Soft abrasive flow polishing based on the cavitation effect View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-04

AUTHORS

Shiming Ji, Huiqiang Cao, Jun Zhao, Ye Pan, Enyong Jiang

ABSTRACT

The fluid medium used for soft abrasive flow (SAF) is of low viscosity and high flow velocity. The conventional SAF processing method is widely used due its ability to avoid damage and its adaptability of the workpiece surface. However, the current SAF method also suffers from limitations such as long polishing times and low processing efficiency. To address these issues, this paper proposes a method based on the cavitation effect to assist the soft abrasive flow polishing, termed CSAF. First, the working mechanism of CSAF is introduced through a schematic diagram. In addition, a CSAF fluid mechanic model is configured on the basis of the mixture multiphase model and cavitation model. Then, the distribution of key flow parameters, such as the velocity, dynamic pressure, and turbulent kinetic energy, is obtained and compared through computational fluid dynamic software. Numerical analysis results show that the flow field assisted by the cavitation effects shows better processing performance than SAF. Finally, particle imaging velocity (PIV) observation and experimental processing platforms are established, and extensive experiments are conducted. The processing comparison experiments showed that the abrasive flow assisted by the cavitation effect can lower the workpiece surface roughness to 3.46 nm with a satisfactory surface quality over a shorter time than the SAF method. The numerical analysis results are aligned with the PIV observation and the polishing experiment results. The SAF polishing method based on the cavitation effect significantly increases the polishing efficiency. More... »

PAGES

1865-1878

References to SciGraph publications

  • 2017-10. Gas compensation-based abrasive flow processing method for complex titanium alloy surfaces in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • 2019-01. Towards the understanding of creep-feed deep grinding of DD6 nickel-based single-crystal superalloy in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • 2019-01. Critical penetration condition and Ekman suction-extraction mechanism of a sink vortex in JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE A
  • 2019-02. An embedded self-adapting network service framework for networked manufacturing system in JOURNAL OF INTELLIGENT MANUFACTURING
  • 2018-03. A gas-liquid-solid three-phase abrasive flow processing method based on bubble collapsing in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • 2012-08. Softness abrasive flow method oriented to tiny scale mold structural surface in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • 2018-07. Comparative investigation on grindability of K4125 and Inconel718 nickel-based superalloys in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • 2017-04. A review on recent advances in machining methods based on abrasive jet polishing (AJP) in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • 2019-01. Material removal mechanism of PTMCs in high-speed grinding when considering consecutive action of two abrasive grains in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • 2018-09. Numerical study on flow characteristics and impact erosion in ultrasonic assisted waterjet machining in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • 2018-04. A novel Lap-MRF method for large aperture mirrors in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00170-018-2983-9

