Iterative NC program modification and energy saving for a CNC machine tool feed drive system with linear motors View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-02-19

AUTHORS

Yogi Muldani Hendrawan, Abdallah Farrage, Naoki Uchiyama

ABSTRACT

This study proposes a design of iterative learning contouring controller (ILCC) by considering actual contour error compensation (ACEC) to enhance the contouring performance of CNC machine tool feed drive systems with linear motors. The ACEC with linear and circular interpolation is designed to estimates contour error precisely. The proposed control iteratively modifies the numerical control (NC) programs for each drive axis to reduce a contour error. Hence, the proposed approach can be directly applied for a commercial CNC machine tool with linear motors currently in use without any modification of their original controllers. Both of linear and circular interpolations are verified by simulation in both air-cutting and machining condition. The simulation is conducted for a non-smooth rhombus and circular trajectory. The effectiveness of the proposed methods has been experimentally verified through a CNC machine tool with linear motors for a non-smooth rhombus trajectory. Experimental results show that the proposed controller could reduce the maximum and mean contour errors by 94.58% and 88.67% on average, respectively. The proposed method improved the control input variance by 37.9%, and consequently energy consumption was reduced by 11.7% compared with the original NC program. More... »

PAGES

1-20

References to SciGraph publications

  • 2016-12. Application of cuckoo search algorithm to constrained control problem of a parallel robot platform in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • 2010-07. Solution of inverse dynamics problems for contour error minimization in CNC machines in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • 2016-12. Iterative pre-compensation scheme of tracking error for contouring error reduction in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • 2013-07. Cubic spline trajectory generation with axis jerk and tracking error constraints in INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING
  • 2016-04. Robust adaptive cross-coupling position control of biaxial motion system in SCIENCE CHINA TECHNOLOGICAL SCIENCES
  • 2017-03. Adaptive feedrate planning for continuous parametric tool path with confined contour error and axis jerks in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • 2017-03. Estimation and compensation for continuous-path running trajectory error in high-feed-speed machining in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00170-019-03390-1

