Improvement of motion accuracy and energy consumption of a mechanical feed drive system using a Fourier series-based nonlinear friction model View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-11

AUTHORS

Abdallah Farrage, Naoki Uchiyama

ABSTRACT

Friction occurring in all mechanical systems, such as computer numerical controlled (CNC) machine tools, is an important issue in achieving the high accurate performance. Friction adversely affects not only motion accuracy of drive axes but also excessively consumes energy. Feed drives of CNC machines normally operate all day and night around the world, and therefore consumed energy reduction is highly expected. The motivation behind this work is to construct a novel friction model that can comprise many unknown friction sources in both low and high velocity regions and enable a friction compensator to precisely describe actual frictional behavior. A sliding mode control (SMC) is designed to verify the effectives of the proposed friction model in a biaxial feed drive system. Experimental results confirm that a combination of SMC and the proposed friction can effectively improve tracking accuracy and further achieve significant reduction of consumed energy compared to combining with the conventional model. Results show that the proposed approach can largely decrease the mean tracking error to less than 5 µm for each axis. The new friction also achieved effective reduction of control variance by 7.62%. Consequently, consumed energy of feed drives was significantly improved by 12.83% compared to using the conventional model. More... »

PAGES

1203-1214

References to SciGraph publications

  • 2013-10. Tracking error reduction in CNC machining by reshaping the kinematic trajectory in JOURNAL OF SYSTEMS SCIENCE AND COMPLEXITY
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00170-018-2413-z

    DOI

    http://dx.doi.org/10.1007/s00170-018-2413-z

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1106195668


    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": "Assiut University", 
              "id": "https://www.grid.ac/institutes/grid.252487.e", 
              "name": [
                "Department of Mechanical Engineering, Toyohashi University of Technology, 441-8580, Toyohashi, Japan", 
                "Assistant Lecturer in Mechanical Engineering Department, Faculty of Engineering, Assiut University, 71515, Assiut, Egypt"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Farrage", 
            "givenName": "Abdallah", 
            "id": "sg:person.07520735546.49", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07520735546.49"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Toyohashi University of Technology", 
              "id": "https://www.grid.ac/institutes/grid.412804.b", 
              "name": [
                "Department of Mechanical Engineering, Toyohashi University of Technology, 441-8580, Toyohashi, Japan"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Uchiyama", 
            "givenName": "Naoki", 
            "id": "sg:person.07460222141.02", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07460222141.02"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1016/j.procir.2014.08.021", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004806237"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.mechatronics.2005.03.003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015479804"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.mechatronics.2005.03.003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015479804"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ijmachtools.2004.08.008", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023509489"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.camwa.2011.11.053", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1034843017"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.conengprac.2003.10.006", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039477076"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11424-013-3179-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045623392", 
              "https://doi.org/10.1007/s11424-013-3179-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ijmachtools.2006.11.008", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1046439511"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0890-6955(01)00003-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053266385"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/9.376053", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061244371"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/9.847103", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061246282"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/9.995050", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061247005"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/mcs.2008.929425", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061397610"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/mcs.2008.929425", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061397610"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tac.2005.858676", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061476060"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tie.2013.2264786", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061626149"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tmech.2013.2296698", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061693220"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1115/1.3149612", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062103634"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.21236/ada041920", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092087796"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cca.1995.555721", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093779123"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/icmech.2013.6519147", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094108588"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/phycon.2003.1237071", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094194129"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/amc.2008.4516068", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094603908"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.23919/sice.2017.8105473", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095851388"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.mechatronics.2018.06.009", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1105773196"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/med.2018.8442918", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1106345545"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2018-11", 
        "datePublishedReg": "2018-11-01", 
        "description": "Friction occurring in all mechanical systems, such as computer numerical controlled (CNC) machine tools, is an important issue in achieving the high accurate performance. Friction adversely affects not only motion accuracy of drive axes but also excessively consumes energy. Feed drives of CNC machines normally operate all day and night around the world, and therefore consumed energy reduction is highly expected. The motivation behind this work is to construct a novel friction model that can comprise many unknown friction sources in both low and high velocity regions and enable a friction compensator to precisely describe actual frictional behavior. A sliding mode control (SMC) is designed to verify the effectives of the proposed friction model in a biaxial feed drive system. Experimental results confirm that a combination of SMC and the proposed friction can effectively improve tracking accuracy and further achieve significant reduction of consumed energy compared to combining with the conventional model. Results show that the proposed approach can largely decrease the mean tracking error to less than 5 \u00b5m for each axis. The new friction also achieved effective reduction of control variance by 7.62%. Consequently, consumed energy of feed drives was significantly improved by 12.83% compared to using the conventional model.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s00170-018-2413-z", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1043671", 
            "issn": [
              "0268-3768", 
              "1433-3015"
            ], 
            "name": "The International Journal of Advanced Manufacturing Technology", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "5-8", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "99"
          }
        ], 
        "name": "Improvement of motion accuracy and energy consumption of a mechanical feed drive system using a Fourier series-based nonlinear friction model", 
        "pagination": "1203-1214", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "dc10e54fe080dfa40f3b266470b833d0d11d0e343ba9b9c88483969b90d069d2"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s00170-018-2413-z"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1106195668"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s00170-018-2413-z", 
          "https://app.dimensions.ai/details/publication/pub.1106195668"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T00:25", 
        "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_8695_00000565.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://link.springer.com/10.1007%2Fs00170-018-2413-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/s00170-018-2413-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/s00170-018-2413-z'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00170-018-2413-z'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00170-018-2413-z'


