An Algorithm for Inverse Modeling of Layer-by-Layer Deposition Processes View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2009-04

AUTHORS

S.G. Lambrakos, K.P. Cooper

ABSTRACT

Metallic parts can be made by deposition of liquid metal in a layer-by-layer fashion. By this means, layered structures can be produced that are made up of overlapping reinforced droplets. In particular, prototypes, i.e., customized parts and tooling, can be produced in this way. In order that layer-by-layer fabrication techniques transition from prototyping to manufacturing, however, the processes must be made reliable and consistent. Accordingly, detailed microstructural and thermal characterizations of the product are needed to advance manufacturing goals based on layer-by-layer deposition processes. The inherent complexity of layer-by-layer deposition processes, characteristic of energy and mass deposition processes in general, is such that process modeling based on theory, or the direct-problem approach, is extremely difficult. A general approach to overcoming difficulties associated with this inherent complexity is the inverse-problem approach. Presented here is an algorithm for inverse modeling of heat transfer occurring during layer-by-layer deposition, which is potentially adaptable for prediction of temperature histories in samples that are made by layer-by-layer deposition processes. More... »

PAGES

221-230

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11665-008-9268-7

DOI

http://dx.doi.org/10.1007/s11665-008-9268-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1042230192


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": "United States Naval Research Laboratory", 
          "id": "https://www.grid.ac/institutes/grid.89170.37", 
          "name": [
            "Materials Science and Technology Division, Naval Research Laboratory, Washington, DC, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lambrakos", 
        "givenName": "S.G.", 
        "id": "sg:person.01265271754.16", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01265271754.16"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "United States Naval Research Laboratory", 
          "id": "https://www.grid.ac/institutes/grid.89170.37", 
          "name": [
            "Materials Science and Technology Division, Naval Research Laboratory, Washington, DC, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Cooper", 
        "givenName": "K.P.", 
        "id": "sg:person.013075065325.23", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013075065325.23"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1179/136217103225005561", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020759126"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11665-007-9197-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022613227", 
          "https://doi.org/10.1007/s11665-007-9197-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/11428848_95", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023547261", 
          "https://doi.org/10.1007/11428848_95"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/11428848_95", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023547261", 
          "https://doi.org/10.1007/11428848_95"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11837-000-0028-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031989878", 
          "https://doi.org/10.1007/s11837-000-0028-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1179/136217102225002646", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039216845"
        ], 
        "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/10.1137/030602551", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062842822"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1557/proc-758-ll1.4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1067956055"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4612-5338-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1109705064", 
          "https://doi.org/10.1007/978-1-4612-5338-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4612-5338-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1109705064", 
          "https://doi.org/10.1007/978-1-4612-5338-9"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2009-04", 
    "datePublishedReg": "2009-04-01", 
    "description": "Metallic parts can be made by deposition of liquid metal in a layer-by-layer fashion. By this means, layered structures can be produced that are made up of overlapping reinforced droplets. In particular, prototypes, i.e., customized parts and tooling, can be produced in this way. In order that layer-by-layer fabrication techniques transition from prototyping to manufacturing, however, the processes must be made reliable and consistent. Accordingly, detailed microstructural and thermal characterizations of the product are needed to advance manufacturing goals based on layer-by-layer deposition processes. The inherent complexity of layer-by-layer deposition processes, characteristic of energy and mass deposition processes in general, is such that process modeling based on theory, or the direct-problem approach, is extremely difficult. A general approach to overcoming difficulties associated with this inherent complexity is the inverse-problem approach. Presented here is an algorithm for inverse modeling of heat transfer occurring during layer-by-layer deposition, which is potentially adaptable for prediction of temperature histories in samples that are made by layer-by-layer deposition processes.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s11665-008-9268-7", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1042007", 
        "issn": [
          "1059-9495", 
          "1544-1024"
        ], 
        "name": "Journal of Materials Engineering and Performance", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "18"
      }
    ], 
    "name": "An Algorithm for Inverse Modeling of Layer-by-Layer Deposition Processes", 
    "pagination": "221-230", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "c929ed13bf8741be36926b0b0159278dc01c76b935a3dc9d6d18940381fc0c48"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11665-008-9268-7"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1042230192"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11665-008-9268-7", 
      "https://app.dimensions.ai/details/publication/pub.1042230192"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T02:29", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8700_00000592.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs11665-008-9268-7"
  }
]
 

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/s11665-008-9268-7'

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/s11665-008-9268-7'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11665-008-9268-7'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11665-008-9268-7'


 

