Predicting surface deformation during mechanical attrition of metallic alloys View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-12

AUTHORS

Shan Cecilia Cao, Xiaochun Zhang, Jian Lu, Yongli Wang, San-Qiang Shi, Robert O. Ritchie

ABSTRACT

Extensive efforts have been devoted in both the engineering and scientific domains to seek new designs and processing techniques capable of making stronger and tougher materials. One such method for enhancing such damage-tolerance in metallic alloys is a surface nano-crystallization technology that involves the use of hundreds of small hard balls which are vibrated using high-power ultrasound so that they impact onto the surface of a material at high speed (termed Surface Mechanical Attrition Treatment or SMAT). However, few studies have been devoted to the precise underlying mechanical mechanisms associated with this technology and the effect of processing parameters. As SMAT is dynamic plastic deformation process, here we use random impact deformation as a means to investigate the relationship between impact deformation and the parameters involved in the processing, specifically ball size, impact velocity, ball density and kinetic energy. Using analytical and numerical solutions, we examine the size of the indents and the depths of the associated plastic zones induced by random impacts, with results verified by experiment in austenitic stainless steels. In addition, global random impact and local impact frequency models are developed to analyze the statistical characteristics of random impact coverage, together with a description of the effect of random multiple impacts, which are more reflective of SMAT. We believe that these models will serve as a necessary foundation for further, and more energy-efficient, development of such surface nano-crystalline processing technologies for the strengthening of metallic materials. More... »

PAGES

36

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41524-019-0171-6

DOI

http://dx.doi.org/10.1038/s41524-019-0171-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112765636


