Monitoring of hard turning using acoustic emission signal View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2012-02

AUTHORS

J. Bhaskaran, M. Murugan, N. Balashanmugam, M. Chellamalai

ABSTRACT

Monitoring of tool wear during hard turning is essential. Many investigators have analyzed the acoustic emission (AE) signals generated during machining to understand the metal cutting process and for monitoring tool wear and failure. In the current study on hard turning, the skew and kurtosis parameters of the root mean square values of AE signal (AERMS) are used to monitor tool wear. The rubbing between the tool and the workpiece increases as the tool wear crosses a threshold, thereby shifting the mass of AERMS distribution to right, leading to a negative skew. The increased rubbing also led to a high kurtosis value in the AERMS distribution curve. More... »

PAGES

609-615

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12206-011-1036-1

DOI

http://dx.doi.org/10.1007/s12206-011-1036-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1034057184


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": "B.S. Abdur Rahman University", 
          "id": "https://www.grid.ac/institutes/grid.449273.f", 
          "name": [
            "Faculty of Mechanical Engineering, B.S. Abdur Rahman University, Chennai, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bhaskaran", 
        "givenName": "J.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "B.S. Abdur Rahman University", 
          "id": "https://www.grid.ac/institutes/grid.449273.f", 
          "name": [
            "Faculty of Mechanical Engineering, B.S. Abdur Rahman University, Chennai, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Murugan", 
        "givenName": "M.", 
        "id": "sg:person.010434245265.20", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010434245265.20"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Central Manufacturing Technology Institute", 
          "id": "https://www.grid.ac/institutes/grid.464765.2", 
          "name": [
            "Central Manufacturing Technology Institute, Bangalore, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Balashanmugam", 
        "givenName": "N.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Central Manufacturing Technology Institute", 
          "id": "https://www.grid.ac/institutes/grid.464765.2", 
          "name": [
            "Central Manufacturing Technology Institute, Bangalore, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chellamalai", 
        "givenName": "M.", 
        "id": "sg:person.014660405365.52", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014660405365.52"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/j.jmatprotec.2007.12.018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000663901"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0963-8695(92)90636-u", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001085709"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0963-8695(92)90636-u", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001085709"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0890-6955(99)00103-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002410779"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00170-003-1878-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003833523", 
          "https://doi.org/10.1007/s00170-003-1878-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00170-003-1878-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003833523", 
          "https://doi.org/10.1007/s00170-003-1878-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ijmachtools.2004.09.007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008609174"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0007-8506(07)63455-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011617713"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0890-6955(01)00108-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012466626"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0890-6955(95)00074-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013855596"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0007-8506(07)62385-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018805159"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ijmachtools.2005.02.007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019385522"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cirp.2010.05.010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023422759"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0007-8506(07)60654-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027954077"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.sna.2006.08.011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031679783"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0007-8506(07)61465-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032484908"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0007-8506(07)60768-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033576911"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jmatprotec.2005.03.038", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034989174"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00170-003-1569-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038552028", 
          "https://doi.org/10.1007/s00170-003-1569-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0890-6955(03)00110-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038951622"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0890-6955(03)00110-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038951622"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0043-1648(82)90009-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039069156"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0043-1648(82)90009-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039069156"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0007-8506(07)62660-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040309364"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0043-1648(97)00139-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042732732"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0020-7403(80)90029-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043558654"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0020-7403(80)90029-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043558654"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ijmachtools.2006.03.020", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045145806"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2012-02", 
    "datePublishedReg": "2012-02-01", 
    "description": "Monitoring of tool wear during hard turning is essential. Many investigators have analyzed the acoustic emission (AE) signals generated during machining to understand the metal cutting process and for monitoring tool wear and failure. In the current study on hard turning, the skew and kurtosis parameters of the root mean square values of AE signal (AERMS) are used to monitor tool wear. The rubbing between the tool and the workpiece increases as the tool wear crosses a threshold, thereby shifting the mass of AERMS distribution to right, leading to a negative skew. The increased rubbing also led to a high kurtosis value in the AERMS distribution curve.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s12206-011-1036-1", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1295111", 
        "issn": [
          "1011-8861", 
          "1226-4865"
        ], 
        "name": "Journal of Mechanical Science and Technology", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "26"
      }
    ], 
    "name": "Monitoring of hard turning using acoustic emission signal", 
    "pagination": "609-615", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "a23d7344cf6ad0872ef0ae9c5c533fbd67402f0181d7b66ad9d74b03b3024fa8"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s12206-011-1036-1"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1034057184"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s12206-011-1036-1", 
      "https://app.dimensions.ai/details/publication/pub.1034057184"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T18:23", 
    "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_8675_00000523.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs12206-011-1036-1"
  }
]
 

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/s12206-011-1036-1'

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/s12206-011-1036-1'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s12206-011-1036-1'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s12206-011-1036-1'


 