    DOI

    http://dx.doi.org/10.1007/s00170-018-2983-9

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1110202936


    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/2008/0915", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Interdisciplinary 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": "Zhejiang University of Technology", 
              "id": "https://www.grid.ac/institutes/grid.469325.f", 
              "name": [
                "Key Laboratory of Special Purpose Equipment and Advanced Processing Technology of the Ministry of Education, Zhejiang University of Technology, 310032, Hangzhou, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Ji", 
            "givenName": "Shiming", 
            "id": "sg:person.01046153163.59", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01046153163.59"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Zhejiang University of Technology", 
              "id": "https://www.grid.ac/institutes/grid.469325.f", 
              "name": [
                "Key Laboratory of Special Purpose Equipment and Advanced Processing Technology of the Ministry of Education, Zhejiang University of Technology, 310032, Hangzhou, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Cao", 
            "givenName": "Huiqiang", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Zhejiang University of Technology", 
              "id": "https://www.grid.ac/institutes/grid.469325.f", 
              "name": [
                "Key Laboratory of Special Purpose Equipment and Advanced Processing Technology of the Ministry of Education, Zhejiang University of Technology, 310032, Hangzhou, China", 
                "Yiwu Academy of Science and Technology, Zhejiang University of Technology, 322001, Yiwu, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Zhao", 
            "givenName": "Jun", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Zhejiang University of Technology", 
              "id": "https://www.grid.ac/institutes/grid.469325.f", 
              "name": [
                "Key Laboratory of Special Purpose Equipment and Advanced Processing Technology of the Ministry of Education, Zhejiang University of Technology, 310032, Hangzhou, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Pan", 
            "givenName": "Ye", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Zhejiang University of Technology", 
              "id": "https://www.grid.ac/institutes/grid.469325.f", 
              "name": [
                "Key Laboratory of Special Purpose Equipment and Advanced Processing Technology of the Ministry of Education, Zhejiang University of Technology, 310032, Hangzhou, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Jiang", 
            "givenName": "Enyong", 
            "id": "sg:person.015210475713.12", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015210475713.12"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1016/j.cirp.2013.03.010", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002499062"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.stam.2006.12.003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008645758"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.triboint.2007.02.013", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012270426"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3154/tvsj.23.107", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014567397"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3154/tvsj.23.107", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014567397"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.optcom.2012.06.015", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015739134"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0043-1648(63)90003-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016823502"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0043-1648(63)90003-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016823502"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10845-016-1265-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022477621", 
              "https://doi.org/10.1007/s10845-016-1265-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10845-016-1265-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022477621", 
              "https://doi.org/10.1007/s10845-016-1265-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.precisioneng.2015.04.004", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024520903"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.triboint.2016.04.006", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024768310"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-016-9405-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032989241", 
              "https://doi.org/10.1007/s00170-016-9405-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-016-9405-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032989241", 
              "https://doi.org/10.1007/s00170-016-9405-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.solmat.2007.03.017", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1035847494"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ijmachtools.2010.03.007", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040541875"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jmatprotec.2008.05.012", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043049606"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.powtec.2016.08.057", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050689781"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-011-3621-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052942639", 
              "https://doi.org/10.1007/s00170-011-3621-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-011-3621-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052942639", 
              "https://doi.org/10.1007/s00170-011-3621-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1088/0004-637x/806/1/61", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1058945517"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physrevlett.109.264502", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1060760753"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physrevlett.109.264502", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1060760753"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1115/1.1524584", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062071572"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1209/epl/i2004-10435-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1064237217"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1364/ao.37.006771", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1065113687"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3901/jme.2012.15.193", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1071553406"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.wear.2017.02.030", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1083868301"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-017-0400-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085050040", 
