Analytical evaluation of defect generation for selective laser melting of metals View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-04-02

AUTHORS

Patcharapit Promoppatum, Shi-Chune Yao

ABSTRACT

Defects in selective laser melting parts are among major reasons that hinder the wider adoption of a laser powder bed–based manufacturing. Experiments and numerical modeling are often proceeded to gain a better understanding of defect generation. However, these approaches demand extensive computational and experimental resources until comprehensive understanding of a process-defect relationship can be developed. To address this challenge, the present study utilized an analytical approach to illustrate the relationship between processing parameters and common defects such as lack of fusion, keyholing, and balling effects. The primary aim was to serve as a quick and efficient tool to identify processing conditions leading to undesirable outcome. The analysis began by predicting melt pool dimensions, in which these dimensions were successively used for the defect determination. Ultimately, criteria for defect initiation were combined and presented in a single processing window, where available experimental data from various materials and processes were compared throughout the study to ensure the validity of the proposed approach. More... »

PAGES

1-14

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00170-019-03500-z

DOI

http://dx.doi.org/10.1007/s00170-019-03500-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1113199625


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/2008/0910", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Manufacturing 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": "King Mongkut's University of Technology Thonburi", 
          "id": "https://www.grid.ac/institutes/grid.412151.2", 
          "name": [
            "Department of Mechanical Engineering, Faculty of Engineering, King Mongkut\u2019s University of Technology Thonburi, 126 Pracha Uthit Rd., Bang Mod, Thung Khru, 10140, Bangkok, Thailand"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Promoppatum", 
        "givenName": "Patcharapit", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Carnegie Mellon University", 
          "id": "https://www.grid.ac/institutes/grid.147455.6", 
          "name": [
            "Department of Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Ave., 15213, Pittsburgh, PA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yao", 
        "givenName": "Shi-Chune", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s00170-011-3776-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003054026", 
          "https://doi.org/10.1007/s00170-011-3776-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ijmachtools.2011.02.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005473588"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.matdes.2014.09.044", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010234395"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jmatprotec.2014.06.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011723383"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jmatprotec.2014.05.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015630083"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.addma.2016.12.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017789106"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.addma.2014.08.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018024618"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.matdes.2016.10.037", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021097737"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.matdes.2016.10.037", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021097737"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.matdes.2016.10.037", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021097737"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.matdes.2014.07.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021849271"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1022323959", 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-19831-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022323959", 
          "https://doi.org/10.1007/978-3-642-19831-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-19831-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022323959", 
          "https://doi.org/10.1007/978-3-642-19831-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00170-011-3566-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023499442", 
          "https://doi.org/10.1007/s00170-011-3566-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.optlastec.2014.07.021", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024046954"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jmatprotec.2010.05.010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027818721"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11665-014-0958-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032374549", 
          "https://doi.org/10.1007/s11665-014-0958-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0022-3727/44/44/445401", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033667930"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.powtec.2017.01.030", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034365791"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.addma.2016.03.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038651443"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11837-001-0067-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045226426", 
          "https://doi.org/10.1007/s11837-001-0067-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.actamat.2016.12.062", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048146924"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.actamat.2016.12.062", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048146924"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.matdes.2011.09.051", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048332807"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.prosdent.2014.06.017", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048422595"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.msea.2009.02.019", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051171143"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jmatprotec.2016.10.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052737361"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11837-016-2234-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052862781", 
          "https://doi.org/10.1007/s11837-016-2234-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11837-016-2234-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052862781", 
          "https://doi.org/10.1007/s11837-016-2234-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11665-017-2768-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085981757", 
          "https://doi.org/10.1007/s11665-017-2768-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11665-017-2768-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085981757", 
          "https://doi.org/10.1007/s11665-017-2768-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1089/pho.2017.4311", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092198093"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jmatprotec.2017.11.032", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092916735"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.eng.2017.05.023", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092997704"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00170-017-1489-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1100163211", 
          "https://doi.org/10.1007/s00170-017-1489-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s40964-018-0039-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1100559565", 
          "https://doi.org/10.1007/s40964-018-0039-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ijmachtools.2018.01.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1100753459"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jmatprotec.2018.02.034", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101342509"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jmapro.2018.04.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103217269"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jmapro.2018.04.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103217269"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jmapro.2018.04.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103217269"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.camwa.2018.06.029", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1105250535"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.msea.2018.08.037", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1106132871"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.msea.2018.08.037", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1106132871"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.addma.2018.10.017", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1107491262"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.addma.2018.10.017", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1107491262"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-04-02", 
    "datePublishedReg": "2019-04-02", 
    "description": "Defects in selective laser melting parts are among major reasons that hinder the wider adoption of a laser powder bed\u2013based manufacturing. Experiments and numerical modeling are often proceeded to gain a better understanding of defect generation. However, these approaches demand extensive computational and experimental resources until comprehensive understanding of a process-defect relationship can be developed. To address this challenge, the present study utilized an analytical approach to illustrate the relationship between processing parameters and common defects such as lack of fusion, keyholing, and balling effects. The primary aim was to serve as a quick and efficient tool to identify processing conditions leading to undesirable outcome. The analysis began by predicting melt pool dimensions, in which these dimensions were successively used for the defect determination. Ultimately, criteria for defect initiation were combined and presented in a single processing window, where available experimental data from various materials and processes were compared throughout the study to ensure the validity of the proposed approach.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s00170-019-03500-z", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1043671", 
        "issn": [
          "0268-3768", 
          "1433-3015"
        ], 
        "name": "The International Journal of Advanced Manufacturing Technology", 
        "type": "Periodical"
      }
    ], 
    "name": "Analytical evaluation of defect generation for selective laser melting of metals", 
    "pagination": "1-14", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00170-019-03500-z"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "90f01f3cd45d8a6e6d3bc24ebe64ad7fe432fcc2c13bda4ee787cc14ce37190b"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1113199625"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00170-019-03500-z", 
      "https://app.dimensions.ai/details/publication/pub.1113199625"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-15T09:27", 
    "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_56193_00000006.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs00170-019-03500-z"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s00170-019-03500-z'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s00170-019-03500-z'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00170-019-03500-z'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00170-019-03500-z'


