On-line prediction of micro-turning multi-response variables by machine vision system using adaptive neuro-fuzzy inference system (ANFIS) View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2013-01

AUTHORS

S. Palani, U. Natarajan, M. Chellamalai

ABSTRACT

In this paper, a new attempt has been made in the area of tool-based micromachining for automated, non-contact, and flexible prediction of quality responses such as average surface roughness (Ra), tool wear ratio (TWR) and metal removal rate (MRR) of micro-turned miniaturized parts through a machine vision system (MVS) which is integrated with an adaptive neuro-fuzzy inference system (ANFIS). The images of machined surface grabbed by the MVS could be extracted using the algorithm developed in this work, to get the features of image texture [average gray level (Ga)]. This work presents an area-based surface characterization technique which applies the basic light scattering principles used in other optimal optical measurement systems. These principles are applied in a novel fashion which is especially suitable for in-process prediction and control. The main objective of this study is to design an ANFIS for estimation of Ra, TWR, and MRR in micro-turning process. Cutting speed (S), feed rate (F), depth of cut (D), Ga were taken as input parameters and Ra, TWR, MRR as the output parameters. The results obtained from the ANFIS model were compared with experimental values. It is found that the predicted values of the responses are in good agreement with the experimental values. More... »

PAGES

19-32

References to SciGraph publications

  • 2008-01. Application of multiple regression and adaptive neuro fuzzy inference system for the prediction of surface roughness in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • 2010-01. Noncontact roughness measurement of turned parts using machine vision in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00138-011-0378-0

    DOI

    http://dx.doi.org/10.1007/s00138-011-0378-0

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1011764086


    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": "Anna University, Chennai", 
              "id": "https://www.grid.ac/institutes/grid.252262.3", 
              "name": [
                "Department of Mechanical Engineering, Mount Zion College of Engineering and Technology, Pudukkottai, Tamilnadu, India"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Palani", 
            "givenName": "S.", 
            "id": "sg:person.010277542365.73", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010277542365.73"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "Department of Mechanical Engineering, A.C. College of Engineering and Technology, Karaikudi, Tamilnadu, India"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Natarajan", 
            "givenName": "U.", 
            "id": "sg:person.010134230365.57", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010134230365.57"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Central Manufacturing Technology Institute", 
              "id": "https://www.grid.ac/institutes/grid.464765.2", 
              "name": [
                "Department of Micro and Precision Machining, 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/s0007-8506(07)63454-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001984386"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ijmachtools.2006.05.005", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004152410"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ijmachtools.2009.02.001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008533020"
            ], 
            "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/s0957-4158(02)00096-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009156232"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0957-4158(02)00096-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009156232"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-006-0755-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011969254", 
              "https://doi.org/10.1007/s00170-006-0755-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-006-0755-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011969254", 
              "https://doi.org/10.1007/s00170-006-0755-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-009-2101-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014255045", 
              "https://doi.org/10.1007/s00170-009-2101-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-009-2101-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014255045", 
              "https://doi.org/10.1007/s00170-009-2101-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-009-2101-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014255045", 
              "https://doi.org/10.1007/s00170-009-2101-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0924-0136(96)02818-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014710465"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0890-6955(96)00011-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017805046"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1080/09511920802287138", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027561039"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0007-8506(07)62071-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031468131"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0890-6955(02)00078-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032452785"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0007-8506(07)63451-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042649695"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0924-0136(03)00687-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043456750"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0924-0136(03)00687-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043456750"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.apm.2010.07.048", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047033721"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.eswa.2008.01.051", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050784513"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1504/ijmsi.2007.013870", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1067479070"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2013-01", 
        "datePublishedReg": "2013-01-01", 
        "description": "In this paper, a new attempt has been made in the area of tool-based micromachining for automated, non-contact, and flexible prediction of quality responses such as average surface roughness (Ra), tool wear ratio (TWR) and metal removal rate (MRR) of micro-turned miniaturized parts through a machine vision system (MVS) which is integrated with an adaptive neuro-fuzzy inference system (ANFIS). The images of machined surface grabbed by the MVS could be extracted using the algorithm developed in this work, to get the features of image texture [average gray level (Ga)]. This work presents an area-based surface characterization technique which applies the basic light scattering principles used in other optimal optical measurement systems. These principles are applied in a novel fashion which is especially suitable for in-process prediction and control. The main objective of this study is to design an ANFIS for estimation of Ra, TWR, and MRR in micro-turning process. Cutting speed (S), feed rate (F), depth of cut (D), Ga were taken as input parameters and Ra, TWR, MRR as the output parameters. The results obtained from the ANFIS model were compared with experimental values. It is found that the predicted values of the responses are in good agreement with the experimental values.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s00138-011-0378-0", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1045266", 
            "issn": [
              "0932-8092", 
              "1432-1769"
            ], 
            "name": "Machine Vision and Applications", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "24"
          }
        ], 
        "name": "On-line prediction of micro-turning multi-response variables by machine vision system using adaptive neuro-fuzzy inference system (ANFIS)", 
        "pagination": "19-32", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "380cfa9ac96b446ed6d00a6b87db627d6d0b2bfe27f7ceca9f349a3fc187d4ec"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s00138-011-0378-0"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1011764086"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s00138-011-0378-0", 
          "https://app.dimensions.ai/details/publication/pub.1011764086"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-10T14:52", 
        "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/v90/0000000001_0000000264/records_8663_00000480.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "http://link.springer.com/10.1007/s00138-011-0378-0"
      }
    ]
     