              "https://doi.org/10.1007/s00170-017-0400-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-017-0400-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085050040", 
              "https://doi.org/10.1007/s00170-017-0400-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.cirp.2017.04.083", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085183219"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-017-1250-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092579135", 
              "https://doi.org/10.1007/s00170-017-1250-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ultsonch.2017.11.016", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092668454"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tii.2017.2773644", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092718339"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ces.2017.11.037", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092957074"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-017-1498-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1100405748", 
              "https://doi.org/10.1007/s00170-017-1498-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-018-1993-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1103687202", 
              "https://doi.org/10.1007/s00170-018-1993-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-018-1993-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1103687202", 
              "https://doi.org/10.1007/s00170-018-1993-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jmatprotec.2018.04.043", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1103688830"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.wear.2018.06.001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1104422939"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.wear.2018.06.001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1104422939"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-018-2271-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1104496021", 
              "https://doi.org/10.1007/s00170-018-2271-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.precisioneng.2018.09.001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1106858273"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.precisioneng.2018.09.001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1106858273"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1631/jzus.a1800260", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1107135863", 
              "https://doi.org/10.1631/jzus.a1800260"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1631/jzus.a1800260", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1107135863", 
              "https://doi.org/10.1631/jzus.a1800260"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-018-2685-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1107186981", 
              "https://doi.org/10.1007/s00170-018-2685-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-018-2685-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1107186981", 
              "https://doi.org/10.1007/s00170-018-2685-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-018-2685-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1107186981", 
              "https://doi.org/10.1007/s00170-018-2685-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-018-2686-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1107229126", 
              "https://doi.org/10.1007/s00170-018-2686-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-018-2686-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1107229126", 
              "https://doi.org/10.1007/s00170-018-2686-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-018-2686-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1107229126", 
              "https://doi.org/10.1007/s00170-018-2686-2"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2019-04", 
        "datePublishedReg": "2019-04-01", 
        "description": "The fluid medium used for soft abrasive flow (SAF) is of low viscosity and high flow velocity. The conventional SAF processing method is widely used due its ability to avoid damage and its adaptability of the workpiece surface. However, the current SAF method also suffers from limitations such as long polishing times and low processing efficiency. To address these issues, this paper proposes a method based on the cavitation effect to assist the soft abrasive flow polishing, termed CSAF. First, the working mechanism of CSAF is introduced through a schematic diagram. In addition, a CSAF fluid mechanic model is configured on the basis of the mixture multiphase model and cavitation model. Then, the distribution of key flow parameters, such as the velocity, dynamic pressure, and turbulent kinetic energy, is obtained and compared through computational fluid dynamic software. Numerical analysis results show that the flow field assisted by the cavitation effects shows better processing performance than SAF. Finally, particle imaging velocity (PIV) observation and experimental processing platforms are established, and extensive experiments are conducted. The processing comparison experiments showed that the abrasive flow assisted by the cavitation effect can lower the workpiece surface roughness to 3.46 nm with a satisfactory surface quality over a shorter time than the SAF method. The numerical analysis results are aligned with the PIV observation and the polishing experiment results. The SAF polishing method based on the cavitation effect significantly increases the polishing efficiency.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s00170-018-2983-9", 
        "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": "101"
          }
        ], 
        "name": "Soft abrasive flow polishing based on the cavitation effect", 
        "pagination": "1865-1878", 
        "productId": [
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s00170-018-2983-9"
            ]
          }, 
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "796bfe1d0c52f8f1ebb46890ead3e12afb4fc8fe76684ef076e15f58eed44567"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1110202936"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s00170-018-2983-9", 
          "https://app.dimensions.ai/details/publication/pub.1110202936"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-15T09:15", 
        "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/v90/0000000376_0000000376/records_56167_00000005.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://link.springer.com/10.1007%2Fs00170-018-2983-9"
      }
    ]
     