 

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

182 TRIPLES      21 PREDICATES      61 URIs      16 LITERALS      5 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00170-019-03500-z schema:about anzsrc-for:09
2 anzsrc-for:0910
3 schema:author Nd6bf001c5b2b43a7ac1a9ce5097428d4
4 schema:citation sg:pub.10.1007/978-3-642-19831-1
5 sg:pub.10.1007/s00170-011-3566-1
6 sg:pub.10.1007/s00170-011-3776-6
7 sg:pub.10.1007/s00170-017-1489-1
8 sg:pub.10.1007/s11665-014-0958-z
9 sg:pub.10.1007/s11665-017-2768-6
10 sg:pub.10.1007/s11837-001-0067-y
11 sg:pub.10.1007/s11837-016-2234-1
12 sg:pub.10.1007/s40964-018-0039-1
13 https://app.dimensions.ai/details/publication/pub.1022323959
14 https://doi.org/10.1016/j.actamat.2016.12.062
15 https://doi.org/10.1016/j.addma.2014.08.002
16 https://doi.org/10.1016/j.addma.2016.03.006
17 https://doi.org/10.1016/j.addma.2016.12.001
18 https://doi.org/10.1016/j.addma.2018.10.017
19 https://doi.org/10.1016/j.camwa.2018.06.029
20 https://doi.org/10.1016/j.eng.2017.05.023
21 https://doi.org/10.1016/j.ijmachtools.2011.02.003
22 https://doi.org/10.1016/j.ijmachtools.2018.01.003
23 https://doi.org/10.1016/j.jmapro.2018.04.002
24 https://doi.org/10.1016/j.jmatprotec.2010.05.010
25 https://doi.org/10.1016/j.jmatprotec.2014.05.002
26 https://doi.org/10.1016/j.jmatprotec.2014.06.005
27 https://doi.org/10.1016/j.jmatprotec.2016.10.005
28 https://doi.org/10.1016/j.jmatprotec.2017.11.032
29 https://doi.org/10.1016/j.jmatprotec.2018.02.034
30 https://doi.org/10.1016/j.matdes.2011.09.051
31 https://doi.org/10.1016/j.matdes.2014.07.006
32 https://doi.org/10.1016/j.matdes.2014.09.044
33 https://doi.org/10.1016/j.matdes.2016.10.037
34 https://doi.org/10.1016/j.msea.2009.02.019
35 https://doi.org/10.1016/j.msea.2018.08.037
36 https://doi.org/10.1016/j.optlastec.2014.07.021
37 https://doi.org/10.1016/j.powtec.2017.01.030
38 https://doi.org/10.1016/j.prosdent.2014.06.017
39 https://doi.org/10.1088/0022-3727/44/44/445401
40 https://doi.org/10.1089/pho.2017.4311
41 schema:datePublished 2019-04-02
42 schema:datePublishedReg 2019-04-02
43 schema:description Defects in selective laser melting parts are among major reasons that hinder the wider adoption of a laser powder bed–based manufacturing. Experiments and numerical modeling are often proceeded to gain a better understanding of defect generation. However, these approaches demand extensive computational and experimental resources until comprehensive understanding of a process-defect relationship can be developed. To address this challenge, the present study utilized an analytical approach to illustrate the relationship between processing parameters and common defects such as lack of fusion, keyholing, and balling effects. The primary aim was to serve as a quick and efficient tool to identify processing conditions leading to undesirable outcome. The analysis began by predicting melt pool dimensions, in which these dimensions were successively used for the defect determination. Ultimately, criteria for defect initiation were combined and presented in a single processing window, where available experimental data from various materials and processes were compared throughout the study to ensure the validity of the proposed approach.
44 schema:genre research_article
45 schema:inLanguage en
46 schema:isAccessibleForFree false
47 schema:isPartOf sg:journal.1043671
48 schema:name Analytical evaluation of defect generation for selective laser melting of metals
49 schema:pagination 1-14
50 schema:productId N1252196ee4cc4ce4a7c90c3c49587980
51 N15da80b4c3d441f5aaf182205be0fa5e
52 N7201f510978a477eb7418fb76581d0ee
53 schema:sameAs https://app.dimensions.ai/details/publication/pub.1113199625
54 https://doi.org/10.1007/s00170-019-03500-z
55 schema:sdDatePublished 2019-04-15T09:27
56 schema:sdLicense https://scigraph.springernature.com/explorer/license/
57 schema:sdPublisher N85489bdd247b4d3ab133ebc234a5e6d7