    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/s00138-011-0378-0'

    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/s00138-011-0378-0'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00138-011-0378-0'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00138-011-0378-0'


     

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

    133 TRIPLES      21 PREDICATES      44 URIs      19 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s00138-011-0378-0 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author N9096349d29d54e889066a29b3e3a14dd
    4 schema:citation sg:pub.10.1007/s00170-006-0755-4
    5 sg:pub.10.1007/s00170-009-2101-0
    6 https://doi.org/10.1016/j.apm.2010.07.048
    7 https://doi.org/10.1016/j.eswa.2008.01.051
    8 https://doi.org/10.1016/j.ijmachtools.2004.09.007
    9 https://doi.org/10.1016/j.ijmachtools.2006.05.005
    10 https://doi.org/10.1016/j.ijmachtools.2009.02.001
    11 https://doi.org/10.1016/s0007-8506(07)62071-x
    12 https://doi.org/10.1016/s0007-8506(07)63451-9
    13 https://doi.org/10.1016/s0007-8506(07)63454-4
    14 https://doi.org/10.1016/s0890-6955(02)00078-0
    15 https://doi.org/10.1016/s0890-6955(96)00011-9
    16 https://doi.org/10.1016/s0924-0136(03)00687-3
    17 https://doi.org/10.1016/s0924-0136(96)02818-x
    18 https://doi.org/10.1016/s0957-4158(02)00096-x
    19 https://doi.org/10.1080/09511920802287138
    20 https://doi.org/10.1504/ijmsi.2007.013870
    21 schema:datePublished 2013-01
    22 schema:datePublishedReg 2013-01-01
    23 schema:description In this paper, a new attempt has been made in the area of tool-based micromachining for automated, non-contact, and flexible prediction of quality responses such as average surface roughness (Ra), tool wear ratio (TWR) and metal removal rate (MRR) of micro-turned miniaturized parts through a machine vision system (MVS) which is integrated with an adaptive neuro-fuzzy inference system (ANFIS). The images of machined surface grabbed by the MVS could be extracted using the algorithm developed in this work, to get the features of image texture [average gray level (Ga)]. This work presents an area-based surface characterization technique which applies the basic light scattering principles used in other optimal optical measurement systems. These principles are applied in a novel fashion which is especially suitable for in-process prediction and control. The main objective of this study is to design an ANFIS for estimation of Ra, TWR, and MRR in micro-turning process. Cutting speed (S), feed rate (F), depth of cut (D), Ga were taken as input parameters and Ra, TWR, MRR as the output parameters. The results obtained from the ANFIS model were compared with experimental values. It is found that the predicted values of the responses are in good agreement with the experimental values.
    24 schema:genre research_article
    25 schema:inLanguage en
    26 schema:isAccessibleForFree false
    27 schema:isPartOf N29b7f9464c3b424aafab949a2a629fbc
    28 N99ab9df33f6f4ee3af81ad57bbb70bd6
    29 sg:journal.1045266
    30 schema:name On-line prediction of micro-turning multi-response variables by machine vision system using adaptive neuro-fuzzy inference system (ANFIS)
    31 schema:pagination 19-32
    32 schema:productId N45d0d48462114d6c95cd0acebe9f83aa
    33 Ne4b0874aa21f4b1bb5e74157cf89f2e9
    34 Nf774d67bb79a4c389b5c658f7afa067e
    35 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011764086
    36 https://doi.org/10.1007/s00138-011-0378-0
    37 schema:sdDatePublished 2019-04-10T14:52
    38 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    39 schema:sdPublisher Na455ca017dc84b4c99f8ae3aba6bee02
    40 schema:url http://link.springer.com/10.1007/s00138-011-0378-0
    41 sgo:license sg:explorer/license/
    42 sgo:sdDataset articles
    43 rdf:type schema:ScholarlyArticle
    44 N29b7f9464c3b424aafab949a2a629fbc schema:volumeNumber 24
    45 rdf:type schema:PublicationVolume
    46 N45d0d48462114d6c95cd0acebe9f83aa schema:name readcube_id
    47 schema:value 380cfa9ac96b446ed6d00a6b87db627d6d0b2bfe27f7ceca9f349a3fc187d4ec
    48 rdf:type schema:PropertyValue
    49 N69403ad7479d4d3abe78d4417a9cb627 rdf:first sg:person.010134230365.57
    50 rdf:rest Nf3317f834116497b9d44a5fab497997e
    51 N9096349d29d54e889066a29b3e3a14dd rdf:first sg:person.010277542365.73
    52 rdf:rest N69403ad7479d4d3abe78d4417a9cb627
    53 N99ab9df33f6f4ee3af81ad57bbb70bd6 schema:issueNumber 1
    54 rdf:type schema:PublicationIssue
    55 Na455ca017dc84b4c99f8ae3aba6bee02 schema:name Springer Nature - SN SciGraph project
    56 rdf:type schema:Organization
    57 Ne4b0874aa21f4b1bb5e74157cf89f2e9 schema:name doi
    58 schema:value 10.1007/s00138-011-0378-0
    59 rdf:type schema:PropertyValue
    60 Nef2962ce346d4556ae6eb7a8b72e93b5 schema:name Department of Mechanical Engineering, A.C. College of Engineering and Technology, Karaikudi, Tamilnadu, India