     

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

    145 TRIPLES      21 PREDICATES      51 URIs      19 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s00170-018-2413-z schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author N42838b33d5834f0c83712f32b34009dd
    4 schema:citation sg:pub.10.1007/s11424-013-3179-x
    5 https://doi.org/10.1016/j.camwa.2011.11.053
    6 https://doi.org/10.1016/j.conengprac.2003.10.006
    7 https://doi.org/10.1016/j.ijmachtools.2004.08.008
    8 https://doi.org/10.1016/j.ijmachtools.2006.11.008
    9 https://doi.org/10.1016/j.mechatronics.2005.03.003
    10 https://doi.org/10.1016/j.mechatronics.2018.06.009
    11 https://doi.org/10.1016/j.procir.2014.08.021
    12 https://doi.org/10.1016/s0890-6955(01)00003-7
    13 https://doi.org/10.1109/9.376053
    14 https://doi.org/10.1109/9.847103
    15 https://doi.org/10.1109/9.995050
    16 https://doi.org/10.1109/amc.2008.4516068
    17 https://doi.org/10.1109/cca.1995.555721
    18 https://doi.org/10.1109/icmech.2013.6519147
    19 https://doi.org/10.1109/mcs.2008.929425
    20 https://doi.org/10.1109/med.2018.8442918
    21 https://doi.org/10.1109/phycon.2003.1237071
    22 https://doi.org/10.1109/tac.2005.858676
    23 https://doi.org/10.1109/tie.2013.2264786
    24 https://doi.org/10.1109/tmech.2013.2296698
    25 https://doi.org/10.1115/1.3149612
    26 https://doi.org/10.21236/ada041920
    27 https://doi.org/10.23919/sice.2017.8105473
    28 schema:datePublished 2018-11
    29 schema:datePublishedReg 2018-11-01
    30 schema:description Friction occurring in all mechanical systems, such as computer numerical controlled (CNC) machine tools, is an important issue in achieving the high accurate performance. Friction adversely affects not only motion accuracy of drive axes but also excessively consumes energy. Feed drives of CNC machines normally operate all day and night around the world, and therefore consumed energy reduction is highly expected. The motivation behind this work is to construct a novel friction model that can comprise many unknown friction sources in both low and high velocity regions and enable a friction compensator to precisely describe actual frictional behavior. A sliding mode control (SMC) is designed to verify the effectives of the proposed friction model in a biaxial feed drive system. Experimental results confirm that a combination of SMC and the proposed friction can effectively improve tracking accuracy and further achieve significant reduction of consumed energy compared to combining with the conventional model. Results show that the proposed approach can largely decrease the mean tracking error to less than 5 µm for each axis. The new friction also achieved effective reduction of control variance by 7.62%. Consequently, consumed energy of feed drives was significantly improved by 12.83% compared to using the conventional model.
    31 schema:genre research_article
    32 schema:inLanguage en
    33 schema:isAccessibleForFree false
    34 schema:isPartOf N084ed13dc9fc427daaf4be239cc94615
    35 Ne9f92ac790b14bdbaca4f0da57a53df2
    36 sg:journal.1043671
    37 schema:name Improvement of motion accuracy and energy consumption of a mechanical feed drive system using a Fourier series-based nonlinear friction model
    38 schema:pagination 1203-1214
    39 schema:productId N3c11086b9c9c45979fabd500f41ca726
    40 Nbbccd71587084f73b00cf1aa48308705