58 schema:url https://link.springer.com/10.1007%2Fs00170-019-03500-z
59 sgo:license sg:explorer/license/
60 sgo:sdDataset articles
61 rdf:type schema:ScholarlyArticle
62 N1252196ee4cc4ce4a7c90c3c49587980 schema:name readcube_id
63 schema:value 90f01f3cd45d8a6e6d3bc24ebe64ad7fe432fcc2c13bda4ee787cc14ce37190b
64 rdf:type schema:PropertyValue
65 N15da80b4c3d441f5aaf182205be0fa5e schema:name dimensions_id
66 schema:value pub.1113199625
67 rdf:type schema:PropertyValue
68 N7201f510978a477eb7418fb76581d0ee schema:name doi
69 schema:value 10.1007/s00170-019-03500-z
70 rdf:type schema:PropertyValue
71 N832bffa71865492f80f6f2895e243e4c schema:affiliation https://www.grid.ac/institutes/grid.412151.2
72 schema:familyName Promoppatum
73 schema:givenName Patcharapit
74 rdf:type schema:Person
75 N85489bdd247b4d3ab133ebc234a5e6d7 schema:name Springer Nature - SN SciGraph project
76 rdf:type schema:Organization
77 N98ddb4e68a3d439fb8d414ba5e611225 schema:affiliation https://www.grid.ac/institutes/grid.147455.6
78 schema:familyName Yao
79 schema:givenName Shi-Chune
80 rdf:type schema:Person
81 Nd6bf001c5b2b43a7ac1a9ce5097428d4 rdf:first N832bffa71865492f80f6f2895e243e4c
82 rdf:rest Nf67482c1a9a14cdf89041a6eb40174fe
83 Nf67482c1a9a14cdf89041a6eb40174fe rdf:first N98ddb4e68a3d439fb8d414ba5e611225
84 rdf:rest rdf:nil
85 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
86 schema:name Engineering
87 rdf:type schema:DefinedTerm
88 anzsrc-for:0910 schema:inDefinedTermSet anzsrc-for:
89 schema:name Manufacturing Engineering
90 rdf:type schema:DefinedTerm
91 sg:journal.1043671 schema:issn 0268-3768
92 1433-3015
93 schema:name The International Journal of Advanced Manufacturing Technology
94 rdf:type schema:Periodical
95 sg:pub.10.1007/978-3-642-19831-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022323959
96 https://doi.org/10.1007/978-3-642-19831-1
97 rdf:type schema:CreativeWork
98 sg:pub.10.1007/s00170-011-3566-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023499442
99 https://doi.org/10.1007/s00170-011-3566-1
100 rdf:type schema:CreativeWork
101 sg:pub.10.1007/s00170-011-3776-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003054026
102 https://doi.org/10.1007/s00170-011-3776-6
103 rdf:type schema:CreativeWork
104 sg:pub.10.1007/s00170-017-1489-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1100163211
105 https://doi.org/10.1007/s00170-017-1489-1
106 rdf:type schema:CreativeWork
107 sg:pub.10.1007/s11665-014-0958-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1032374549
108 https://doi.org/10.1007/s11665-014-0958-z
109 rdf:type schema:CreativeWork
110 sg:pub.10.1007/s11665-017-2768-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085981757
111 https://doi.org/10.1007/s11665-017-2768-6
112 rdf:type schema:CreativeWork
113 sg:pub.10.1007/s11837-001-0067-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1045226426
114 https://doi.org/10.1007/s11837-001-0067-y
115 rdf:type schema:CreativeWork
116 sg:pub.10.1007/s11837-016-2234-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052862781
117 https://doi.org/10.1007/s11837-016-2234-1
118 rdf:type schema:CreativeWork
119 sg:pub.10.1007/s40964-018-0039-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1100559565
120 https://doi.org/10.1007/s40964-018-0039-1
121 rdf:type schema:CreativeWork
122 https://app.dimensions.ai/details/publication/pub.1022323959 schema:CreativeWork
123 https://doi.org/10.1016/j.actamat.2016.12.062 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048146924
124 rdf:type schema:CreativeWork
125 https://doi.org/10.1016/j.addma.2014.08.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018024618
126 rdf:type schema:CreativeWork
127 https://doi.org/10.1016/j.addma.2016.03.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038651443
128 rdf:type schema:CreativeWork
129 https://doi.org/10.1016/j.addma.2016.12.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017789106
130 rdf:type schema:CreativeWork