    DOI

    http://dx.doi.org/10.1007/s00170-019-03390-1

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1112227538


    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": "Toyohashi University of Technology", 
              "id": "https://www.grid.ac/institutes/grid.412804.b", 
              "name": [
                "Department of Manufacturing Engineering Technology, Bandung Polytechnic for Manufacturing, st. Kanayakan 21 Dago, 40135, Bandung, Indonesia", 
                "Department of Mechanical Engineering, Toyohashi University of Technology, 441-8580, Toyohashi, Aichi, Japan"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Hendrawan", 
            "givenName": "Yogi Muldani", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Toyohashi University of Technology", 
              "id": "https://www.grid.ac/institutes/grid.412804.b", 
              "name": [
                "Department of Mechanical Engineering, Faculty of Engineering, Assiut University, 71515, Assiut, Egypt", 
                "Department of Mechanical Engineering, Toyohashi University of Technology, 441-8580, Toyohashi, Aichi, Japan"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Farrage", 
            "givenName": "Abdallah", 
            "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, Aichi, Japan"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Uchiyama", 
            "givenName": "Naoki", 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/s00170-016-8627-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002129807", 
              "https://doi.org/10.1007/s00170-016-8627-z"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11431-015-5988-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004290576", 
              "https://doi.org/10.1007/s11431-015-5988-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jmatprotec.2007.04.046", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006433372"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ijmachtools.2007.04.009", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007524689"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ijmachtools.2012.07.012", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008590922"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ijmachtools.2012.07.012", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008590922"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-016-8735-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013903382", 
              "https://doi.org/10.1007/s00170-016-8735-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0007-8506(07)63255-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015126941"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ijmachtools.2013.09.009", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016489166"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.rcim.2003.06.001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017964337"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.rcim.2003.06.001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017964337"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1155/2012/536326", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019022954"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0890-6955(02)00109-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022413570"
            ], 
            "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.ijmachtools.2003.10.001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025080725"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0890-6955(00)00089-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025579293"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-016-9202-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026684222", 
              "https://doi.org/10.1007/s00170-016-9202-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-016-9202-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026684222", 
              "https://doi.org/10.1007/s00170-016-9202-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12541-013-0155-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028860674", 
              "https://doi.org/10.1007/s12541-013-0155-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ijmachtools.2014.07.010", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029391528"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ijmachtools.2016.12.007", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032245239"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0890-6955(98)00095-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037948109"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.mechatronics.2005.05.003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039279330"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-016-9021-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041120711", 
              "https://doi.org/10.1007/s00170-016-9021-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-016-9021-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041120711", 
              "https://doi.org/10.1007/s00170-016-9021-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-009-2407-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041943145", 
              "https://doi.org/10.1007/s00170-009-2407-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-009-2407-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041943145", 
              "https://doi.org/10.1007/s00170-009-2407-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-009-2407-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041943145", 
              "https://doi.org/10.1007/s00170-009-2407-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ijmachtools.2011.10.001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049065534"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1017/s0263574714000769", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1054048208"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1088/0964-1726/25/9/095046", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1059121631"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/28.485814", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061142491"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tcst.2008.919433", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061572716"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tcst.2014.2327578", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061573719"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tie.2016.2542787", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061627889"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1115/1.2801310", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062082878"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1115/1.2802506", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062083124"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1115/1.2896129", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062090844"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1115/1.2897354", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062091324"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1115/1.3143822", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062103346"
            ], 
            "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/10.1016/j.ijmachtools.2017.02.002", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1083801603"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ijmachtools.2017.10.008", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092348317"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/elecsym.2016.7860972", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095165299"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/etfa.2015.7301493", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095587136"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/acc.2015.7171056", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095712152"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.rcim.2017.12.009", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1101083743"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/med.2018.8442532", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1106345471"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/med.2018.8442936", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1106345553"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2019-02-19", 
        "datePublishedReg": "2019-02-19", 
        "description": "This study proposes a design of iterative learning contouring controller (ILCC) by considering actual contour error compensation (ACEC) to enhance the contouring performance of CNC machine tool feed drive systems with linear motors. The ACEC with linear and circular interpolation is designed to estimates contour error precisely. The proposed control iteratively modifies the numerical control (NC) programs for each drive axis to reduce a contour error. Hence, the proposed approach can be directly applied for a commercial CNC machine tool with linear motors currently in use without any modification of their original controllers. Both of linear and circular interpolations are verified by simulation in both air-cutting and machining condition. The simulation is conducted for a non-smooth rhombus and circular trajectory. The effectiveness of the proposed methods has been experimentally verified through a CNC machine tool with linear motors for a non-smooth rhombus trajectory. Experimental results show that the proposed controller could reduce the maximum and mean contour errors by 94.58% and 88.67% on average, respectively. The proposed method improved the control input variance by 37.9%, and consequently energy consumption was reduced by 11.7% compared with the original NC program.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s00170-019-03390-1", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1043671", 
            "issn": [
              "0268-3768", 
              "1433-3015"
            ], 
            "name": "The International Journal of Advanced Manufacturing Technology", 
            "type": "Periodical"
          }
        ], 
        "name": "Iterative NC program modification and energy saving for a CNC machine tool feed drive system with linear motors", 
        "pagination": "1-20", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "a124041000cc6b48ce1c8ffe6dcd2f347bcd6eef9451db25eb9abf2238cdc609"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s00170-019-03390-1"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1112227538"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s00170-019-03390-1", 
          "https://app.dimensions.ai/details/publication/pub.1112227538"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T09:13", 
        "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/v90/0000000338_0000000338/records_47989_00000003.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://link.springer.com/10.1007%2Fs00170-019-03390-1"
      }
    ]
     

    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/s00170-019-03390-1'

    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/s00170-019-03390-1'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00170-019-03390-1'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00170-019-03390-1'


     

