Ontology type: schema:ScholarlyArticle
1996-03
AUTHORSM. Rappaz, Ch A. Gandin, J. L. Desbiolles, Ph. Thévoz
ABSTRACTGrain structure formation during solidification can be simulatedvia the use of stochastic models providing the physical mechanisms of nucleation and dendrite growth are accounted for. With this goal in mind, a physically based cellular automaton (CA) model has been coupled with finite element (FE) heat flow computations and implemented into the code3- MOS. The CA enmeshment of the solidifying domain with small square cells is first generated automatically from the FE mesh. Within each time-step, the variation of enthalpy at each node of the FE mesh is calculated using an implicit scheme and a Newton-type linearization method. After interpolation of the explicit temperature and of the enthalpy variation at the cell location, the nucleation and growth of grains are simulated using the CA algorithm. This algorithm accounts for the heterogeneous nucleation in the bulk and at the surface of the ingot, for the growth and preferential growth directions of the dendrites, and for microsegregation. The variations of volume fraction of solid at the cell location are then summed up at the FE nodes in order to find the new temperatures. This CAFE model, which allows the prediction and the visualization of grain structures during and after solidification, is applied to various solidification processes: the investment casting of turbine blades, the continuous casting of rods, and the laser remelting or welding of plates. Because the CAFE model is yet two-dimensional (2-D), the simulation results are compared in a qualitative way with experimental findings. More... »
PAGES695-705
http://scigraph.springernature.com/pub.10.1007/bf02648956
DOIhttp://dx.doi.org/10.1007/bf02648956
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1033404338
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/2008/03",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Chemical Sciences",
"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"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0306",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Physical Chemistry (incl. Structural)",
"type": "DefinedTerm"
},
{
"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/2008/0913",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Mechanical Engineering",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "D\u00e9partement des Mat\u00e9riaux, Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne, Laboratoire de M\u00e9tallurgie Physique, CH-1015, Lausanne, Switzerland",
"id": "http://www.grid.ac/institutes/grid.5333.6",
"name": [
"D\u00e9partement des Mat\u00e9riaux, Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne, Laboratoire de M\u00e9tallurgie Physique, CH-1015, Lausanne, Switzerland"
],
"type": "Organization"
},
"familyName": "Rappaz",
"givenName": "M.",
"id": "sg:person.013657516157.10",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013657516157.10"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "D\u00e9partement des Mat\u00e9riaux, Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne, Laboratoire de M\u00e9tallurgie Physique, CH-1015, Lausanne, Switzerland",
"id": "http://www.grid.ac/institutes/grid.5333.6",
"name": [
"D\u00e9partement des Mat\u00e9riaux, Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne, Laboratoire de M\u00e9tallurgie Physique, CH-1015, Lausanne, Switzerland"
],
"type": "Organization"
},
"familyName": "Gandin",
"givenName": "Ch A.",
"id": "sg:person.010332710054.26",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010332710054.26"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "D\u00e9partement des Mat\u00e9riaux, Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne, Laboratoire de M\u00e9tallurgie Physique, CH-1015, Lausanne, Switzerland",
"id": "http://www.grid.ac/institutes/grid.5333.6",
"name": [
"D\u00e9partement des Mat\u00e9riaux, Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne, Laboratoire de M\u00e9tallurgie Physique, CH-1015, Lausanne, Switzerland"
],
"type": "Organization"
},
"familyName": "Desbiolles",
"givenName": "J. L.",
"id": "sg:person.011355726671.24",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011355726671.24"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Calcom SA, CH-1015, Lausanne, PSE, Switzerland",
"id": "http://www.grid.ac/institutes/grid.433079.a",
"name": [
"Calcom SA, CH-1015, Lausanne, PSE, Switzerland"
],
"type": "Organization"
},
"familyName": "Th\u00e9voz",
"givenName": "Ph.",
"id": "sg:person.014717560262.48",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014717560262.48"
],
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1007/bf02651604",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1001291895",
"https://doi.org/10.1007/bf02651604"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf02644688",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1001954572",
"https://doi.org/10.1007/bf02644688"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf02647605",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1008404524",