    41 Nfa28269fedbc43cd808c61d3dea60f07
    42 schema:sameAs https://app.dimensions.ai/details/publication/pub.1106195668
    43 https://doi.org/10.1007/s00170-018-2413-z
    44 schema:sdDatePublished 2019-04-11T00:25
    45 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    46 schema:sdPublisher N0dfca2cd46d049ddb3b43360f7786134
    47 schema:url https://link.springer.com/10.1007%2Fs00170-018-2413-z
    48 sgo:license sg:explorer/license/
    49 sgo:sdDataset articles
    50 rdf:type schema:ScholarlyArticle
    51 N084ed13dc9fc427daaf4be239cc94615 schema:volumeNumber 99
    52 rdf:type schema:PublicationVolume
    53 N0dfca2cd46d049ddb3b43360f7786134 schema:name Springer Nature - SN SciGraph project
    54 rdf:type schema:Organization
    55 N3c11086b9c9c45979fabd500f41ca726 schema:name doi
    56 schema:value 10.1007/s00170-018-2413-z
    57 rdf:type schema:PropertyValue
    58 N42838b33d5834f0c83712f32b34009dd rdf:first sg:person.07520735546.49
    59 rdf:rest N7a07d21345fd4ef3a1ea8e9aa2c462cb
    60 N7a07d21345fd4ef3a1ea8e9aa2c462cb rdf:first sg:person.07460222141.02
    61 rdf:rest rdf:nil
    62 Nbbccd71587084f73b00cf1aa48308705 schema:name readcube_id
    63 schema:value dc10e54fe080dfa40f3b266470b833d0d11d0e343ba9b9c88483969b90d069d2
    64 rdf:type schema:PropertyValue
    65 Ne9f92ac790b14bdbaca4f0da57a53df2 schema:issueNumber 5-8
    66 rdf:type schema:PublicationIssue
    67 Nfa28269fedbc43cd808c61d3dea60f07 schema:name dimensions_id
    68 schema:value pub.1106195668
    69 rdf:type schema:PropertyValue
    70 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    71 schema:name Information and Computing Sciences
    72 rdf:type schema:DefinedTerm
    73 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    74 schema:name Artificial Intelligence and Image Processing
    75 rdf:type schema:DefinedTerm
    76 sg:journal.1043671 schema:issn 0268-3768
    77 1433-3015
    78 schema:name The International Journal of Advanced Manufacturing Technology
    79 rdf:type schema:Periodical
    80 sg:person.07460222141.02 schema:affiliation https://www.grid.ac/institutes/grid.412804.b
    81 schema:familyName Uchiyama
    82 schema:givenName Naoki
    83 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07460222141.02
    84 rdf:type schema:Person
    85 sg:person.07520735546.49 schema:affiliation https://www.grid.ac/institutes/grid.252487.e
    86 schema:familyName Farrage
    87 schema:givenName Abdallah
    88 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07520735546.49
    89 rdf:type schema:Person
    90 sg:pub.10.1007/s11424-013-3179-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1045623392
    91 https://doi.org/10.1007/s11424-013-3179-x
    92 rdf:type schema:CreativeWork
    93 https://doi.org/10.1016/j.camwa.2011.11.053 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034843017
    94 rdf:type schema:CreativeWork
    95 https://doi.org/10.1016/j.conengprac.2003.10.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039477076
    96 rdf:type schema:CreativeWork
    97 https://doi.org/10.1016/j.ijmachtools.2004.08.008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023509489
    98 rdf:type schema:CreativeWork
    99 https://doi.org/10.1016/j.ijmachtools.2006.11.008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046439511
    100 rdf:type schema:CreativeWork
    101 https://doi.org/10.1016/j.mechatronics.2005.03.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015479804