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

    204 TRIPLES      21 PREDICATES      67 URIs      16 LITERALS      5 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s00170-019-03390-1 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author N06a900b2d18d4e9da95f3478c531797f
    4 schema:citation sg:pub.10.1007/s00170-009-2407-y
    5 sg:pub.10.1007/s00170-016-8627-z
    6 sg:pub.10.1007/s00170-016-8735-9
    7 sg:pub.10.1007/s00170-016-9021-6
    8 sg:pub.10.1007/s00170-016-9202-3
    9 sg:pub.10.1007/s11431-015-5988-8
    10 sg:pub.10.1007/s12541-013-0155-2
    11 https://doi.org/10.1016/j.ijmachtools.2003.10.001
    12 https://doi.org/10.1016/j.ijmachtools.2004.08.008
    13 https://doi.org/10.1016/j.ijmachtools.2007.04.009
    14 https://doi.org/10.1016/j.ijmachtools.2011.10.001
    15 https://doi.org/10.1016/j.ijmachtools.2012.07.012
    16 https://doi.org/10.1016/j.ijmachtools.2013.09.009
    17 https://doi.org/10.1016/j.ijmachtools.2014.07.010
    18 https://doi.org/10.1016/j.ijmachtools.2016.12.007
    19 https://doi.org/10.1016/j.ijmachtools.2017.02.002
    20 https://doi.org/10.1016/j.ijmachtools.2017.10.008
    21 https://doi.org/10.1016/j.jmatprotec.2007.04.046
    22 https://doi.org/10.1016/j.mechatronics.2005.05.003
    23 https://doi.org/10.1016/j.rcim.2003.06.001
    24 https://doi.org/10.1016/j.rcim.2017.12.009
    25 https://doi.org/10.1016/s0007-8506(07)63255-7
    26 https://doi.org/10.1016/s0890-6955(00)00089-4
    27 https://doi.org/10.1016/s0890-6955(02)00109-8
    28 https://doi.org/10.1016/s0890-6955(98)00095-9
    29 https://doi.org/10.1017/s0263574714000769
    30 https://doi.org/10.1088/0964-1726/25/9/095046
    31 https://doi.org/10.1109/28.485814
    32 https://doi.org/10.1109/acc.2015.7171056
    33 https://doi.org/10.1109/elecsym.2016.7860972
    34 https://doi.org/10.1109/etfa.2015.7301493
    35 https://doi.org/10.1109/med.2018.8442532
    36 https://doi.org/10.1109/med.2018.8442936
    37 https://doi.org/10.1109/tcst.2008.919433
    38 https://doi.org/10.1109/tcst.2014.2327578
    39 https://doi.org/10.1109/tie.2016.2542787
    40 https://doi.org/10.1115/1.2801310
    41 https://doi.org/10.1115/1.2802506
    42 https://doi.org/10.1115/1.2896129
    43 https://doi.org/10.1115/1.2897354
    44 https://doi.org/10.1115/1.3143822
    45 https://doi.org/10.1115/1.3149612
    46 https://doi.org/10.1155/2012/536326
    47 schema:datePublished 2019-02-19
    48 schema:datePublishedReg 2019-02-19
    49 schema:description This study proposes a design of iterative learning contouring controller (ILCC) by considering actual contour error compensation (ACEC) to enhance the contouring performance of CNC machine tool feed drive systems with linear motors. The ACEC with linear and circular interpolation is designed to estimates contour error precisely. The proposed control iteratively modifies the numerical control (NC) programs for each drive axis to reduce a contour error. Hence, the proposed approach can be directly applied for a commercial CNC machine tool with linear motors currently in use without any modification of their original controllers. Both of linear and circular interpolations are verified by simulation in both air-cutting and machining condition. The simulation is conducted for a non-smooth rhombus and circular trajectory. The effectiveness of the proposed methods has been experimentally verified through a CNC machine tool with linear motors for a non-smooth rhombus trajectory. Experimental results show that the proposed controller could reduce the maximum and mean contour errors by 94.58% and 88.67% on average, respectively. The proposed method improved the control input variance by 37.9%, and consequently energy consumption was reduced by 11.7% compared with the original NC program.
    50 schema:genre research_article
    51 schema:inLanguage en
    52 schema:isAccessibleForFree false
    53 schema:isPartOf sg:journal.1043671
    54 schema:name Iterative NC program modification and energy saving for a CNC machine tool feed drive system with linear motors
    55 schema:pagination 1-20
    56 schema:productId N03c76f6266134815b15df9b9f703f5c2
    57 N645d973a63e14bcaaa1ad8eae4b905dc
    58 N8121f37a73c243ae8c911ef97ce9c8e3
    59 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112227538
    60 https://doi.org/10.1007/s00170-019-03390-1
    61 schema:sdDatePublished 2019-04-11T09:13
    62 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    63 schema:sdPublisher N99cc5714b85d4bfbb71e2e0a984ddb49
    64 schema:url https://link.springer.com/10.1007%2Fs00170-019-03390-1
    65 sgo:license sg:explorer/license/
    66 sgo:sdDataset articles
    67 rdf:type schema:ScholarlyArticle