    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/s00170-018-2983-9'

    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/s00170-018-2983-9'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00170-018-2983-9'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00170-018-2983-9'


     

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

    209 TRIPLES      21 PREDICATES      64 URIs      19 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s00170-018-2983-9 schema:about anzsrc-for:09
    2 anzsrc-for:0915
    3 schema:author N87d504e056d34986a10803ddbe55d7c0
    4 schema:citation sg:pub.10.1007/s00170-011-3621-y
    5 sg:pub.10.1007/s00170-016-9405-7
    6 sg:pub.10.1007/s00170-017-0400-4
    7 sg:pub.10.1007/s00170-017-1250-9
    8 sg:pub.10.1007/s00170-017-1498-0
    9 sg:pub.10.1007/s00170-018-1993-y
    10 sg:pub.10.1007/s00170-018-2271-8
    11 sg:pub.10.1007/s00170-018-2685-3
    12 sg:pub.10.1007/s00170-018-2686-2
    13 sg:pub.10.1007/s10845-016-1265-3
    14 sg:pub.10.1631/jzus.a1800260
    15 https://doi.org/10.1016/0043-1648(63)90003-6
    16 https://doi.org/10.1016/j.ces.2017.11.037
    17 https://doi.org/10.1016/j.cirp.2013.03.010
    18 https://doi.org/10.1016/j.cirp.2017.04.083
    19 https://doi.org/10.1016/j.ijmachtools.2010.03.007
    20 https://doi.org/10.1016/j.jmatprotec.2008.05.012
    21 https://doi.org/10.1016/j.jmatprotec.2018.04.043
    22 https://doi.org/10.1016/j.optcom.2012.06.015
    23 https://doi.org/10.1016/j.powtec.2016.08.057
    24 https://doi.org/10.1016/j.precisioneng.2015.04.004
    25 https://doi.org/10.1016/j.precisioneng.2018.09.001
    26 https://doi.org/10.1016/j.solmat.2007.03.017
    27 https://doi.org/10.1016/j.stam.2006.12.003
    28 https://doi.org/10.1016/j.triboint.2007.02.013
    29 https://doi.org/10.1016/j.triboint.2016.04.006
    30 https://doi.org/10.1016/j.ultsonch.2017.11.016
    31 https://doi.org/10.1016/j.wear.2017.02.030
    32 https://doi.org/10.1016/j.wear.2018.06.001
    33 https://doi.org/10.1088/0004-637x/806/1/61
    34 https://doi.org/10.1103/physrevlett.109.264502
    35 https://doi.org/10.1109/tii.2017.2773644
    36 https://doi.org/10.1115/1.1524584
    37 https://doi.org/10.1209/epl/i2004-10435-7
    38 https://doi.org/10.1364/ao.37.006771
    39 https://doi.org/10.3154/tvsj.23.107
    40 https://doi.org/10.3901/jme.2012.15.193
    41 schema:datePublished 2019-04
    42 schema:datePublishedReg 2019-04-01
    43 schema:description The fluid medium used for soft abrasive flow (SAF) is of low viscosity and high flow velocity. The conventional SAF processing method is widely used due its ability to avoid damage and its adaptability of the workpiece surface. However, the current SAF method also suffers from limitations such as long polishing times and low processing efficiency. To address these issues, this paper proposes a method based on the cavitation effect to assist the soft abrasive flow polishing, termed CSAF. First, the working mechanism of CSAF is introduced through a schematic diagram. In addition, a CSAF fluid mechanic model is configured on the basis of the mixture multiphase model and cavitation model. Then, the distribution of key flow parameters, such as the velocity, dynamic pressure, and turbulent kinetic energy, is obtained and compared through computational fluid dynamic software. Numerical analysis results show that the flow field assisted by the cavitation effects shows better processing performance than SAF. Finally, particle imaging velocity (PIV) observation and experimental processing platforms are established, and extensive experiments are conducted. The processing comparison experiments showed that the abrasive flow assisted by the cavitation effect can lower the workpiece surface roughness to 3.46 nm with a satisfactory surface quality over a shorter time than the SAF method. The numerical analysis results are aligned with the PIV observation and the polishing experiment results. The SAF polishing method based on the cavitation effect significantly increases the polishing efficiency.
    44 schema:genre research_article
    45 schema:inLanguage en
    46 schema:isAccessibleForFree false
    47 schema:isPartOf Nc7c27a8040fe491691f5553d4b2bc17f
    48 Nd597b3c469b349d09a06586ea6cb7f27
    49 sg:journal.1043671
    50 schema:name Soft abrasive flow polishing based on the cavitation effect
    51 schema:pagination 1865-1878
    52 schema:productId N0cf8f215de7147168e91a96f1d05fd8b
    53 N7ca73cc0c71d4a2da55f21c0e52c4944
    54 Ncc7eb6b437e2406a8055db3d9595c449
    55 schema:sameAs https://app.dimensions.ai/details/publication/pub.1110202936
    56 https://doi.org/10.1007/s00170-018-2983-9
    57 schema:sdDatePublished 2019-04-15T09:15
    58 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    59 schema:sdPublisher Nf13addb59b0349638a9e336c9676a11d