    102 rdf:type schema:CreativeWork
    103 https://doi.org/10.1016/j.mechatronics.2018.06.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105773196
    104 rdf:type schema:CreativeWork
    105 https://doi.org/10.1016/j.procir.2014.08.021 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004806237
    106 rdf:type schema:CreativeWork
    107 https://doi.org/10.1016/s0890-6955(01)00003-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053266385
    108 rdf:type schema:CreativeWork
    109 https://doi.org/10.1109/9.376053 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061244371
    110 rdf:type schema:CreativeWork
    111 https://doi.org/10.1109/9.847103 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061246282
    112 rdf:type schema:CreativeWork
    113 https://doi.org/10.1109/9.995050 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061247005
    114 rdf:type schema:CreativeWork
    115 https://doi.org/10.1109/amc.2008.4516068 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094603908
    116 rdf:type schema:CreativeWork
    117 https://doi.org/10.1109/cca.1995.555721 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093779123
    118 rdf:type schema:CreativeWork
    119 https://doi.org/10.1109/icmech.2013.6519147 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094108588
    120 rdf:type schema:CreativeWork
    121 https://doi.org/10.1109/mcs.2008.929425 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061397610
    122 rdf:type schema:CreativeWork
    123 https://doi.org/10.1109/med.2018.8442918 schema:sameAs https://app.dimensions.ai/details/publication/pub.1106345545
    124 rdf:type schema:CreativeWork
    125 https://doi.org/10.1109/phycon.2003.1237071 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094194129
    126 rdf:type schema:CreativeWork
    127 https://doi.org/10.1109/tac.2005.858676 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061476060
    128 rdf:type schema:CreativeWork
    129 https://doi.org/10.1109/tie.2013.2264786 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061626149
    130 rdf:type schema:CreativeWork
    131 https://doi.org/10.1109/tmech.2013.2296698 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061693220
    132 rdf:type schema:CreativeWork
    133 https://doi.org/10.1115/1.3149612 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062103634
    134 rdf:type schema:CreativeWork
    135 https://doi.org/10.21236/ada041920 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092087796
    136 rdf:type schema:CreativeWork
    137 https://doi.org/10.23919/sice.2017.8105473 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095851388
    138 rdf:type schema:CreativeWork
    139 https://www.grid.ac/institutes/grid.252487.e schema:alternateName Assiut University
    140 schema:name Assistant Lecturer in Mechanical Engineering Department, Faculty of Engineering, Assiut University, 71515, Assiut, Egypt
    141 Department of Mechanical Engineering, Toyohashi University of Technology, 441-8580, Toyohashi, Japan
    142 rdf:type schema:Organization
    143 https://www.grid.ac/institutes/grid.412804.b schema:alternateName Toyohashi University of Technology
    144 schema:name Department of Mechanical Engineering, Toyohashi University of Technology, 441-8580, Toyohashi, Japan
    145 rdf:type schema:Organization
     




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


    ...