Seeking a paper for digital printing with maximum gamut volume: a lesson from artificial intelligence View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2021-10-15

AUTHORS

Maryam Ataeefard, Seyyed Mohamad Sadati Tilebon

ABSTRACT

The color gamut of imaging media is significant for the reproduction of color images because its magnitude directly affects the degree to which colors change during the printing process. Over the last few years, digital impression technology has started to play a substantial role in the printing industry due to the quest for short runs and variable information printing. The color gamut of electrophotographic digital printing depends on various parameters including the printer and toner, but especially the properties (whiteness, roughness, and gloss) of the paper, which influence the final printed color gamut and replication quality. Artificial intelligence approaches are applied herein for the first time to choose and predict the performance of a paper with appropriate properties to achieve the maximum color gamut. A genetic algorithm-based computer code is developed to optimize the architecture of an artificial neural network, thereby yielding an accurate model to predict the color gamut achievable in electrophotographic color printing. The gamut volume was generated using an Eye-One spectrophotometer, ProfileMaker, and ColorThink software. The properties of 11 dissimilar types of paper were assessed by atomic force microscopy, spectrophotometer, and goniophotometer. The results indicate that the reproducibility depended considerably on the features of the paper. Although high whiteness and gloss increased the color gamut volume, and high roughness decreased the reproducibility of the printing machine, the artificial intelligence approach provided the opportunity to achieve a high gamut volume with low gloss and high roughness.Graphic abstract More... »

PAGES

285-293

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11998-020-00393-6

DOI

http://dx.doi.org/10.1007/s11998-020-00393-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1141917221


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/2008/09", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Engineering", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0912", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Materials Engineering", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Printing Science and Technology, Institute for Color Science and Technology, P. O. Box 16765-654, Tehran, Iran", 
          "id": "http://www.grid.ac/institutes/grid.459642.8", 
          "name": [
            "Department of Printing Science and Technology, Institute for Color Science and Technology, P. O. Box 16765-654, Tehran, Iran"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ataeefard", 
        "givenName": "Maryam", 
        "id": "sg:person.014054054551.09", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014054054551.09"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "School of Chemical, Petroleum, and Gas Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran", 
          "id": "http://www.grid.ac/institutes/grid.411748.f", 
          "name": [
            "School of Chemical, Petroleum, and Gas Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tilebon", 
        "givenName": "Seyyed Mohamad Sadati", 
        "id": "sg:person.012575630110.98", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012575630110.98"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s00396-018-4282-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101555495", 
          "https://doi.org/10.1007/s00396-018-4282-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-77744-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019082744", 
          "https://doi.org/10.1007/978-3-642-77744-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11998-018-0056-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101838299", 
          "https://doi.org/10.1007/s11998-018-0056-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-540-29900-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008909662", 
          "https://doi.org/10.1007/978-3-540-29900-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1134/s0965545x19050031", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1121262687", 
          "https://doi.org/10.1134/s0965545x19050031"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2021-10-15", 
    "datePublishedReg": "2021-10-15", 
    "description": "The color gamut of imaging media is significant for the reproduction of color images because its magnitude directly affects the degree to which colors change during the printing process. Over the last few years, digital impression technology has started to play a substantial role in the printing industry due to the quest for short runs and variable information printing. The color gamut of electrophotographic digital printing depends on various parameters including the printer and toner, but especially the properties (whiteness, roughness, and gloss) of the paper, which influence the final printed color gamut and replication quality. Artificial intelligence approaches are applied herein for the first time to choose and predict the performance of a paper with appropriate properties to achieve the maximum color gamut. A genetic algorithm-based computer code is developed to optimize the architecture of an artificial neural network, thereby yielding an accurate model to predict the color gamut achievable in electrophotographic color printing. The gamut volume was generated using an Eye-One spectrophotometer, ProfileMaker, and ColorThink software. The properties of 11 dissimilar types of paper were assessed by atomic force microscopy, spectrophotometer, and goniophotometer. The results indicate that the reproducibility depended considerably on the features of the paper. Although high whiteness and gloss increased the color gamut volume, and high roughness decreased the reproducibility of the printing machine, the artificial intelligence approach provided the opportunity to achieve a high gamut volume with low gloss and high roughness.Graphic abstract", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s11998-020-00393-6", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1412839", 
        "issn": [
          "1945-9645", 
          "1742-0261"
        ], 
        "name": "Journal of Coatings Technology and Research", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "19"
      }
    ], 
    "keywords": [
      "artificial intelligence approach", 
      "intelligence approach", 
      "artificial neural network", 
      "artificial intelligence", 
      "neural network", 
      "digital printing", 
      "color images", 
      "digital impression technology", 
      "maximum color gamut", 
      "accurate model", 
      "intelligence", 
      "dissimilar types", 
      "architecture", 
      "computer code", 
      "machine", 
      "software", 
      "network", 
      "gamut volume", 
      "color gamut", 
      "images", 
      "code", 
      "technology", 
      "color printing", 
      "gamut", 
      "printer", 
      "performance", 
      "features", 
      "printing", 
      "printing process", 
      "color gamut volume", 
      "quality", 
      "industry", 
      "model", 
      "opportunities", 
      "process", 
      "run", 
      "time", 
      "lessons", 
      "appropriate properties", 
      "volume", 
      "results", 
      "parameters", 
      "quest", 
      "goniophotometer", 
      "types", 
      "reproducibility", 
      "properties", 
      "degree", 
      "medium", 
      "Abstract", 
      "gloss", 
      "substantial role", 
      "first time", 
      "years", 
      "role", 
      "magnitude", 
      "changes", 
      "lower gloss", 
      "reproduction", 
      "short run", 
      "color change", 
      "toner", 
      "roughness", 
      "high whiteness", 
      "Graphic abstract", 
      "replication quality", 
      "whiteness", 
      "paper", 
      "atomic force microscopy", 
      "spectrophotometer", 
      "high roughness", 
      "approach", 
      "force microscopy", 
      "microscopy"
    ], 
    "name": "Seeking a paper for digital printing with maximum gamut volume: a lesson from artificial intelligence", 
    "pagination": "285-293", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1141917221"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11998-020-00393-6"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11998-020-00393-6", 
      "https://app.dimensions.ai/details/publication/pub.1141917221"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-05-20T07:38", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220519/entities/gbq_results/article/article_880.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s11998-020-00393-6"
  }
]
 