    68 N03c76f6266134815b15df9b9f703f5c2 schema:name dimensions_id
    69 schema:value pub.1112227538
    70 rdf:type schema:PropertyValue
    71 N06a900b2d18d4e9da95f3478c531797f rdf:first Ne79adb6d44874e0688e09f611f254f88
    72 rdf:rest N5d4071ed23064aaa9b1f6561d8041d4c
    73 N564c3d5e885849859f65b2927011edc7 rdf:first Nb899796cad5d4f96b6e4dbf1e5c7b2d1
    74 rdf:rest rdf:nil
    75 N56f1d9671d704c40a6cc574ff74c2dd0 schema:affiliation https://www.grid.ac/institutes/grid.412804.b
    76 schema:familyName Farrage
    77 schema:givenName Abdallah
    78 rdf:type schema:Person
    79 N5d4071ed23064aaa9b1f6561d8041d4c rdf:first N56f1d9671d704c40a6cc574ff74c2dd0
    80 rdf:rest N564c3d5e885849859f65b2927011edc7
    81 N645d973a63e14bcaaa1ad8eae4b905dc schema:name readcube_id
    82 schema:value a124041000cc6b48ce1c8ffe6dcd2f347bcd6eef9451db25eb9abf2238cdc609
    83 rdf:type schema:PropertyValue
    84 N8121f37a73c243ae8c911ef97ce9c8e3 schema:name doi
    85 schema:value 10.1007/s00170-019-03390-1
    86 rdf:type schema:PropertyValue
    87 N99cc5714b85d4bfbb71e2e0a984ddb49 schema:name Springer Nature - SN SciGraph project
    88 rdf:type schema:Organization
    89 Nb899796cad5d4f96b6e4dbf1e5c7b2d1 schema:affiliation https://www.grid.ac/institutes/grid.412804.b
    90 schema:familyName Uchiyama
    91 schema:givenName Naoki
    92 rdf:type schema:Person
    93 Ne79adb6d44874e0688e09f611f254f88 schema:affiliation https://www.grid.ac/institutes/grid.412804.b
    94 schema:familyName Hendrawan
    95 schema:givenName Yogi Muldani
    96 rdf:type schema:Person
    97 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    98 schema:name Information and Computing Sciences
    99 rdf:type schema:DefinedTerm
    100 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    101 schema:name Artificial Intelligence and Image Processing
    102 rdf:type schema:DefinedTerm
    103 sg:journal.1043671 schema:issn 0268-3768
    104 1433-3015
    105 schema:name The International Journal of Advanced Manufacturing Technology
    106 rdf:type schema:Periodical
    107 sg:pub.10.1007/s00170-009-2407-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1041943145
    108 https://doi.org/10.1007/s00170-009-2407-y
    109 rdf:type schema:CreativeWork
    110 sg:pub.10.1007/s00170-016-8627-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1002129807
    111 https://doi.org/10.1007/s00170-016-8627-z
    112 rdf:type schema:CreativeWork
    113 sg:pub.10.1007/s00170-016-8735-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013903382
    114 https://doi.org/10.1007/s00170-016-8735-9
    115 rdf:type schema:CreativeWork
    116 sg:pub.10.1007/s00170-016-9021-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041120711
    117 https://doi.org/10.1007/s00170-016-9021-6
    118 rdf:type schema:CreativeWork
    119 sg:pub.10.1007/s00170-016-9202-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026684222
    120 https://doi.org/10.1007/s00170-016-9202-3
    121 rdf:type schema:CreativeWork
    122 sg:pub.10.1007/s11431-015-5988-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004290576
    123 https://doi.org/10.1007/s11431-015-5988-8
    124 rdf:type schema:CreativeWork
    125 sg:pub.10.1007/s12541-013-0155-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028860674
    126 https://doi.org/10.1007/s12541-013-0155-2
    127 rdf:type schema:CreativeWork
    128 https://doi.org/10.1016/j.ijmachtools.2003.10.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025080725
    129 rdf:type schema:CreativeWork
    130 https://doi.org/10.1016/j.ijmachtools.2004.08.008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023509489
    131 rdf:type schema:CreativeWork
    132 https://doi.org/10.1016/j.ijmachtools.2007.04.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007524689
    133 rdf:type schema:CreativeWork
    134 https://doi.org/10.1016/j.ijmachtools.2011.10.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049065534
    135 rdf:type schema:CreativeWork
    136 https://doi.org/10.1016/j.ijmachtools.2012.07.012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008590922
    137 rdf:type schema:CreativeWork
    138 https://doi.org/10.1016/j.ijmachtools.2013.09.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016489166
    139 rdf:type schema:CreativeWork
    140 https://doi.org/10.1016/j.ijmachtools.2014.07.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029391528
    141 rdf:type schema:CreativeWork
    142 https://doi.org/10.1016/j.ijmachtools.2016.12.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032245239
    143 rdf:type schema:CreativeWork
    144 https://doi.org/10.1016/j.ijmachtools.2017.02.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083801603