    60 schema:url https://link.springer.com/10.1007%2Fs00170-018-2983-9
    61 sgo:license sg:explorer/license/
    62 sgo:sdDataset articles
    63 rdf:type schema:ScholarlyArticle
    64 N0cf8f215de7147168e91a96f1d05fd8b schema:name readcube_id
    65 schema:value 796bfe1d0c52f8f1ebb46890ead3e12afb4fc8fe76684ef076e15f58eed44567
    66 rdf:type schema:PropertyValue
    67 N157e2bb641df4cfaa684bc02294b2972 schema:affiliation https://www.grid.ac/institutes/grid.469325.f
    68 schema:familyName Zhao
    69 schema:givenName Jun
    70 rdf:type schema:Person
    71 N3babc621a4204a14b5837aabdd252410 schema:affiliation https://www.grid.ac/institutes/grid.469325.f
    72 schema:familyName Cao
    73 schema:givenName Huiqiang
    74 rdf:type schema:Person
    75 N3d965d7351894627a18065582664897d rdf:first N157e2bb641df4cfaa684bc02294b2972
    76 rdf:rest Naea7621c28a543edae4cb7aa838ac728
    77 N3e580ee155cd43eabc6cc91414abcdf6 rdf:first N3babc621a4204a14b5837aabdd252410
    78 rdf:rest N3d965d7351894627a18065582664897d
    79 N7ca73cc0c71d4a2da55f21c0e52c4944 schema:name dimensions_id
    80 schema:value pub.1110202936
    81 rdf:type schema:PropertyValue
    82 N87d504e056d34986a10803ddbe55d7c0 rdf:first sg:person.01046153163.59
    83 rdf:rest N3e580ee155cd43eabc6cc91414abcdf6
    84 N89c62f1b1e774d05a399f83565874eec schema:affiliation https://www.grid.ac/institutes/grid.469325.f
    85 schema:familyName Pan
    86 schema:givenName Ye
    87 rdf:type schema:Person
    88 N988a62cf5f5d4a809399e865e8dbfc1b rdf:first sg:person.015210475713.12
    89 rdf:rest rdf:nil
    90 Naea7621c28a543edae4cb7aa838ac728 rdf:first N89c62f1b1e774d05a399f83565874eec
    91 rdf:rest N988a62cf5f5d4a809399e865e8dbfc1b
    92 Nc7c27a8040fe491691f5553d4b2bc17f schema:volumeNumber 101
    93 rdf:type schema:PublicationVolume
    94 Ncc7eb6b437e2406a8055db3d9595c449 schema:name doi
    95 schema:value 10.1007/s00170-018-2983-9
    96 rdf:type schema:PropertyValue
    97 Nd597b3c469b349d09a06586ea6cb7f27 schema:issueNumber 5-8
    98 rdf:type schema:PublicationIssue
    99 Nf13addb59b0349638a9e336c9676a11d schema:name Springer Nature - SN SciGraph project
    100 rdf:type schema:Organization
    101 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
    102 schema:name Engineering
    103 rdf:type schema:DefinedTerm
    104 anzsrc-for:0915 schema:inDefinedTermSet anzsrc-for:
    105 schema:name Interdisciplinary Engineering
    106 rdf:type schema:DefinedTerm
    107 sg:journal.1043671 schema:issn 0268-3768
    108 1433-3015
    109 schema:name The International Journal of Advanced Manufacturing Technology
    110 rdf:type schema:Periodical
    111 sg:person.01046153163.59 schema:affiliation https://www.grid.ac/institutes/grid.469325.f
    112 schema:familyName Ji
    113 schema:givenName Shiming
    114 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01046153163.59
    115 rdf:type schema:Person
    116 sg:person.015210475713.12 schema:affiliation https://www.grid.ac/institutes/grid.469325.f
    117 schema:familyName Jiang
    118 schema:givenName Enyong
    119 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015210475713.12
    120 rdf:type schema:Person
    121 sg:pub.10.1007/s00170-011-3621-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1052942639
    122 https://doi.org/10.1007/s00170-011-3621-y
    123 rdf:type schema:CreativeWork
    124 sg:pub.10.1007/s00170-016-9405-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032989241
    125 https://doi.org/10.1007/s00170-016-9405-7
    126 rdf:type schema:CreativeWork
    127 sg:pub.10.1007/s00170-017-0400-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085050040
    128 https://doi.org/10.1007/s00170-017-0400-4
    129 rdf:type schema:CreativeWork
    130 sg:pub.10.1007/s00170-017-1250-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092579135
    131 https://doi.org/10.1007/s00170-017-1250-9
    132 rdf:type schema:CreativeWork
    133 sg:pub.10.1007/s00170-017-1498-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1100405748
    134 https://doi.org/10.1007/s00170-017-1498-0
    135 rdf:type schema:CreativeWork
    136 sg:pub.10.1007/s00170-018-1993-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1103687202
    137 https://doi.org/10.1007/s00170-018-1993-y
    138 rdf:type schema:CreativeWork
    139 sg:pub.10.1007/s00170-018-2271-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1104496021
    140 https://doi.org/10.1007/s00170-018-2271-8
    141 rdf:type schema:CreativeWork
    142 sg:pub.10.1007/s00170-018-2685-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1107186981
    143 https://doi.org/10.1007/s00170-018-2685-3
    144 rdf:type schema:CreativeWork
    145 sg:pub.10.1007/s00170-018-2686-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1107229126
    146 https://doi.org/10.1007/s00170-018-2686-2