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.10.1007/s11998-020-00393-6'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11998-020-00393-6'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11998-020-00393-6'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11998-020-00393-6'


 

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

162 TRIPLES      22 PREDICATES      104 URIs      91 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11998-020-00393-6 schema:about anzsrc-for:09
2 anzsrc-for:0912
3 schema:author N8ac15a6e739a4571b760042f9eb1ac8a
4 schema:citation sg:pub.10.1007/978-3-540-29900-4
5 sg:pub.10.1007/978-3-642-77744-8
6 sg:pub.10.1007/s00396-018-4282-2
7 sg:pub.10.1007/s11998-018-0056-5
8 sg:pub.10.1134/s0965545x19050031
9 schema:datePublished 2021-10-15
10 schema:datePublishedReg 2021-10-15
11 schema:description The color gamut of imaging media is significant for the reproduction of color images because its magnitude directly affects the degree to which colors change during the printing process. Over the last few years, digital impression technology has started to play a substantial role in the printing industry due to the quest for short runs and variable information printing. The color gamut of electrophotographic digital printing depends on various parameters including the printer and toner, but especially the properties (whiteness, roughness, and gloss) of the paper, which influence the final printed color gamut and replication quality. Artificial intelligence approaches are applied herein for the first time to choose and predict the performance of a paper with appropriate properties to achieve the maximum color gamut. A genetic algorithm-based computer code is developed to optimize the architecture of an artificial neural network, thereby yielding an accurate model to predict the color gamut achievable in electrophotographic color printing. The gamut volume was generated using an Eye-One spectrophotometer, ProfileMaker, and ColorThink software. The properties of 11 dissimilar types of paper were assessed by atomic force microscopy, spectrophotometer, and goniophotometer. The results indicate that the reproducibility depended considerably on the features of the paper. Although high whiteness and gloss increased the color gamut volume, and high roughness decreased the reproducibility of the printing machine, the artificial intelligence approach provided the opportunity to achieve a high gamut volume with low gloss and high roughness.Graphic abstract
12 schema:genre article
13 schema:inLanguage en
14 schema:isAccessibleForFree false
15 schema:isPartOf Ndfe53e5baa9841e98bf5b5416139e002
16 Ne23a9974dd6f4701b10d18181adee2b4
17 sg:journal.1412839
18 schema:keywords Abstract
19 Graphic abstract
20 accurate model
21 approach
22 appropriate properties
23 architecture
24 artificial intelligence
25 artificial intelligence approach
26 artificial neural network
27 atomic force microscopy
28 changes
29 code
30 color change
31 color gamut
32 color gamut volume
33 color images
34 color printing
35 computer code
36 degree
37 digital impression technology
38 digital printing
39 dissimilar types
40 features
41 first time
42 force microscopy
43 gamut
44 gamut volume
45 gloss
46 goniophotometer
47 high roughness
48 high whiteness
49 images
50 industry
51 intelligence
52 intelligence approach
53 lessons
54 lower gloss
55 machine
56 magnitude
57 maximum color gamut
58 medium
59 microscopy
60 model
61 network
62 neural network
63 opportunities
64 paper
65 parameters
66 performance
67 printer
68 printing
69 printing process
70 process
71 properties
72 quality
73 quest
74 replication quality
75 reproducibility
76 reproduction
77 results
78 role
79 roughness
80 run
81 short run
82 software
83 spectrophotometer
84 substantial role
85 technology
86 time
87 toner
88 types
89 volume
90 whiteness
91 years
92 schema:name Seeking a paper for digital printing with maximum gamut volume: a lesson from artificial intelligence