    145 rdf:type schema:CreativeWork
    146 https://doi.org/10.1016/j.ijmachtools.2017.10.008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092348317
    147 rdf:type schema:CreativeWork
    148 https://doi.org/10.1016/j.jmatprotec.2007.04.046 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006433372
    149 rdf:type schema:CreativeWork
    150 https://doi.org/10.1016/j.mechatronics.2005.05.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039279330
    151 rdf:type schema:CreativeWork
    152 https://doi.org/10.1016/j.rcim.2003.06.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017964337
    153 rdf:type schema:CreativeWork
    154 https://doi.org/10.1016/j.rcim.2017.12.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101083743
    155 rdf:type schema:CreativeWork
    156 https://doi.org/10.1016/s0007-8506(07)63255-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015126941
    157 rdf:type schema:CreativeWork
    158 https://doi.org/10.1016/s0890-6955(00)00089-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025579293
    159 rdf:type schema:CreativeWork
    160 https://doi.org/10.1016/s0890-6955(02)00109-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022413570
    161 rdf:type schema:CreativeWork
    162 https://doi.org/10.1016/s0890-6955(98)00095-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037948109
    163 rdf:type schema:CreativeWork
    164 https://doi.org/10.1017/s0263574714000769 schema:sameAs https://app.dimensions.ai/details/publication/pub.1054048208
    165 rdf:type schema:CreativeWork
    166 https://doi.org/10.1088/0964-1726/25/9/095046 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059121631
    167 rdf:type schema:CreativeWork
    168 https://doi.org/10.1109/28.485814 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061142491
    169 rdf:type schema:CreativeWork
    170 https://doi.org/10.1109/acc.2015.7171056 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095712152
    171 rdf:type schema:CreativeWork
    172 https://doi.org/10.1109/elecsym.2016.7860972 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095165299
    173 rdf:type schema:CreativeWork
    174 https://doi.org/10.1109/etfa.2015.7301493 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095587136
    175 rdf:type schema:CreativeWork
    176 https://doi.org/10.1109/med.2018.8442532 schema:sameAs https://app.dimensions.ai/details/publication/pub.1106345471
    177 rdf:type schema:CreativeWork
    178 https://doi.org/10.1109/med.2018.8442936 schema:sameAs https://app.dimensions.ai/details/publication/pub.1106345553
    179 rdf:type schema:CreativeWork
    180 https://doi.org/10.1109/tcst.2008.919433 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061572716
    181 rdf:type schema:CreativeWork
    182 https://doi.org/10.1109/tcst.2014.2327578 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061573719
    183 rdf:type schema:CreativeWork
    184 https://doi.org/10.1109/tie.2016.2542787 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061627889
    185 rdf:type schema:CreativeWork
    186 https://doi.org/10.1115/1.2801310 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062082878
    187 rdf:type schema:CreativeWork
    188 https://doi.org/10.1115/1.2802506 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062083124
    189 rdf:type schema:CreativeWork
    190 https://doi.org/10.1115/1.2896129 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062090844
    191 rdf:type schema:CreativeWork
    192 https://doi.org/10.1115/1.2897354 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062091324
    193 rdf:type schema:CreativeWork
    194 https://doi.org/10.1115/1.3143822 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062103346
    195 rdf:type schema:CreativeWork
    196 https://doi.org/10.1115/1.3149612 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062103634
    197 rdf:type schema:CreativeWork
    198 https://doi.org/10.1155/2012/536326 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019022954
    199 rdf:type schema:CreativeWork
    200 https://www.grid.ac/institutes/grid.412804.b schema:alternateName Toyohashi University of Technology
    201 schema:name Department of Manufacturing Engineering Technology, Bandung Polytechnic for Manufacturing, st. Kanayakan 21 Dago, 40135, Bandung, Indonesia
    202 Department of Mechanical Engineering, Faculty of Engineering, Assiut University, 71515, Assiut, Egypt
    203 Department of Mechanical Engineering, Toyohashi University of Technology, 441-8580, Toyohashi, Aichi, Japan
    204 rdf:type schema:Organization
     




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


    ...