"https://doi.org/10.1007/bf02647605"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf02657334",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1035268094",
"https://doi.org/10.1007/bf02657334"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf02647249",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1015246123",
"https://doi.org/10.1007/bf02647249"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-94-010-9506-8",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1043637086",
"https://doi.org/10.1007/978-94-010-9506-8"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1557/s0883769400038811",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1067962964",
"https://doi.org/10.1557/s0883769400038811"
],
"type": "CreativeWork"
}
],
"datePublished": "1996-03",
"datePublishedReg": "1996-03-01",
"description": "Grain structure formation during solidification can be simulatedvia the use of stochastic models providing the physical mechanisms of nucleation and dendrite growth are accounted for. With this goal in mind, a physically based cellular automaton (CA) model has been coupled with finite element (FE) heat flow computations and implemented into the code3- MOS. The CA enmeshment of the solidifying domain with small square cells is first generated automatically from the FE mesh. Within each time-step, the variation of enthalpy at each node of the FE mesh is calculated using an implicit scheme and a Newton-type linearization method. After interpolation of the explicit temperature and of the enthalpy variation at the cell location, the nucleation and growth of grains are simulated using the CA algorithm. This algorithm accounts for the heterogeneous nucleation in the bulk and at the surface of the ingot, for the growth and preferential growth directions of the dendrites, and for microsegregation. The variations of volume fraction of solid at the cell location are then summed up at the FE nodes in order to find the new temperatures. This CAFE model, which allows the prediction and the visualization of grain structures during and after solidification, is applied to various solidification processes: the investment casting of turbine blades, the continuous casting of rods, and the laser remelting or welding of plates. Because the CAFE model is yet two-dimensional (2-D), the simulation results are compared in a qualitative way with experimental findings.",
"genre": "article",
"id": "sg:pub.10.1007/bf02648956",
"isAccessibleForFree": false,
"isPartOf": [
{
"id": "sg:journal.1136292",
"issn": [
"1073-5623",
"1543-1940"
],
"name": "Metallurgical and Materials Transactions A",
"publisher": "Springer Nature",
"type": "Periodical"
},
{
"issueNumber": "3",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "27"
}
],
"keywords": [
"grain structure",
"solidification process",
"FE mesh",
"CAFE model",
"welding of plates",
"grain structure formation",
"heat flow computations",
"growth of grains",
"laser remelting",
"turbine blades",
"continuous casting",
"investment casting",
"FE nodes",
"preferential growth direction",
"volume fraction",
"flow computations",
"linearization method",
"heterogeneous nucleation",
"simulation results",
"casting",
"solidification",
"physical mechanisms",
"growth direction",
"implicit scheme",
"nucleation",
"small square cells",
"square cells",
"new temperature",
"structure formation",
"variation of enthalpy",
"cellular automata model",
"temperature",
"welding",
"mesh",
"enthalpy variation",
"experimental findings",
"explicit temperature",
"ingots",
"remelting",
"microsegregation",
"blades",
"automata model",
"prediction",
"plate",
"stochastic model",
"surface",
"structure",
"model",
"process",
"grains",
"cell location",
"rods",
"algorithm",
"bulk",
"CA algorithm",
"variation",
"interpolation",
"qualitative way",
"location",
"scheme",
"direction",
"enthalpy",
"method",
"order",
"computation",
"growth",
"fraction",
"nodes",
"formation",
"results",
"visualization",
"dendrites",
"use",
"mechanism",
"way",
"domain",
"goal",
"enmeshment",
"cells",
"mind",
"findings"
],
"name": "Prediction of grain structures in various solidification processes",
"pagination": "695-705",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1033404338"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/bf02648956"
]
}
],
"sameAs": [
"https://doi.org/10.1007/bf02648956",
"https://app.dimensions.ai/details/publication/pub.1033404338"
],
"sdDataset": "articles",
"sdDatePublished": "2022-08-04T16:53",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-springernature-scigraph/baseset/20220804/entities/gbq_results/article/article_289.jsonl",
"type": "ScholarlyArticle",
"url": "https://doi.org/10.1007/bf02648956"
}
]
Download the RDF metadata as: json-ld nt turtle xml License info
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/bf02648956'
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/bf02648956'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/bf02648956'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/bf02648956'
This table displays all metadata directly associated to this object as RDF triples.