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0912", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Materials Engineering", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/09", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Engineering", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of California, Berkeley", 
          "id": "https://www.grid.ac/institutes/grid.47840.3f", 
          "name": [
            "Department of Materials Science & Engineering, University of California, Berkeley, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Cao", 
        "givenName": "Shan Cecilia", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Shanghai Institute of Applied Physics", 
          "id": "https://www.grid.ac/institutes/grid.450275.1", 
          "name": [
            "Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhang", 
        "givenName": "Xiaochun", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "City University of Hong Kong", 
          "id": "https://www.grid.ac/institutes/grid.35030.35", 
          "name": [
            "Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lu", 
        "givenName": "Jian", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Hong Kong Polytechnic University", 
          "id": "https://www.grid.ac/institutes/grid.16890.36", 
          "name": [
            "Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, China", 
            "Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wang", 
        "givenName": "Yongli", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Hong Kong Polytechnic University", 
          "id": "https://www.grid.ac/institutes/grid.16890.36", 
          "name": [
            "Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Shi", 
        "givenName": "San-Qiang", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Lawrence Berkeley National Laboratory", 
          "id": "https://www.grid.ac/institutes/grid.184769.5", 
          "name": [
            "Department of Materials Science & Engineering, University of California, Berkeley, USA", 
            "Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ritchie", 
        "givenName": "Robert O.", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/0013-7944(85)90052-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000646465"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0013-7944(85)90052-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000646465"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1179/026708399101505707", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001118857"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.actamat.2006.07.013", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003325913"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nmat3115", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004166204", 
          "https://doi.org/10.1038/nmat3115"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.calphad.2007.11.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010120660"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ncomms10546", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013120578", 
          "https://doi.org/10.1038/ncomms10546"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0924-0136(00)00893-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013806838"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.surfcoat.2016.11.095", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024851390"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.surfcoat.2005.11.090", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027053648"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.actamat.2004.08.031", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028747799"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/npjcompumats.2015.16", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029487943", 
          "https://doi.org/10.1038/npjcompumats.2015.16"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nmat4089", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029890107", 
          "https://doi.org/10.1038/nmat4089"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1359-6454(02)00594-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034424571"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1359-6454(02)00594-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034424571"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.apsusc.2015.05.093", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037070662"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.scriptamat.2004.10.021", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037242235"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0167-6636(94)00036-g", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041520968"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jmatprotec.2005.02.139", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045501580"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1359-6454(02)00310-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049394873"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.scriptamat.2005.01.023", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050707823"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1115/1.2772338", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062080906"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.1254581", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062470018"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/s41524-017-0010-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083914537", 
          "https://doi.org/10.1038/s41524-017-0010-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature21691", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084537608", 
          "https://doi.org/10.1038/nature21691"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature21691", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084537608", 
          "https://doi.org/10.1038/nature21691"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/s41598-017-01458-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085113780", 
          "https://doi.org/10.1038/s41598-017-01458-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/s41524-017-0023-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085537693", 
          "https://doi.org/10.1038/s41524-017-0023-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/s41524-018-0062-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1100522964", 
          "https://doi.org/10.1038/s41524-018-0062-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.vacuum.2018.01.027", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1100726738"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/s41598-018-23358-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101701008", 
          "https://doi.org/10.1038/s41598-018-23358-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/s41598-018-23358-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101701008", 
          "https://doi.org/10.1038/s41598-018-23358-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.scriptamat.2018.06.012", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1104603850"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-12", 
    "datePublishedReg": "2019-12-01", 
    "description": "Extensive efforts have been devoted in both the engineering and scientific domains to seek new designs and processing techniques capable of making stronger and tougher materials. One such method for enhancing such damage-tolerance in metallic alloys is a surface nano-crystallization technology that involves the use of hundreds of small hard balls which are vibrated using high-power ultrasound so that they impact onto the surface of a material at high speed (termed Surface Mechanical Attrition Treatment or SMAT). However, few studies have been devoted to the precise underlying mechanical mechanisms associated with this technology and the effect of processing parameters. As SMAT is dynamic plastic deformation process, here we use random impact deformation as a means to investigate the relationship between impact deformation and the parameters involved in the processing, specifically ball size, impact velocity, ball density and kinetic energy. Using analytical and numerical solutions, we examine the size of the indents and the depths of the associated plastic zones induced by random impacts, with results verified by experiment in austenitic stainless steels. In addition, global random impact and local impact frequency models are developed to analyze the statistical characteristics of random impact coverage, together with a description of the effect of random multiple impacts, which are more reflective of SMAT. We believe that these models will serve as a necessary foundation for further, and more energy-efficient, development of such surface nano-crystalline processing technologies for the strengthening of metallic materials.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1038/s41524-019-0171-6", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1285194", 
        "issn": [
          "2057-3960"
        ], 
        "name": "npj Computational Materials", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "5"
      }
    ], 
    "name": "Predicting surface deformation during mechanical attrition of metallic alloys", 
    "pagination": "36", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "adab276a0cfacc2c7b6718e04c7c6e2b93ac1df9c0da4615ebf905f524456f47"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1038/s41524-019-0171-6"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1112765636"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1038/s41524-019-0171-6", 
      "https://app.dimensions.ai/details/publication/pub.1112765636"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T11:53", 
    "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/v90/0000000359_0000000359/records_29197_00000003.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://www.nature.com/articles/s41524-019-0171-6"
  }
]
 

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.10.1038/s41524-019-0171-6'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41524-019-0171-6'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41524-019-0171-6'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41524-019-0171-6'


 

