Ontology type: schema:ScholarlyArticle
2019-04
AUTHORSJi-Deok Song, Young-Gyu Kim, Tae-Hyoung Park
ABSTRACTIn this paper, we propose a solder joint defect type classification method for automatic optical inspection machines in the manufacturing system of printed circuit boards. The inspection procedure for the solder joint defect type classification consists of an offline stage, which sets the optimal feature extraction region, and an online stage which classifies a defect type. In the offline stage, we use an optimization technique, namely, the genetic algorithm, to optimize the feature extraction region. In this stage, the optimal feature extraction region for defect type classification is constructed automatically. In the online stage, feature extraction regions are used to segment the solder joint image after component image acquirement. We then extract various color features from the segmented feature extraction regions. Next, we use support vector machine, which is one of the machine learning model’s method to classify the solder joint defect type. To evaluate the performance of the proposed method, ten types of solder joint defects were used in an experiment. The experimental results verified the effectiveness of the method in terms of the recognition rate, and its convenience. More... »
PAGES1303-1313
http://scigraph.springernature.com/pub.10.1007/s00170-018-3022-6
DOIhttp://dx.doi.org/10.1007/s00170-018-3022-6
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1109991750
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/2008/0801",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Artificial Intelligence and Image Processing",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Information and Computing Sciences",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Chungbuk National University",
"id": "https://www.grid.ac/institutes/grid.254229.a",
"name": [
"Department of Control & Robot Eng., Chungbuk National University, Cheongju, South Korea"
],
"type": "Organization"
},
"familyName": "Song",
"givenName": "Ji-Deok",
"type": "Person"
},
{
"affiliation": {
"alternateName": "Chungbuk National University",
"id": "https://www.grid.ac/institutes/grid.254229.a",
"name": [
"Department of Control & Robot Eng., Chungbuk National University, Cheongju, South Korea"
],
"type": "Organization"
},
"familyName": "Kim",
"givenName": "Young-Gyu",
"type": "Person"
},
{
"affiliation": {
"alternateName": "Chungbuk National University",
"id": "https://www.grid.ac/institutes/grid.254229.a",
"name": [
"Department of Control & Robot Eng., Chungbuk National University, Cheongju, South Korea"
],
"type": "Organization"
},
"familyName": "Park",
"givenName": "Tae-Hyoung",
"id": "sg:person.016240054564.34",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016240054564.34"
],
"type": "Person"
}
],
"citation": [
{
"id": "https://doi.org/10.1016/j.ijleo.2012.12.030",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1014624971"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.rcim.2014.03.003",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1027221723"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.5302/j.icros.2015.14.0083",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036160051"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/11759966_125",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036421249",
"https://doi.org/10.1007/11759966_125"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/11759966_125",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036421249",
"https://doi.org/10.1007/11759966_125"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tcpmt.2011.2118208",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061560825"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tcpmt.2011.2168531",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061560958"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tcpmt.2012.2205149",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061561156"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tcpmt.2012.2231902",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061561282"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tcpmt.2015.2501284",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061561919"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tie.2006.885448",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061622663"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tpami.2011.153",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061744047"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.5302/j.icros.2015.15.0019",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1072754695"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tcpmt.2016.2638503",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1083781256"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/nnsp.1999.788121",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1093178998"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/icinfa.2009.5205058",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1093661394"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/cisp.2008.292",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1093666121"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.5302/j.icros.2017.17.0187",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1099902872"
],
"type": "CreativeWork"
}
],
"datePublished": "2019-04",
"datePublishedReg": "2019-04-01",
"description": "In this paper, we propose a solder joint defect type classification method for automatic optical inspection machines in the manufacturing system of printed circuit boards. The inspection procedure for the solder joint defect type classification consists of an offline stage, which sets the optimal feature extraction region, and an online stage which classifies a defect type. In the offline stage, we use an optimization technique, namely, the genetic algorithm, to optimize the feature extraction region. In this stage, the optimal feature extraction region for defect type classification is constructed automatically. In the online stage, feature extraction regions are used to segment the solder joint image after component image acquirement. We then extract various color features from the segmented feature extraction regions. Next, we use support vector machine, which is one of the machine learning model\u2019s method to classify the solder joint defect type. To evaluate the performance of the proposed method, ten types of solder joint defects were used in an experiment. The experimental results verified the effectiveness of the method in terms of the recognition rate, and its convenience.",
"genre": "research_article",
"id": "sg:pub.10.1007/s00170-018-3022-6",
"inLanguage": [
"en"
],
"isAccessibleForFree": false,
"isPartOf": [
{
"id": "sg:journal.1043671",
"issn": [
"0268-3768",
"1433-3015"
],
"name": "The International Journal of Advanced Manufacturing Technology",
"type": "Periodical"
},
{
"issueNumber": "5-8",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "101"
}
],
"name": "SMT defect classification by feature extraction region optimization and machine learning",
"pagination": "1303-1313",
"productId": [
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s00170-018-3022-6"
]
},
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"3b95099321bc986f99a8a68adfb7c8287d52572f8bdf301371ce0547aaaf632e"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1109991750"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s00170-018-3022-6",
"https://app.dimensions.ai/details/publication/pub.1109991750"
],
"sdDataset": "articles",
"sdDatePublished": "2019-04-15T09:11",
"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_56158_00000005.jsonl",
"type": "ScholarlyArticle",
"url": "https://link.springer.com/10.1007%2Fs00170-018-3022-6"
}
]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/s00170-018-3022-6'
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/s00170-018-3022-6'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00170-018-3022-6'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00170-018-3022-6'
This table displays all metadata directly associated to this object as RDF triples.
125 TRIPLES
21 PREDICATES
44 URIs
19 LITERALS
7 BLANK NODES