202 TRIPLES
21 PREDICATES
116 URIs
98 LITERALS
6 BLANK NODES
Subject | Predicate | Object | |
---|---|---|---|
1 | sg:pub.10.1007/bf02648956 | schema:about | anzsrc-for:03 |
2 | ″ | ″ | anzsrc-for:0306 |
3 | ″ | ″ | anzsrc-for:09 |
4 | ″ | ″ | anzsrc-for:0912 |
5 | ″ | ″ | anzsrc-for:0913 |
6 | ″ | schema:author | Nfb668c43a14c40f88d032b9113ce5047 |
7 | ″ | schema:citation | sg:pub.10.1007/978-94-010-9506-8 |
8 | ″ | ″ | sg:pub.10.1007/bf02644688 |
9 | ″ | ″ | sg:pub.10.1007/bf02647249 |
10 | ″ | ″ | sg:pub.10.1007/bf02647605 |
11 | ″ | ″ | sg:pub.10.1007/bf02651604 |
12 | ″ | ″ | sg:pub.10.1007/bf02657334 |
13 | ″ | ″ | sg:pub.10.1557/s0883769400038811 |
14 | ″ | schema:datePublished | 1996-03 |
15 | ″ | schema:datePublishedReg | 1996-03-01 |
16 | ″ | schema:description | Grain structure formation during solidification can be simulatedvia the use of stochastic models providing the physical mechanisms of nucleation and dendrite growth are accounted for. With this goal in mind, a physically based cellular automaton (CA) model has been coupled with finite element (FE) heat flow computations and implemented into the code3- MOS. The CA enmeshment of the solidifying domain with small square cells is first generated automatically from the FE mesh. Within each time-step, the variation of enthalpy at each node of the FE mesh is calculated using an implicit scheme and a Newton-type linearization method. After interpolation of the explicit temperature and of the enthalpy variation at the cell location, the nucleation and growth of grains are simulated using the CA algorithm. This algorithm accounts for the heterogeneous nucleation in the bulk and at the surface of the ingot, for the growth and preferential growth directions of the dendrites, and for microsegregation. The variations of volume fraction of solid at the cell location are then summed up at the FE nodes in order to find the new temperatures. This CAFE model, which allows the prediction and the visualization of grain structures during and after solidification, is applied to various solidification processes: the investment casting of turbine blades, the continuous casting of rods, and the laser remelting or welding of plates. Because the CAFE model is yet two-dimensional (2-D), the simulation results are compared in a qualitative way with experimental findings. |
17 | ″ | schema:genre | article |
18 | ″ | schema:isAccessibleForFree | false |
19 | ″ | schema:isPartOf | N159eaf355a8f483396f9a3baaaf68cf4 |
20 | ″ | ″ | Nbc9b6addee444b2695e3cec77f713060 |
21 | ″ | ″ | sg:journal.1136292 |
22 | ″ | schema:keywords | CA algorithm |
23 | ″ | ″ | CAFE model |
24 | ″ | ″ | FE mesh |
25 | ″ | ″ | FE nodes |
26 | ″ | ″ | algorithm |
27 | ″ | ″ | automata model |
28 | ″ | ″ | blades |
29 | ″ | ″ | bulk |
30 | ″ | ″ | casting |
31 | ″ | ″ | cell location |
32 | ″ | ″ | cells |
33 | ″ | ″ | cellular automata model |
34 | ″ | ″ | computation |
35 | ″ | ″ | continuous casting |
36 | ″ | ″ | dendrites |
37 | ″ | ″ | direction |
38 | ″ | ″ | domain |
39 | ″ | ″ | enmeshment |
40 | ″ | ″ | enthalpy |
41 | ″ | ″ | enthalpy variation |
42 | ″ | ″ | experimental findings |
43 | ″ | ″ | explicit temperature |
44 | ″ | ″ | findings |
45 | ″ | ″ | flow computations |
46 | ″ | ″ | formation |
47 | ″ | ″ | fraction |
48 | ″ | ″ | goal |
49 | ″ | ″ | grain structure |
50 | ″ | ″ | grain structure formation |
51 | ″ | ″ | grains |
52 | ″ | ″ | growth |
53 | ″ | ″ | growth direction |
54 | ″ | ″ | growth of grains |
55 | ″ | ″ | heat flow computations |
56 | ″ | ″ | heterogeneous nucleation |
57 | ″ | ″ | implicit scheme |
58 | ″ | ″ | ingots |