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

200 TRIPLES      21 PREDICATES      56 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1038/s41524-019-0171-6 schema:about anzsrc-for:09
2 anzsrc-for:0912
3 schema:author N084d79f1ddf84a359735dd4ffc6c745e
4 schema:citation sg:pub.10.1038/nature21691
5 sg:pub.10.1038/ncomms10546
6 sg:pub.10.1038/nmat3115
7 sg:pub.10.1038/nmat4089
8 sg:pub.10.1038/npjcompumats.2015.16
9 sg:pub.10.1038/s41524-017-0010-6
10 sg:pub.10.1038/s41524-017-0023-1
11 sg:pub.10.1038/s41524-018-0062-2
12 sg:pub.10.1038/s41598-017-01458-0
13 sg:pub.10.1038/s41598-018-23358-7
14 https://doi.org/10.1016/0013-7944(85)90052-9
15 https://doi.org/10.1016/0167-6636(94)00036-g
16 https://doi.org/10.1016/j.actamat.2004.08.031
17 https://doi.org/10.1016/j.actamat.2006.07.013
18 https://doi.org/10.1016/j.apsusc.2015.05.093
19 https://doi.org/10.1016/j.calphad.2007.11.003
20 https://doi.org/10.1016/j.jmatprotec.2005.02.139
21 https://doi.org/10.1016/j.scriptamat.2004.10.021
22 https://doi.org/10.1016/j.scriptamat.2005.01.023
23 https://doi.org/10.1016/j.scriptamat.2018.06.012
24 https://doi.org/10.1016/j.surfcoat.2005.11.090
25 https://doi.org/10.1016/j.surfcoat.2016.11.095
26 https://doi.org/10.1016/j.vacuum.2018.01.027
27 https://doi.org/10.1016/s0924-0136(00)00893-1
28 https://doi.org/10.1016/s1359-6454(02)00310-5
29 https://doi.org/10.1016/s1359-6454(02)00594-3
30 https://doi.org/10.1115/1.2772338
31 https://doi.org/10.1126/science.1254581
32 https://doi.org/10.1179/026708399101505707
33 schema:datePublished 2019-12
34 schema:datePublishedReg 2019-12-01
35 schema:description Extensive efforts have been devoted in both the engineering and scientific domains to seek new designs and processing techniques capable of making stronger and tougher materials. One such method for enhancing such damage-tolerance in metallic alloys is a surface nano-crystallization technology that involves the use of hundreds of small hard balls which are vibrated using high-power ultrasound so that they impact onto the surface of a material at high speed (termed Surface Mechanical Attrition Treatment or SMAT). However, few studies have been devoted to the precise underlying mechanical mechanisms associated with this technology and the effect of processing parameters. As SMAT is dynamic plastic deformation process, here we use random impact deformation as a means to investigate the relationship between impact deformation and the parameters involved in the processing, specifically ball size, impact velocity, ball density and kinetic energy. Using analytical and numerical solutions, we examine the size of the indents and the depths of the associated plastic zones induced by random impacts, with results verified by experiment in austenitic stainless steels. In addition, global random impact and local impact frequency models are developed to analyze the statistical characteristics of random impact coverage, together with a description of the effect of random multiple impacts, which are more reflective of SMAT. We believe that these models will serve as a necessary foundation for further, and more energy-efficient, development of such surface nano-crystalline processing technologies for the strengthening of metallic materials.
36 schema:genre research_article
37 schema:inLanguage en
38 schema:isAccessibleForFree false
39 schema:isPartOf N8bd6d109b1f844308951af5032770de7
40 Ncb106abf198847d991dce327fbbc53ac
41 sg:journal.1285194
42 schema:name Predicting surface deformation during mechanical attrition of metallic alloys
43 schema:pagination 36
44 schema:productId N32b07482ce2940bf971ae9a2f6859ef9
45 N6749ef402cfd4d91b9bbe6bd4948133c
46 Nba82c0329f7d4718a1e8d522b9973d1a
47 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112765636
48 https://doi.org/10.1038/s41524-019-0171-6
49 schema:sdDatePublished 2019-04-11T11:53
50 schema:sdLicense https://scigraph.springernature.com/explorer/license/
51 schema:sdPublisher N110c8641d2cf44b38d7bf71fc3b04f0f
52 schema:url https://www.nature.com/articles/s41524-019-0171-6
53 sgo:license sg:explorer/license/
54 sgo:sdDataset articles
55 rdf:type schema:ScholarlyArticle
56 N084d79f1ddf84a359735dd4ffc6c745e rdf:first N100e39340cf147d78ba2e37489c29253
57 rdf:rest Nee1f0147b64f493db4d1e590c78341af
