
59 schema:sdDatePublished 2019-04-10T21:41
60 schema:sdLicense https://scigraph.springernature.com/explorer/license/
61 schema:sdPublisher Nf1e1d332ab364047b6c41e3397c58499
62 schema:url http://link.springer.com/10.1007%2Fs41683-017-0013-z
63 sgo:license sg:explorer/license/
64 sgo:sdDataset articles
65 rdf:type schema:ScholarlyArticle
66 N00214445df4e42cfbabc2f140e9407e6 rdf:first sg:person.016611005153.06
67 rdf:rest N3ec0cd00172f44ab9f54465ca77e2b58
68 N1c4ac6d879594120927a6be7b255239e schema:name dimensions_id
69 schema:value pub.1091893930
70 rdf:type schema:PropertyValue
71 N36ec3f56f5af42ffbf692ed1688ae7ec schema:name doi
72 schema:value 10.1007/s41683-017-0013-z
73 rdf:type schema:PropertyValue
74 N3ec0cd00172f44ab9f54465ca77e2b58 rdf:first sg:person.014206373615.51
75 rdf:rest rdf:nil
76 N50638d236c22498a90c53bb850b652db schema:issueNumber 2
77 rdf:type schema:PublicationIssue
78 N64ec116487064e3fbe132f3e6c4dfa2e schema:volumeNumber 6
79 rdf:type schema:PublicationVolume
80 N860aee46a4ce47d0aa84bccc93ce7c96 schema:name readcube_id
81 schema:value dd5257c26643fe90ea6a1744e1e6a0c3fbcd1956fd74a175d09a78d65dc614f7
82 rdf:type schema:PropertyValue
83 Nf1e1d332ab364047b6c41e3397c58499 schema:name Springer Nature - SN SciGraph project
84 rdf:type schema:Organization
85 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
86 schema:name Engineering
87 rdf:type schema:DefinedTerm
88 anzsrc-for:0912 schema:inDefinedTermSet anzsrc-for:
89 schema:name Materials Engineering
90 rdf:type schema:DefinedTerm
91 sg:journal.1290428 schema:issn 2509-7989
92 2509-7997
93 schema:name ISSS Journal of Micro and Smart Systems
94 rdf:type schema:Periodical
95 sg:person.014206373615.51 schema:affiliation https://www.grid.ac/institutes/grid.34980.36
96 schema:familyName Ganguli
97 schema:givenName Ranjan
98 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014206373615.51
99 rdf:type schema:Person
100 sg:person.016611005153.06 schema:affiliation https://www.grid.ac/institutes/grid.450282.9
101 schema:familyName Sarangapani
102 schema:givenName G.
103 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016611005153.06
104 rdf:type schema:Person
105 sg:pub.10.1007/bf00739291 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021222396
106 https://doi.org/10.1007/bf00739291
107 rdf:type schema:CreativeWork
108 https://doi.org/10.1006/jsvi.1999.2295 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024231195
109 rdf:type schema:CreativeWork
110 https://doi.org/10.1016/0020-7683(93)90110-s schema:sameAs https://app.dimensions.ai/details/publication/pub.1034940910
111 rdf:type schema:CreativeWork
112 https://doi.org/10.1016/0022-460x(91)90595-b schema:sameAs https://app.dimensions.ai/details/publication/pub.1001371649
113 rdf:type schema:CreativeWork
114 https://doi.org/10.1016/j.commatsci.2009.12.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012438130
115 rdf:type schema:CreativeWork
116 https://doi.org/10.1016/j.compositesb.2014.12.032 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040656068
117 rdf:type schema:CreativeWork
118 https://doi.org/10.1016/j.compositesb.2015.06.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033437200
119 rdf:type schema:CreativeWork
120 https://doi.org/10.1016/j.compscitech.2004.09.021 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015441266
121 rdf:type schema:CreativeWork
122 https://doi.org/10.1016/j.compstruc.2007.05.034 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035680309
123 rdf:type schema:CreativeWork
124 https://doi.org/10.1016/j.compstruc.2009.08.017 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006501257
125 rdf:type schema:CreativeWork
126 https://doi.org/10.1016/j.compstruct.2006.05.026 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052858468
127 rdf:type schema:CreativeWork
128 https://doi.org/10.1016/j.compstruct.2015.07.082 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013068726
129 rdf:type schema:CreativeWork
130 https://doi.org/10.1016/j.compstruct.2016.03.027 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011113791
131 rdf:type schema:CreativeWork
132 https://doi.org/10.1016/j.jsv.2004.01.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021499651
133 rdf:type schema:CreativeWork
134 https://doi.org/10.1016/j.jsv.2009.05.030 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011144113
135 rdf:type schema:CreativeWork
136 https://doi.org/10.1016/j.jsv.2016.06.047 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047297456
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1016/j.proeng.2011.07.390 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012399372
139 rdf:type schema:CreativeWork
140 https://doi.org/10.1016/j.ress.2010.02.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047278642
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1016/j.ymssp.2006.10.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012149320
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1016/s0020-7683(96)00156-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002767759
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1016/s0141-0296(96)00149-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039616551
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1016/s0168-874x(02)00227-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008982474
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1016/s1359-835x(02)00081-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000953943
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1080/10759419608945865 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018372744
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1080/107594199305511 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032359593
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1080/15376490903056577 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011337173
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1080/15376494.2011.621838 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034459427
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1088/0964-1726/10/3/319 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014413978
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1088/0964-1726/12/2/311 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003286726
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1088/0964-1726/13/4/029 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052924553
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1088/0964-1726/18/2/025002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002534914
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1106/3c65-marc-9xtm-345x schema:sameAs https://app.dimensions.ai/details/publication/pub.1060842664
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1177/002199838902301102 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063624555
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1243/03093247v142049 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064446866
173 rdf:type schema:CreativeWork
174 https://doi.org/10.12989/sss.2008.4.1.099 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064865327
175 rdf:type schema:CreativeWork
176 https://doi.org/10.12989/sss.2010.6.1.039 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064865419
177 rdf:type schema:CreativeWork
178 https://doi.org/10.12989/sss.2011.8.2.191 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064865519
179 rdf:type schema:CreativeWork
180 https://doi.org/10.12989/sss.2012.9.4.335 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064865586
181 rdf:type schema:CreativeWork
182 https://doi.org/10.2514/6.2008-1735 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033535559
183 rdf:type schema:CreativeWork
184 https://www.grid.ac/institutes/grid.34980.36 schema:alternateName Indian Institute of Science Bangalore
185 schema:name Department of Aerospace Engineering, Indian Institute of Science, 560012, Bangalore, India
186 rdf:type schema:Organization
187 https://www.grid.ac/institutes/grid.450282.9 schema:alternateName Vikram Sarabhai Space Centre
188 schema:name Vikram Sarabhai Space Centre, Indian Space Research Organisation, 695022, Thiruvananthapuram, India
189 rdf:type schema:Organization
 




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


...