{"id":"https://openalex.org/W3041068296","doi":"https://doi.org/10.1109/metroind4.0iot48571.2020.9138205","title":"Quality Assurance of Weld Seams Using Laser Triangulation Imaging and Deep Neural Networks","display_name":"Quality Assurance of Weld Seams Using Laser Triangulation Imaging and Deep Neural Networks","publication_year":2020,"publication_date":"2020-06-01","ids":{"openalex":"https://openalex.org/W3041068296","doi":"https://doi.org/10.1109/metroind4.0iot48571.2020.9138205","mag":"3041068296"},"language":"en","primary_location":{"id":"doi:10.1109/metroind4.0iot48571.2020.9138205","is_oa":false,"landing_page_url":"https://doi.org/10.1109/metroind4.0iot48571.2020.9138205","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Workshop on Metrology for Industry 4.0 &amp; IoT","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2209.13648","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5042665477","display_name":"Andreas Spruck","orcid":null},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Andreas Spruck","raw_affiliation_strings":["Multimedia Communications and Signal Processing, University of Erlangen-N\u00fcrnberg, Erlangen, Germany"],"affiliations":[{"raw_affiliation_string":"Multimedia Communications and Signal Processing, University of Erlangen-N\u00fcrnberg, Erlangen, Germany","institution_ids":["https://openalex.org/I181369854"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101633542","display_name":"J\u00fcrgen Seiler","orcid":"https://orcid.org/0000-0002-3016-110X"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Jurgen Seiler","raw_affiliation_strings":["Multimedia Communications and Signal Processing, University of Erlangen-N\u00fcrnberg, Erlangen, Germany"],"affiliations":[{"raw_affiliation_string":"Multimedia Communications and Signal Processing, University of Erlangen-N\u00fcrnberg, Erlangen, Germany","institution_ids":["https://openalex.org/I181369854"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108601827","display_name":"Michael Roll","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Michael Roll","raw_affiliation_strings":["Autotech Engineering Deutschland GmbH, Bielefeld, Germany"],"affiliations":[{"raw_affiliation_string":"Autotech Engineering Deutschland GmbH, Bielefeld, Germany","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044879143","display_name":"Thomas Dudziak","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Thomas Dudziak","raw_affiliation_strings":["Autotech Engineering Deutschland GmbH, Bielefeld, Germany"],"affiliations":[{"raw_affiliation_string":"Autotech Engineering Deutschland GmbH, Bielefeld, Germany","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049624278","display_name":"Jurgen Eckstein","orcid":null},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Jurgen Eckstein","raw_affiliation_strings":["Multimedia Communications and Signal Processing, University of Erlangen-N\u00fcrnberg, Erlangen, Germany"],"affiliations":[{"raw_affiliation_string":"Multimedia Communications and Signal Processing, University of Erlangen-N\u00fcrnberg, Erlangen, Germany","institution_ids":["https://openalex.org/I181369854"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062850220","display_name":"Andr\u00e9 Kaup","orcid":"https://orcid.org/0000-0002-0929-5074"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Andre Kaup","raw_affiliation_strings":["Multimedia Communications and Signal Processing, University of Erlangen-N\u00fcrnberg, Erlangen, Germany"],"affiliations":[{"raw_affiliation_string":"Multimedia Communications and Signal Processing, University of Erlangen-N\u00fcrnberg, Erlangen, Germany","institution_ids":["https://openalex.org/I181369854"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5042665477"],"corresponding_institution_ids":["https://openalex.org/I181369854"],"apc_list":null,"apc_paid":null,"fwci":0.5411,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.73424553,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"407","last_page":"412"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10834","display_name":"Welding Techniques and Residual Stresses","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13049","display_name":"Surface Roughness and Optical Measurements","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6786623001098633},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6492555141448975},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5853877067565918},{"id":"https://openalex.org/keywords/welding","display_name":"Welding","score":0.5801424980163574},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.550549328327179},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.45846235752105713},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.44412797689437866},{"id":"https://openalex.org/keywords/visual-inspection","display_name":"Visual inspection","score":0.4265350103378296},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.41506022214889526},{"id":"https://openalex.org/keywords/automated-optical-inspection","display_name":"Automated optical