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

154 TRIPLES      21 PREDICATES      50 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s12206-011-1036-1 schema:about anzsrc-for:09
2 anzsrc-for:0912
3 schema:author N006714103a3843dcb440614556733db4
4 schema:citation sg:pub.10.1007/s00170-003-1569-2
5 sg:pub.10.1007/s00170-003-1878-5
6 https://doi.org/10.1016/0020-7403(80)90029-6
7 https://doi.org/10.1016/0043-1648(82)90009-6
8 https://doi.org/10.1016/0890-6955(95)00074-7
9 https://doi.org/10.1016/0963-8695(92)90636-u
10 https://doi.org/10.1016/j.cirp.2010.05.010
11 https://doi.org/10.1016/j.ijmachtools.2004.09.007
12 https://doi.org/10.1016/j.ijmachtools.2005.02.007
13 https://doi.org/10.1016/j.ijmachtools.2006.03.020
14 https://doi.org/10.1016/j.jmatprotec.2005.03.038
15 https://doi.org/10.1016/j.jmatprotec.2007.12.018
16 https://doi.org/10.1016/j.sna.2006.08.011
17 https://doi.org/10.1016/s0007-8506(07)60654-4
18 https://doi.org/10.1016/s0007-8506(07)60768-9
19 https://doi.org/10.1016/s0007-8506(07)61465-6
20 https://doi.org/10.1016/s0007-8506(07)62385-3
21 https://doi.org/10.1016/s0007-8506(07)62660-2
22 https://doi.org/10.1016/s0007-8506(07)63455-6
23 https://doi.org/10.1016/s0043-1648(97)00139-7
24 https://doi.org/10.1016/s0890-6955(01)00108-0
25 https://doi.org/10.1016/s0890-6955(03)00110-x
26 https://doi.org/10.1016/s0890-6955(99)00103-0
27 schema:datePublished 2012-02
28 schema:datePublishedReg 2012-02-01
29 schema:description Monitoring of tool wear during hard turning is essential. Many investigators have analyzed the acoustic emission (AE) signals generated during machining to understand the metal cutting process and for monitoring tool wear and failure. In the current study on hard turning, the skew and kurtosis parameters of the root mean square values of AE signal (AERMS) are used to monitor tool wear. The rubbing between the tool and the workpiece increases as the tool wear crosses a threshold, thereby shifting the mass of AERMS distribution to right, leading to a negative skew. The increased rubbing also led to a high kurtosis value in the AERMS distribution curve.
30 schema:genre research_article
31 schema:inLanguage en
32 schema:isAccessibleForFree false
33 schema:isPartOf N40238a4ac92d4f238c22bec2a919d33c
34 N8cebeb7d709642c4b84793b8a46e9569
35 sg:journal.1295111
36 schema:name Monitoring of hard turning using acoustic emission signal
37 schema:pagination 609-615
38 schema:productId N096780b0e9124166b480c8a75d76ebc0
39 N0ac1fadd13314c1f95fed8649d15c301
40 N6b62ade334b94baebeed1460cfa391ec
41 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034057184
42 https://doi.org/10.1007/s12206-011-1036-1
43 schema:sdDatePublished 2019-04-10T18:23
44 schema:sdLicense https://scigraph.springernature.com/explorer/license/
45 schema:sdPublisher Ndab184e7799d467e842cb7bc77678f4b
46 schema:url http://link.springer.com/10.1007%2Fs12206-011-1036-1
47 sgo:license sg:explorer/license/
48 sgo:sdDataset articles
49 rdf:type schema:ScholarlyArticle
50 N006714103a3843dcb440614556733db4 rdf:first Nd629a5cf8c7846598c8dabd3176092f4
51 rdf:rest N3ba0730521644e1fbcfc6c5502ab42dd
52 N096780b0e9124166b480c8a75d76ebc0 schema:name readcube_id
53 schema:value a23d7344cf6ad0872ef0ae9c5c533fbd67402f0181d7b66ad9d74b03b3024fa8
54 rdf:type schema:PropertyValue
55 N0ac1fadd13314c1f95fed8649d15c301 schema:name doi
56 schema:value 10.1007/s12206-011-1036-1
57 rdf:type schema:PropertyValue
58 N3ba0730521644e1fbcfc6c5502ab42dd rdf:first sg:person.010434245265.20
59 rdf:rest N68f79e30c2884788afba2ec823a794cd
60 N40238a4ac92d4f238c22bec2a919d33c schema:issueNumber 2
61 rdf:type schema:PublicationIssue
62 N68f79e30c2884788afba2ec823a794cd rdf:first N8ac2ba65d19c48c9a5013ba1dea28f64
63 rdf:rest N71e3d92f67eb4d77b39481fe28a5ac5a
64 N6b62ade334b94baebeed1460cfa391ec schema:name dimensions_id
65 schema:value pub.1034057184
66 rdf:type schema:PropertyValue
67 N71e3d92f67eb4d77b39481fe28a5ac5a rdf:first sg:person.014660405365.52
68 rdf:rest rdf:nil
69 N8ac2ba65d19c48c9a5013ba1dea28f64 schema:affiliation https://www.grid.ac/institutes/grid.464765.2
70 schema:familyName Balashanmugam
71 schema:givenName N.
72 rdf:type schema:Person
73 N8cebeb7d709642c4b84793b8a46e9569 schema:volumeNumber 26
74 rdf:type schema:PublicationVolume
75 Nd629a5cf8c7846598c8dabd3176092f4 schema:affiliation https://www.grid.ac/institutes/grid.449273.f
76 schema:familyName Bhaskaran
77 schema:givenName J.
78 rdf:type schema:Person
79 Ndab184e7799d467e842cb7bc77678f4b schema:name Springer Nature - SN SciGraph project
80 rdf:type schema:Organization
81 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
82 schema:name Engineering