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

100 TRIPLES      21 PREDICATES      36 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11665-008-9268-7 schema:about anzsrc-for:09
2 anzsrc-for:0915
3 schema:author Nf86bba3662e043bc94fbb473e0e649aa
4 schema:citation sg:pub.10.1007/11428848_95
5 sg:pub.10.1007/978-1-4612-5338-9
6 sg:pub.10.1007/s11665-007-9197-x
7 sg:pub.10.1007/s11837-000-0028-x
8 sg:pub.10.1007/s11837-001-0067-y
9 https://doi.org/10.1137/030602551
10 https://doi.org/10.1179/136217102225002646
11 https://doi.org/10.1179/136217103225005561
12 https://doi.org/10.1557/proc-758-ll1.4
13 schema:datePublished 2009-04
14 schema:datePublishedReg 2009-04-01
15 schema:description Metallic parts can be made by deposition of liquid metal in a layer-by-layer fashion. By this means, layered structures can be produced that are made up of overlapping reinforced droplets. In particular, prototypes, i.e., customized parts and tooling, can be produced in this way. In order that layer-by-layer fabrication techniques transition from prototyping to manufacturing, however, the processes must be made reliable and consistent. Accordingly, detailed microstructural and thermal characterizations of the product are needed to advance manufacturing goals based on layer-by-layer deposition processes. The inherent complexity of layer-by-layer deposition processes, characteristic of energy and mass deposition processes in general, is such that process modeling based on theory, or the direct-problem approach, is extremely difficult. A general approach to overcoming difficulties associated with this inherent complexity is the inverse-problem approach. Presented here is an algorithm for inverse modeling of heat transfer occurring during layer-by-layer deposition, which is potentially adaptable for prediction of temperature histories in samples that are made by layer-by-layer deposition processes.
16 schema:genre research_article
17 schema:inLanguage en
18 schema:isAccessibleForFree false
19 schema:isPartOf N789c1802ac6c485caf9aecb910cf64a1
20 Nd4e4a29621df4bf98e974ddb514f2d49
21 sg:journal.1042007
22 schema:name An Algorithm for Inverse Modeling of Layer-by-Layer Deposition Processes
23 schema:pagination 221-230
24 schema:productId N628180ac7d094156a22f3e0af7aa30e2
25 N6c95c9ebc34e4d1f8f014a45a98e8cee
26 Nd9d73d44463a4b578a77143a00f7025e
27 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042230192
28 https://doi.org/10.1007/s11665-008-9268-7
29 schema:sdDatePublished 2019-04-11T02:29
30 schema:sdLicense https://scigraph.springernature.com/explorer/license/
31 schema:sdPublisher N39192729a46c46be96bd2fb3f7738f3e
32 schema:url http://link.springer.com/10.1007%2Fs11665-008-9268-7
33 sgo:license sg:explorer/license/
34 sgo:sdDataset articles
35 rdf:type schema:ScholarlyArticle
36 N39192729a46c46be96bd2fb3f7738f3e schema:name Springer Nature - SN SciGraph project
37 rdf:type schema:Organization
38 N628180ac7d094156a22f3e0af7aa30e2 schema:name doi
39 schema:value 10.1007/s11665-008-9268-7
40 rdf:type schema:PropertyValue
41 N6c21ba1d63b644039e9e1b776ea8f0c8 rdf:first sg:person.013075065325.23
42 rdf:rest rdf:nil
43 N6c95c9ebc34e4d1f8f014a45a98e8cee schema:name dimensions_id
44 schema:value pub.1042230192
45 rdf:type schema:PropertyValue
46 N789c1802ac6c485caf9aecb910cf64a1 schema:issueNumber 3
47 rdf:type schema:PublicationIssue
48 Nd4e4a29621df4bf98e974ddb514f2d49 schema:volumeNumber 18
49 rdf:type schema:PublicationVolume
50 Nd9d73d44463a4b578a77143a00f7025e schema:name readcube_id
51 schema:value c929ed13bf8741be36926b0b0159278dc01c76b935a3dc9d6d18940381fc0c48
52 rdf:type schema:PropertyValue
53 Nf86bba3662e043bc94fbb473e0e649aa rdf:first sg:person.01265271754.16
54 rdf:rest N6c21ba1d63b644039e9e1b776ea8f0c8
55 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
56 schema:name Engineering
57 rdf:type schema:DefinedTerm
58 anzsrc-for:0915 schema:inDefinedTermSet anzsrc-for:
59 schema:name Interdisciplinary Engineering
60 rdf:type schema:DefinedTerm
61 sg:journal.1042007 schema:issn 1059-9495
62 1544-1024
63 schema:name Journal of Materials Engineering and Performance
64 rdf:type schema:Periodical
65 sg:person.01265271754.16 schema:affiliation https://www.grid.ac/institutes/grid.89170.37
66 schema:familyName Lambrakos
67 schema:givenName S.G.
68 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01265271754.16
69 rdf:type schema:Person
70 sg:person.013075065325.23 schema:affiliation https://www.grid.ac/institutes/grid.89170.37
71 schema:familyName Cooper
72 schema:givenName K.P.
73 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013075065325.23
74 rdf:type schema:Person
75 sg:pub.10.1007/11428848_95 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023547261
76 https://doi.org/10.1007/11428848_95
77 rdf:type schema:CreativeWork
78 sg:pub.10.1007/978-1-4612-5338-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1109705064
79 https://doi.org/10.1007/978-1-4612-5338-9
80 rdf:type schema:CreativeWork
81 sg:pub.10.1007/s11665-007-9197-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1022613227
82 https://doi.org/10.1007/s11665-007-9197-x
83 rdf:type schema:CreativeWork
84 sg:pub.10.1007/s11837-000-0028-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1031989878
85 https://doi.org/10.1007/s11837-000-0028-x
86 rdf:type schema:CreativeWork
87 sg:pub.10.1007/s11837-001-0067-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1045226426
88 https://doi.org/10.1007/s11837-001-0067-y
89 rdf:type schema:CreativeWork
90 https://doi.org/10.1137/030602551 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062842822
91 rdf:type schema:CreativeWork
92 https://doi.org/10.1179/136217102225002646 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039216845
93 rdf:type schema:CreativeWork
94 https://doi.org/10.1179/136217103225005561 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020759126
95 rdf:type schema:CreativeWork
96 https://doi.org/10.1557/proc-758-ll1.4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1067956055
97 rdf:type schema:CreativeWork
98 https://www.grid.ac/institutes/grid.89170.37 schema:alternateName United States Naval Research Laboratory
99 schema:name Materials Science and Technology Division, Naval Research Laboratory, Washington, DC, USA
100 rdf:type schema:Organization
 




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


...