59 | ″ | ″ | interpolation |
60 | ″ | ″ | investment casting |
61 | ″ | ″ | laser remelting |
62 | ″ | ″ | linearization method |
63 | ″ | ″ | location |
64 | ″ | ″ | mechanism |
65 | ″ | ″ | mesh |
66 | ″ | ″ | method |
67 | ″ | ″ | microsegregation |
68 | ″ | ″ | mind |
69 | ″ | ″ | model |
70 | ″ | ″ | new temperature |
71 | ″ | ″ | nodes |
72 | ″ | ″ | nucleation |
73 | ″ | ″ | order |
74 | ″ | ″ | physical mechanisms |
75 | ″ | ″ | plate |
76 | ″ | ″ | prediction |
77 | ″ | ″ | preferential growth direction |
78 | ″ | ″ | process |
79 | ″ | ″ | qualitative way |
80 | ″ | ″ | remelting |
81 | ″ | ″ | results |
82 | ″ | ″ | rods |
83 | ″ | ″ | scheme |
84 | ″ | ″ | simulation results |
85 | ″ | ″ | small square cells |
86 | ″ | ″ | solidification |
87 | ″ | ″ | solidification process |
88 | ″ | ″ | square cells |
89 | ″ | ″ | stochastic model |
90 | ″ | ″ | structure |
91 | ″ | ″ | structure formation |
92 | ″ | ″ | surface |
93 | ″ | ″ | temperature |
94 | ″ | ″ | turbine blades |
95 | ″ | ″ | use |
96 | ″ | ″ | variation |
97 | ″ | ″ | variation of enthalpy |
98 | ″ | ″ | visualization |
99 | ″ | ″ | volume fraction |
100 | ″ | ″ | way |
101 | ″ | ″ | welding |
102 | ″ | ″ | welding of plates |
103 | ″ | schema:name | Prediction of grain structures in various solidification processes |
104 | ″ | schema:pagination | 695-705 |
105 | ″ | schema:productId | N4428b8fe9d2942a385743f531e3a6396 |
106 | ″ | ″ | Na054ea4b42ea4e50b46f9437e3556481 |
107 | ″ | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1033404338 |
108 | ″ | ″ | https://doi.org/10.1007/bf02648956 |
109 | ″ | schema:sdDatePublished | 2022-08-04T16:53 |
110 | ″ | schema:sdLicense | https://scigraph.springernature.com/explorer/license/ |
111 | ″ | schema:sdPublisher | N1be3de76ecc841be97a3311728261f7a |
112 | ″ | schema:url | https://doi.org/10.1007/bf02648956 |
113 | ″ | sgo:license | sg:explorer/license/ |
114 | ″ | sgo:sdDataset | articles |
115 | ″ | rdf:type | schema:ScholarlyArticle |
116 | N159eaf355a8f483396f9a3baaaf68cf4 | schema:issueNumber | 3 |
117 | ″ | rdf:type | schema:PublicationIssue |
118 | N1afe124071fd4640a0f467419141c5bd | rdf:first | sg:person.014717560262.48 |
119 | ″ | rdf:rest | rdf:nil |
120 | N1be3de76ecc841be97a3311728261f7a | schema:name | Springer Nature - SN SciGraph project |
121 | ″ | rdf:type | schema:Organization |
122 | N4428b8fe9d2942a385743f531e3a6396 | schema:name | dimensions_id |
123 | ″ | schema:value | pub.1033404338 |
124 | ″ | rdf:type | schema:PropertyValue |
125 | N6a93b5e446e54a4d8a8e3829b2cd593e | rdf:first | sg:person.011355726671.24 |
126 | ″ | rdf:rest | N1afe124071fd4640a0f467419141c5bd |
127 | Na054ea4b42ea4e50b46f9437e3556481 | schema:name | doi |
128 | ″ | schema:value | 10.1007/bf02648956 |
129 | ″ | rdf:type | schema:PropertyValue |
130 | Nbc9b6addee444b2695e3cec77f713060 | schema:volumeNumber | 27 |
131 | ″ | rdf:type | schema:PublicationVolume |
132 | Nd613310331a6407bae3570b2b5e7167b | rdf:first | sg:person.010332710054.26 |
133 | ″ | rdf:rest | N6a93b5e446e54a4d8a8e3829b2cd593e |
134 | Nfb668c43a14c40f88d032b9113ce5047 | rdf:first | sg:person.013657516157.10 |
135 | ″ | rdf:rest | Nd613310331a6407bae3570b2b5e7167b |
136 | anzsrc-for:03 | schema:inDefinedTermSet | anzsrc-for: |
137 | ″ | schema:name | Chemical Sciences |
138 | ″ | rdf:type | schema:DefinedTerm |
139 | anzsrc-for:0306 | schema:inDefinedTermSet | anzsrc-for: |
140 | ″ | schema:name | Physical Chemistry (incl. Structural) |
141 | ″ | rdf:type | schema:DefinedTerm |
142 | anzsrc-for:09 | schema:inDefinedTermSet | anzsrc-for: |
143 | ″ | schema:name | Engineering |
144 | ″ | rdf:type | schema:DefinedTerm |