93 schema:pagination 285-293
94 schema:productId N34031f1ff45348ca8bd3992bdcc3e1b0
95 N98faa31b0c5942cd9e1bf9f52256da63
96 schema:sameAs https://app.dimensions.ai/details/publication/pub.1141917221
97 https://doi.org/10.1007/s11998-020-00393-6
98 schema:sdDatePublished 2022-05-20T07:38
99 schema:sdLicense https://scigraph.springernature.com/explorer/license/
100 schema:sdPublisher N6852693660534e9897573c9084c15b57
101 schema:url https://doi.org/10.1007/s11998-020-00393-6
102 sgo:license sg:explorer/license/
103 sgo:sdDataset articles
104 rdf:type schema:ScholarlyArticle
105 N1373ccb29222436b9107f2c1c76bc127 rdf:first sg:person.012575630110.98
106 rdf:rest rdf:nil
107 N34031f1ff45348ca8bd3992bdcc3e1b0 schema:name dimensions_id
108 schema:value pub.1141917221
109 rdf:type schema:PropertyValue
110 N6852693660534e9897573c9084c15b57 schema:name Springer Nature - SN SciGraph project
111 rdf:type schema:Organization
112 N8ac15a6e739a4571b760042f9eb1ac8a rdf:first sg:person.014054054551.09
113 rdf:rest N1373ccb29222436b9107f2c1c76bc127
114 N98faa31b0c5942cd9e1bf9f52256da63 schema:name doi
115 schema:value 10.1007/s11998-020-00393-6
116 rdf:type schema:PropertyValue
117 Ndfe53e5baa9841e98bf5b5416139e002 schema:issueNumber 1
118 rdf:type schema:PublicationIssue
119 Ne23a9974dd6f4701b10d18181adee2b4 schema:volumeNumber 19
120 rdf:type schema:PublicationVolume
121 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
122 schema:name Engineering
123 rdf:type schema:DefinedTerm
124 anzsrc-for:0912 schema:inDefinedTermSet anzsrc-for:
125 schema:name Materials Engineering
126 rdf:type schema:DefinedTerm
127 sg:journal.1412839 schema:issn 1742-0261
128 1945-9645
129 schema:name Journal of Coatings Technology and Research
130 schema:publisher Springer Nature
131 rdf:type schema:Periodical
132 sg:person.012575630110.98 schema:affiliation grid-institutes:grid.411748.f
133 schema:familyName Tilebon
134 schema:givenName Seyyed Mohamad Sadati
135 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012575630110.98
136 rdf:type schema:Person
137 sg:person.014054054551.09 schema:affiliation grid-institutes:grid.459642.8
138 schema:familyName Ataeefard
139 schema:givenName Maryam
140 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014054054551.09
141 rdf:type schema:Person
142 sg:pub.10.1007/978-3-540-29900-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008909662
143 https://doi.org/10.1007/978-3-540-29900-4
144 rdf:type schema:CreativeWork
145 sg:pub.10.1007/978-3-642-77744-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019082744
146 https://doi.org/10.1007/978-3-642-77744-8
147 rdf:type schema:CreativeWork
148 sg:pub.10.1007/s00396-018-4282-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101555495
149 https://doi.org/10.1007/s00396-018-4282-2
150 rdf:type schema:CreativeWork
151 sg:pub.10.1007/s11998-018-0056-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101838299
152 https://doi.org/10.1007/s11998-018-0056-5
153 rdf:type schema:CreativeWork
154 sg:pub.10.1134/s0965545x19050031 schema:sameAs https://app.dimensions.ai/details/publication/pub.1121262687
155 https://doi.org/10.1134/s0965545x19050031
156 rdf:type schema:CreativeWork
157 grid-institutes:grid.411748.f schema:alternateName School of Chemical, Petroleum, and Gas Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran
158 schema:name School of Chemical, Petroleum, and Gas Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran
159 rdf:type schema:Organization
160 grid-institutes:grid.459642.8 schema:alternateName Department of Printing Science and Technology, Institute for Color Science and Technology, P. O. Box 16765-654, Tehran, Iran
161 schema:name Department of Printing Science and Technology, Institute for Color Science and Technology, P. O. Box 16765-654, Tehran, Iran
162 rdf:type schema:Organization
 




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


...