    147 rdf:type schema:CreativeWork
    148 sg:pub.10.1007/s10845-016-1265-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022477621
    149 https://doi.org/10.1007/s10845-016-1265-3
    150 rdf:type schema:CreativeWork
    151 sg:pub.10.1631/jzus.a1800260 schema:sameAs https://app.dimensions.ai/details/publication/pub.1107135863
    152 https://doi.org/10.1631/jzus.a1800260
    153 rdf:type schema:CreativeWork
    154 https://doi.org/10.1016/0043-1648(63)90003-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016823502
    155 rdf:type schema:CreativeWork
    156 https://doi.org/10.1016/j.ces.2017.11.037 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092957074
    157 rdf:type schema:CreativeWork
    158 https://doi.org/10.1016/j.cirp.2013.03.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002499062
    159 rdf:type schema:CreativeWork
    160 https://doi.org/10.1016/j.cirp.2017.04.083 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085183219
    161 rdf:type schema:CreativeWork
    162 https://doi.org/10.1016/j.ijmachtools.2010.03.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040541875
    163 rdf:type schema:CreativeWork
    164 https://doi.org/10.1016/j.jmatprotec.2008.05.012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043049606
    165 rdf:type schema:CreativeWork
    166 https://doi.org/10.1016/j.jmatprotec.2018.04.043 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103688830
    167 rdf:type schema:CreativeWork
    168 https://doi.org/10.1016/j.optcom.2012.06.015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015739134
    169 rdf:type schema:CreativeWork
    170 https://doi.org/10.1016/j.powtec.2016.08.057 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050689781
    171 rdf:type schema:CreativeWork
    172 https://doi.org/10.1016/j.precisioneng.2015.04.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024520903
    173 rdf:type schema:CreativeWork
    174 https://doi.org/10.1016/j.precisioneng.2018.09.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1106858273
    175 rdf:type schema:CreativeWork
    176 https://doi.org/10.1016/j.solmat.2007.03.017 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035847494
    177 rdf:type schema:CreativeWork
    178 https://doi.org/10.1016/j.stam.2006.12.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008645758
    179 rdf:type schema:CreativeWork
    180 https://doi.org/10.1016/j.triboint.2007.02.013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012270426
    181 rdf:type schema:CreativeWork
    182 https://doi.org/10.1016/j.triboint.2016.04.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024768310
    183 rdf:type schema:CreativeWork
    184 https://doi.org/10.1016/j.ultsonch.2017.11.016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092668454
    185 rdf:type schema:CreativeWork
    186 https://doi.org/10.1016/j.wear.2017.02.030 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083868301
    187 rdf:type schema:CreativeWork
    188 https://doi.org/10.1016/j.wear.2018.06.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1104422939
    189 rdf:type schema:CreativeWork
    190 https://doi.org/10.1088/0004-637x/806/1/61 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058945517
    191 rdf:type schema:CreativeWork
    192 https://doi.org/10.1103/physrevlett.109.264502 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060760753
    193 rdf:type schema:CreativeWork
    194 https://doi.org/10.1109/tii.2017.2773644 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092718339
    195 rdf:type schema:CreativeWork
    196 https://doi.org/10.1115/1.1524584 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062071572
    197 rdf:type schema:CreativeWork
    198 https://doi.org/10.1209/epl/i2004-10435-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064237217
    199 rdf:type schema:CreativeWork
    200 https://doi.org/10.1364/ao.37.006771 schema:sameAs https://app.dimensions.ai/details/publication/pub.1065113687
    201 rdf:type schema:CreativeWork
    202 https://doi.org/10.3154/tvsj.23.107 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014567397
    203 rdf:type schema:CreativeWork
    204 https://doi.org/10.3901/jme.2012.15.193 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071553406
    205 rdf:type schema:CreativeWork
    206 https://www.grid.ac/institutes/grid.469325.f schema:alternateName Zhejiang University of Technology
    207 schema:name Key Laboratory of Special Purpose Equipment and Advanced Processing Technology of the Ministry of Education, Zhejiang University of Technology, 310032, Hangzhou, China
    208 Yiwu Academy of Science and Technology, Zhejiang University of Technology, 322001, Yiwu, China
    209 rdf:type schema:Organization
     




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


    ...