    61 rdf:type schema:Organization
    62 Nf3317f834116497b9d44a5fab497997e rdf:first sg:person.014660405365.52
    63 rdf:rest rdf:nil
    64 Nf774d67bb79a4c389b5c658f7afa067e schema:name dimensions_id
    65 schema:value pub.1011764086
    66 rdf:type schema:PropertyValue
    67 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    68 schema:name Information and Computing Sciences
    69 rdf:type schema:DefinedTerm
    70 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    71 schema:name Artificial Intelligence and Image Processing
    72 rdf:type schema:DefinedTerm
    73 sg:journal.1045266 schema:issn 0932-8092
    74 1432-1769
    75 schema:name Machine Vision and Applications
    76 rdf:type schema:Periodical
    77 sg:person.010134230365.57 schema:affiliation Nef2962ce346d4556ae6eb7a8b72e93b5
    78 schema:familyName Natarajan
    79 schema:givenName U.
    80 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010134230365.57
    81 rdf:type schema:Person
    82 sg:person.010277542365.73 schema:affiliation https://www.grid.ac/institutes/grid.252262.3
    83 schema:familyName Palani
    84 schema:givenName S.
    85 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010277542365.73
    86 rdf:type schema:Person
    87 sg:person.014660405365.52 schema:affiliation https://www.grid.ac/institutes/grid.464765.2
    88 schema:familyName Chellamalai
    89 schema:givenName M.
    90 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014660405365.52
    91 rdf:type schema:Person
    92 sg:pub.10.1007/s00170-006-0755-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011969254
    93 https://doi.org/10.1007/s00170-006-0755-4
    94 rdf:type schema:CreativeWork
    95 sg:pub.10.1007/s00170-009-2101-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014255045
    96 https://doi.org/10.1007/s00170-009-2101-0
    97 rdf:type schema:CreativeWork
    98 https://doi.org/10.1016/j.apm.2010.07.048 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047033721
    99 rdf:type schema:CreativeWork
    100 https://doi.org/10.1016/j.eswa.2008.01.051 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050784513
    101 rdf:type schema:CreativeWork
    102 https://doi.org/10.1016/j.ijmachtools.2004.09.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008609174
    103 rdf:type schema:CreativeWork
    104 https://doi.org/10.1016/j.ijmachtools.2006.05.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004152410
    105 rdf:type schema:CreativeWork
    106 https://doi.org/10.1016/j.ijmachtools.2009.02.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008533020
    107 rdf:type schema:CreativeWork
    108 https://doi.org/10.1016/s0007-8506(07)62071-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1031468131
    109 rdf:type schema:CreativeWork
    110 https://doi.org/10.1016/s0007-8506(07)63451-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042649695
    111 rdf:type schema:CreativeWork
    112 https://doi.org/10.1016/s0007-8506(07)63454-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001984386
    113 rdf:type schema:CreativeWork
    114 https://doi.org/10.1016/s0890-6955(02)00078-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032452785
    115 rdf:type schema:CreativeWork
    116 https://doi.org/10.1016/s0890-6955(96)00011-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017805046
    117 rdf:type schema:CreativeWork
    118 https://doi.org/10.1016/s0924-0136(03)00687-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043456750
    119 rdf:type schema:CreativeWork
    120 https://doi.org/10.1016/s0924-0136(96)02818-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1014710465
    121 rdf:type schema:CreativeWork
    122 https://doi.org/10.1016/s0957-4158(02)00096-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1009156232
    123 rdf:type schema:CreativeWork
    124 https://doi.org/10.1080/09511920802287138 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027561039
    125 rdf:type schema:CreativeWork
    126 https://doi.org/10.1504/ijmsi.2007.013870 schema:sameAs https://app.dimensions.ai/details/publication/pub.1067479070
    127 rdf:type schema:CreativeWork
    128 https://www.grid.ac/institutes/grid.252262.3 schema:alternateName Anna University, Chennai
    129 schema:name Department of Mechanical Engineering, Mount Zion College of Engineering and Technology, Pudukkottai, Tamilnadu, India
    130 rdf:type schema:Organization
    131 https://www.grid.ac/institutes/grid.464765.2 schema:alternateName Central Manufacturing Technology Institute
    132 schema:name Department of Micro and Precision Machining, Central Manufacturing Technology Institute, Bangalore, India
    133 rdf:type schema:Organization
     




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


    ...