58 N100e39340cf147d78ba2e37489c29253 schema:affiliation https://www.grid.ac/institutes/grid.47840.3f
59 schema:familyName Cao
60 schema:givenName Shan Cecilia
61 rdf:type schema:Person
62 N110c8641d2cf44b38d7bf71fc3b04f0f schema:name Springer Nature - SN SciGraph project
63 rdf:type schema:Organization
64 N1dfe2cb4b1c2440a92cc4adcc6a0dbc7 schema:affiliation https://www.grid.ac/institutes/grid.184769.5
65 schema:familyName Ritchie
66 schema:givenName Robert O.
67 rdf:type schema:Person
68 N32b07482ce2940bf971ae9a2f6859ef9 schema:name readcube_id
69 schema:value adab276a0cfacc2c7b6718e04c7c6e2b93ac1df9c0da4615ebf905f524456f47
70 rdf:type schema:PropertyValue
71 N4d07655a997f4fcfae18e35e35ebcd7b rdf:first N531c59457a28455a89635418e082fca4
72 rdf:rest Ne6d3f906a71542e1883c58601d21a908
73 N531c59457a28455a89635418e082fca4 schema:affiliation https://www.grid.ac/institutes/grid.16890.36
74 schema:familyName Shi
75 schema:givenName San-Qiang
76 rdf:type schema:Person
77 N6749ef402cfd4d91b9bbe6bd4948133c schema:name doi
78 schema:value 10.1038/s41524-019-0171-6
79 rdf:type schema:PropertyValue
80 N71a2bd8223ae458c89f7fb3d68130c46 schema:affiliation https://www.grid.ac/institutes/grid.16890.36
81 schema:familyName Wang
82 schema:givenName Yongli
83 rdf:type schema:Person
84 N8634ce8458d1408283fb52a869723ca0 rdf:first N71a2bd8223ae458c89f7fb3d68130c46
85 rdf:rest N4d07655a997f4fcfae18e35e35ebcd7b
86 N8bd6d109b1f844308951af5032770de7 schema:volumeNumber 5
87 rdf:type schema:PublicationVolume
88 Na065aaad410e4d95b5cc702df3a2b7a3 schema:affiliation https://www.grid.ac/institutes/grid.35030.35
89 schema:familyName Lu
90 schema:givenName Jian
91 rdf:type schema:Person
92 Nba82c0329f7d4718a1e8d522b9973d1a schema:name dimensions_id
93 schema:value pub.1112765636
94 rdf:type schema:PropertyValue
95 Ncb106abf198847d991dce327fbbc53ac schema:issueNumber 1
96 rdf:type schema:PublicationIssue
97 Nce3949216da644959178522f1d3fced6 rdf:first Na065aaad410e4d95b5cc702df3a2b7a3
98 rdf:rest N8634ce8458d1408283fb52a869723ca0
99 Ne6d3f906a71542e1883c58601d21a908 rdf:first N1dfe2cb4b1c2440a92cc4adcc6a0dbc7
100 rdf:rest rdf:nil
101 Neba885496406406c8b405d54e3920ab6 schema:affiliation https://www.grid.ac/institutes/grid.450275.1
102 schema:familyName Zhang
103 schema:givenName Xiaochun
104 rdf:type schema:Person
105 Nee1f0147b64f493db4d1e590c78341af rdf:first Neba885496406406c8b405d54e3920ab6
106 rdf:rest Nce3949216da644959178522f1d3fced6
107 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
108 schema:name Engineering
109 rdf:type schema:DefinedTerm
110 anzsrc-for:0912 schema:inDefinedTermSet anzsrc-for:
111 schema:name Materials Engineering
112 rdf:type schema:DefinedTerm
113 sg:journal.1285194 schema:issn 2057-3960
114 schema:name npj Computational Materials
115 rdf:type schema:Periodical
116 sg:pub.10.1038/nature21691 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084537608
117 https://doi.org/10.1038/nature21691
118 rdf:type schema:CreativeWork
119 sg:pub.10.1038/ncomms10546 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013120578
120 https://doi.org/10.1038/ncomms10546
121 rdf:type schema:CreativeWork
122 sg:pub.10.1038/nmat3115 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004166204
123 https://doi.org/10.1038/nmat3115
124 rdf:type schema:CreativeWork
125 sg:pub.10.1038/nmat4089 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029890107
126 https://doi.org/10.1038/nmat4089
127 rdf:type schema:CreativeWork
128 sg:pub.10.1038/npjcompumats.2015.16 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029487943
129 https://doi.org/10.1038/npjcompumats.2015.16
130 rdf:type schema:CreativeWork
131 sg:pub.10.1038/s41524-017-0010-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083914537
132 https://doi.org/10.1038/s41524-017-0010-6
133 rdf:type schema:CreativeWork
134 sg:pub.10.1038/s41524-017-0023-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085537693
135 https://doi.org/10.1038/s41524-017-0023-1
136 rdf:type schema:CreativeWork
137 sg:pub.10.1038/s41524-018-0062-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1100522964
138 https://doi.org/10.1038/s41524-018-0062-2
139 rdf:type schema:CreativeWork
140 sg:pub.10.1038/s41598-017-01458-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085113780