inspection","score":0.4121943712234497},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3748427629470825},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1985732913017273}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6786623001098633},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6492555141448975},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5853877067565918},{"id":"https://openalex.org/C19474535","wikidata":"https://www.wikidata.org/wiki/Q131172","display_name":"Welding","level":2,"score":0.5801424980163574},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.550549328327179},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.45846235752105713},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.44412797689437866},{"id":"https://openalex.org/C168820333","wikidata":"https://www.wikidata.org/wiki/Q448889","display_name":"Visual inspection","level":2,"score":0.4265350103378296},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.41506022214889526},{"id":"https://openalex.org/C164830781","wikidata":"https://www.wikidata.org/wiki/Q787330","display_name":"Automated optical inspection","level":2,"score":0.4121943712234497},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3748427629470825},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1985732913017273},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/metroind4.0iot48571.2020.9138205","is_oa":false,"landing_page_url":"https://doi.org/10.1109/metroind4.0iot48571.2020.9138205","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Workshop on Metrology for Industry 4.0 &amp; IoT","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2209.13648","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2209.13648","pdf_url":"https://arxiv.org/pdf/2209.13648","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2209.13648","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2209.13648","pdf_url":"https://arxiv.org/pdf/2209.13648","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1969883785","https://openalex.org/W1983438078","https://openalex.org/W2116419385","https://openalex.org/W2163605009","https://openalex.org/W2183341477","https://openalex.org/W2619004999","https://openalex.org/W2623496481","https://openalex.org/W2624090857","https://openalex.org/W2887669860","https://openalex.org/W2900198492","https://openalex.org/W2963751309","https://openalex.org/W2968194286","https://openalex.org/W2968932132","https://openalex.org/W6684191040","https://openalex.org/W6765770620"],"related_works":["https://openalex.org/W1560398276","https://openalex.org/W3149631139","https://openalex.org/W1979172994","https://openalex.org/W2154087496","https://openalex.org/W571879","https://openalex.org/W2104094426","https://openalex.org/W3152480963","https://openalex.org/W2099049350","https://openalex.org/W1986703546","https://openalex.org/W3172970281"],"abstract_inverted_index":{"In":[0,82],"this":[1,86],"paper,":[2],"a":[3,48,108,155],"novel":[4],"optical":[5,147],"inspection":[6,121,148],"system":[7,28,46,153,180,211],"is":[8,11,29,79,139,181,197],"presented":[9,202],"that":[10],"directly":[12,189],"suitable":[13],"for":[14,54,208],"Industry":[15],"4.0":[16],"and":[17,35,64,74,158,165],"the":[18,23,55,71,77,83,98,105,145,151,178,182,187,191,200,205,209],"implementation":[19],"on":[20],"IoT-devices":[21],"controlling":[22],"manufacturing":[24,192],"process.":[25],"The":[26,44],"proposed":[27,152,179,210],"capable":[30],"of":[31,76,85,107,122,134,144,177],"distinguishing":[32],"between":[33],"erroneous":[34],"faultless":[36],"weld":[37,59,125],"seams,":[38],"without":[39],"explicitly":[40],"defining":[41],"measurement":[42],"criteria.":[43],"developed":[45],"uses":[47],"deep":[49,109],"neural":[50,110],"network":[51],"based":[52],"classifier":[53],"class":[56],"prediction.":[57],"A":[58,174],"seam":[60],"dataset":[61],"was":[62],"acquired":[63],"labelled":[65],"by":[66],"an":[67,120,159],"expert":[68],"committee.":[69],"Thereby,":[70],"visual":[72],"impression":[73],"assessment":[75],"experts":[78],"learnt":[80],"accurately.":[81],"scope":[84],"paper":[87],"laser":[88],"triangulation":[89],"images":[90,99],"are":[91,116],"used.":[92],"Due":[93],"to":[94,103,118,136,141,169,172],"their":[95],"special":[96],"characteristics,":[97],"must":[100],"be":[101],"pre-processed":[102],"enable":[104,119],"use":[106],"network.":[111],"Furthermore,":[112,194],"two":[113],"different":[114],"approaches":[115,128],"investigated":[117],"differently":[123],"sized":[124],"seams.":[126],"Both":[127],"yield":[129],"very":[130],"high":[131],"classification":[132],"accuracies":[133],"up":[135],"96.88%,":[137],"which":[138],"competitive":[140],"current":[142],"state":[143],"art":[146],"systems.":[149],"Moreover,":[150],"enables":[154],"higher":[156],"flexibility":[157],"increased":[160],"robustness":[161],"towards":[162],"systematic":[163],"errors":[164],"environmental":[166],"conditions":[167],"due":[168],"its":[170],"ability":[171],"generalize.":[173],"further":[175],"benefit":[176],"fast":[183],"decision":[184],"process":[185],"enabling":[186],"usage":[188],"within":[190],"line.":[193],"standard":[195],"hardware":[196],"used":[198],"throughout":[199],"whole":[201],"work,":[203],"keeping":[204],"roll-out":[206],"costs":[207],"as":[212,214],"low":[213],"possible.":[215]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