145 | anzsrc-for:0912 | schema:inDefinedTermSet | anzsrc-for: |
146 | ″ | schema:name | Materials Engineering |
147 | ″ | rdf:type | schema:DefinedTerm |
148 | anzsrc-for:0913 | schema:inDefinedTermSet | anzsrc-for: |
149 | ″ | schema:name | Mechanical Engineering |
150 | ″ | rdf:type | schema:DefinedTerm |
151 | sg:journal.1136292 | schema:issn | 1073-5623 |
152 | ″ | ″ | 1543-1940 |
153 | ″ | schema:name | Metallurgical and Materials Transactions A |
154 | ″ | schema:publisher | Springer Nature |
155 | ″ | rdf:type | schema:Periodical |
156 | sg:person.010332710054.26 | schema:affiliation | grid-institutes:grid.5333.6 |
157 | ″ | schema:familyName | Gandin |
158 | ″ | schema:givenName | Ch A. |
159 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010332710054.26 |
160 | ″ | rdf:type | schema:Person |
161 | sg:person.011355726671.24 | schema:affiliation | grid-institutes:grid.5333.6 |
162 | ″ | schema:familyName | Desbiolles |
163 | ″ | schema:givenName | J. L. |
164 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011355726671.24 |
165 | ″ | rdf:type | schema:Person |
166 | sg:person.013657516157.10 | schema:affiliation | grid-institutes:grid.5333.6 |
167 | ″ | schema:familyName | Rappaz |
168 | ″ | schema:givenName | M. |
169 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013657516157.10 |
170 | ″ | rdf:type | schema:Person |
171 | sg:person.014717560262.48 | schema:affiliation | grid-institutes:grid.433079.a |
172 | ″ | schema:familyName | Thévoz |
173 | ″ | schema:givenName | Ph. |
174 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014717560262.48 |
175 | ″ | rdf:type | schema:Person |
176 | sg:pub.10.1007/978-94-010-9506-8 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1043637086 |
177 | ″ | ″ | https://doi.org/10.1007/978-94-010-9506-8 |
178 | ″ | rdf:type | schema:CreativeWork |
179 | sg:pub.10.1007/bf02644688 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1001954572 |
180 | ″ | ″ | https://doi.org/10.1007/bf02644688 |
181 | ″ | rdf:type | schema:CreativeWork |
182 | sg:pub.10.1007/bf02647249 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1015246123 |
183 | ″ | ″ | https://doi.org/10.1007/bf02647249 |
184 | ″ | rdf:type | schema:CreativeWork |
185 | sg:pub.10.1007/bf02647605 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1008404524 |
186 | ″ | ″ | https://doi.org/10.1007/bf02647605 |
187 | ″ | rdf:type | schema:CreativeWork |
188 | sg:pub.10.1007/bf02651604 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1001291895 |
189 | ″ | ″ | https://doi.org/10.1007/bf02651604 |
190 | ″ | rdf:type | schema:CreativeWork |
191 | sg:pub.10.1007/bf02657334 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1035268094 |
192 | ″ | ″ | https://doi.org/10.1007/bf02657334 |
193 | ″ | rdf:type | schema:CreativeWork |
194 | sg:pub.10.1557/s0883769400038811 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1067962964 |
195 | ″ | ″ | https://doi.org/10.1557/s0883769400038811 |
196 | ″ | rdf:type | schema:CreativeWork |
197 | grid-institutes:grid.433079.a | schema:alternateName | Calcom SA, CH-1015, Lausanne, PSE, Switzerland |
198 | ″ | schema:name | Calcom SA, CH-1015, Lausanne, PSE, Switzerland |
199 | ″ | rdf:type | schema:Organization |
200 | grid-institutes:grid.5333.6 | schema:alternateName | Département des Matériaux, Ecole Polytechnique Fédérale de Lausanne, Laboratoire de Métallurgie Physique, CH-1015, Lausanne, Switzerland |
201 | ″ | schema:name | Département des Matériaux, Ecole Polytechnique Fédérale de Lausanne, Laboratoire de Métallurgie Physique, CH-1015, Lausanne, Switzerland |
202 | ″ | rdf:type | schema:Organization |