141 https://doi.org/10.1038/s41598-017-01458-0
142 rdf:type schema:CreativeWork
143 sg:pub.10.1038/s41598-018-23358-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101701008
144 https://doi.org/10.1038/s41598-018-23358-7
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1016/0013-7944(85)90052-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000646465
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1016/0167-6636(94)00036-g schema:sameAs https://app.dimensions.ai/details/publication/pub.1041520968
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1016/j.actamat.2004.08.031 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028747799
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1016/j.actamat.2006.07.013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003325913
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1016/j.apsusc.2015.05.093 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037070662
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1016/j.calphad.2007.11.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010120660
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1016/j.jmatprotec.2005.02.139 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045501580
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1016/j.scriptamat.2004.10.021 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037242235
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1016/j.scriptamat.2005.01.023 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050707823
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1016/j.scriptamat.2018.06.012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1104603850
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1016/j.surfcoat.2005.11.090 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027053648
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1016/j.surfcoat.2016.11.095 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024851390
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1016/j.vacuum.2018.01.027 schema:sameAs https://app.dimensions.ai/details/publication/pub.1100726738
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1016/s0924-0136(00)00893-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013806838
173 rdf:type schema:CreativeWork
174 https://doi.org/10.1016/s1359-6454(02)00310-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049394873
175 rdf:type schema:CreativeWork
176 https://doi.org/10.1016/s1359-6454(02)00594-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034424571
177 rdf:type schema:CreativeWork
178 https://doi.org/10.1115/1.2772338 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062080906
179 rdf:type schema:CreativeWork
180 https://doi.org/10.1126/science.1254581 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062470018
181 rdf:type schema:CreativeWork
182 https://doi.org/10.1179/026708399101505707 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001118857
183 rdf:type schema:CreativeWork
184 https://www.grid.ac/institutes/grid.16890.36 schema:alternateName Hong Kong Polytechnic University
185 schema:name Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, China
186 Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
187 rdf:type schema:Organization
188 https://www.grid.ac/institutes/grid.184769.5 schema:alternateName Lawrence Berkeley National Laboratory
189 schema:name Department of Materials Science & Engineering, University of California, Berkeley, USA
190 Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, USA
191 rdf:type schema:Organization
192 https://www.grid.ac/institutes/grid.35030.35 schema:alternateName City University of Hong Kong
193 schema:name Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, China
194 rdf:type schema:Organization
195 https://www.grid.ac/institutes/grid.450275.1 schema:alternateName Shanghai Institute of Applied Physics
196 schema:name Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, China
197 rdf:type schema:Organization
198 https://www.grid.ac/institutes/grid.47840.3f schema:alternateName University of California, Berkeley
199 schema:name Department of Materials Science & Engineering, University of California, Berkeley, USA
200 rdf:type schema:Organization
 




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


...