131 https://doi.org/10.1016/j.addma.2018.10.017 schema:sameAs https://app.dimensions.ai/details/publication/pub.1107491262
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1016/j.camwa.2018.06.029 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105250535
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1016/j.eng.2017.05.023 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092997704
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1016/j.ijmachtools.2011.02.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005473588
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1016/j.ijmachtools.2018.01.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1100753459
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1016/j.jmapro.2018.04.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103217269
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1016/j.jmatprotec.2010.05.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027818721
144 rdf:type schema:CreativeWork
145 https://doi.org/10.1016/j.jmatprotec.2014.05.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015630083
146 rdf:type schema:CreativeWork
147 https://doi.org/10.1016/j.jmatprotec.2014.06.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011723383
148 rdf:type schema:CreativeWork
149 https://doi.org/10.1016/j.jmatprotec.2016.10.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052737361
150 rdf:type schema:CreativeWork
151 https://doi.org/10.1016/j.jmatprotec.2017.11.032 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092916735
152 rdf:type schema:CreativeWork
153 https://doi.org/10.1016/j.jmatprotec.2018.02.034 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101342509
154 rdf:type schema:CreativeWork
155 https://doi.org/10.1016/j.matdes.2011.09.051 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048332807
156 rdf:type schema:CreativeWork
157 https://doi.org/10.1016/j.matdes.2014.07.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021849271
158 rdf:type schema:CreativeWork
159 https://doi.org/10.1016/j.matdes.2014.09.044 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010234395
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1016/j.matdes.2016.10.037 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021097737
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1016/j.msea.2009.02.019 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051171143
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1016/j.msea.2018.08.037 schema:sameAs https://app.dimensions.ai/details/publication/pub.1106132871
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1016/j.optlastec.2014.07.021 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024046954
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1016/j.powtec.2017.01.030 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034365791
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1016/j.prosdent.2014.06.017 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048422595
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1088/0022-3727/44/44/445401 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033667930
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1089/pho.2017.4311 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092198093
176 rdf:type schema:CreativeWork
177 https://www.grid.ac/institutes/grid.147455.6 schema:alternateName Carnegie Mellon University
178 schema:name Department of Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Ave., 15213, Pittsburgh, PA, USA
179 rdf:type schema:Organization
180 https://www.grid.ac/institutes/grid.412151.2 schema:alternateName King Mongkut's University of Technology Thonburi
181 schema:name Department of Mechanical Engineering, Faculty of Engineering, King Mongkut’s University of Technology Thonburi, 126 Pracha Uthit Rd., Bang Mod, Thung Khru, 10140, Bangkok, Thailand
182 rdf:type schema:Organization
 




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


...