83 rdf:type schema:DefinedTerm
84 anzsrc-for:0912 schema:inDefinedTermSet anzsrc-for:
85 schema:name Materials Engineering
86 rdf:type schema:DefinedTerm
87 sg:journal.1295111 schema:issn 1011-8861
88 1226-4865
89 schema:name Journal of Mechanical Science and Technology
90 rdf:type schema:Periodical
91 sg:person.010434245265.20 schema:affiliation https://www.grid.ac/institutes/grid.449273.f
92 schema:familyName Murugan
93 schema:givenName M.
94 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010434245265.20
95 rdf:type schema:Person
96 sg:person.014660405365.52 schema:affiliation https://www.grid.ac/institutes/grid.464765.2
97 schema:familyName Chellamalai
98 schema:givenName M.
99 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014660405365.52
100 rdf:type schema:Person
101 sg:pub.10.1007/s00170-003-1569-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038552028
102 https://doi.org/10.1007/s00170-003-1569-2
103 rdf:type schema:CreativeWork
104 sg:pub.10.1007/s00170-003-1878-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003833523
105 https://doi.org/10.1007/s00170-003-1878-5
106 rdf:type schema:CreativeWork
107 https://doi.org/10.1016/0020-7403(80)90029-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043558654
108 rdf:type schema:CreativeWork
109 https://doi.org/10.1016/0043-1648(82)90009-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039069156
110 rdf:type schema:CreativeWork
111 https://doi.org/10.1016/0890-6955(95)00074-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013855596
112 rdf:type schema:CreativeWork
113 https://doi.org/10.1016/0963-8695(92)90636-u schema:sameAs https://app.dimensions.ai/details/publication/pub.1001085709
114 rdf:type schema:CreativeWork
115 https://doi.org/10.1016/j.cirp.2010.05.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023422759
116 rdf:type schema:CreativeWork
117 https://doi.org/10.1016/j.ijmachtools.2004.09.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008609174
118 rdf:type schema:CreativeWork
119 https://doi.org/10.1016/j.ijmachtools.2005.02.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019385522
120 rdf:type schema:CreativeWork
121 https://doi.org/10.1016/j.ijmachtools.2006.03.020 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045145806
122 rdf:type schema:CreativeWork
123 https://doi.org/10.1016/j.jmatprotec.2005.03.038 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034989174
124 rdf:type schema:CreativeWork
125 https://doi.org/10.1016/j.jmatprotec.2007.12.018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000663901
126 rdf:type schema:CreativeWork
127 https://doi.org/10.1016/j.sna.2006.08.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031679783
128 rdf:type schema:CreativeWork
129 https://doi.org/10.1016/s0007-8506(07)60654-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027954077
130 rdf:type schema:CreativeWork
131 https://doi.org/10.1016/s0007-8506(07)60768-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033576911
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1016/s0007-8506(07)61465-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032484908
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1016/s0007-8506(07)62385-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018805159
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1016/s0007-8506(07)62660-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040309364
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1016/s0007-8506(07)63455-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011617713
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1016/s0043-1648(97)00139-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042732732
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1016/s0890-6955(01)00108-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012466626
144 rdf:type schema:CreativeWork
145 https://doi.org/10.1016/s0890-6955(03)00110-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1038951622
146 rdf:type schema:CreativeWork
147 https://doi.org/10.1016/s0890-6955(99)00103-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002410779
148 rdf:type schema:CreativeWork
149 https://www.grid.ac/institutes/grid.449273.f schema:alternateName B.S. Abdur Rahman University
150 schema:name Faculty of Mechanical Engineering, B.S. Abdur Rahman University, Chennai, India
151 rdf:type schema:Organization
152 https://www.grid.ac/institutes/grid.464765.2 schema:alternateName Central Manufacturing Technology Institute
153 schema:name Central Manufacturing Technology Institute, Bangalore, India
154 rdf:type schema:Organization
 




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


...