{"id":"https://openalex.org/W4226255349","doi":"https://doi.org/10.1017/s0890060421000330","title":"A deep learning-based approach to extraction of filler morphology in SEM images with the application of automated quality inspection","display_name":"A deep learning-based approach to extraction of filler morphology in SEM images with the application of automated quality inspection","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4226255349","doi":"https://doi.org/10.1017/s0890060421000330"},"language":"en","primary_location":{"id":"doi:10.1017/s0890060421000330","is_oa":false,"landing_page_url":"https://doi.org/10.1017/s0890060421000330","pdf_url":null,"source":{"id":"https://openalex.org/S4210193102","display_name":"Artificial intelligence for engineering design analysis and manufacturing","issn_l":"0890-0604","issn":["0890-0604","1469-1760"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310311721","host_organization_name":"Cambridge University Press","host_organization_lineage":["https://openalex.org/P4310311721","https://openalex.org/P4310311702"],"host_organization_lineage_names":["Cambridge University Press","University of Cambridge"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Artificial Intelligence for Engineering Design, Analysis and Manufacturing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://digitalcommons.chapman.edu/engineering_articles/139","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5013796419","display_name":"Md Fashiar Rahman","orcid":"https://orcid.org/0000-0002-0437-2587"},"institutions":[{"id":"https://openalex.org/I164936912","display_name":"The University of Texas at El Paso","ror":"https://ror.org/04d5vba33","country_code":"US","type":"education","lineage":["https://openalex.org/I164936912"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Md. Fashiar Rahman","raw_affiliation_strings":["Department of Industrial, Manufacturing and Systems Engineering, The University of Texas at El Paso, TX79968, USA"],"raw_orcid":"https://orcid.org/0000-0002-0437-2587","affiliations":[{"raw_affiliation_string":"Department of Industrial, Manufacturing and Systems Engineering, The University of Texas at El Paso, TX79968, USA","institution_ids":["https://openalex.org/I164936912"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058784524","display_name":"Tzu-Liang Tseng","orcid":"https://orcid.org/0000-0002-3903-529X"},"institutions":[{"id":"https://openalex.org/I164936912","display_name":"The University of Texas at El Paso","ror":"https://ror.org/04d5vba33","country_code":"US","type":"education","lineage":["https://openalex.org/I164936912"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tzu-Liang (Bill) Tseng","raw_affiliation_strings":["Department of Industrial, Manufacturing and Systems Engineering, The University of Texas, El Paso, TX79968, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Industrial, Manufacturing and Systems Engineering, The University of Texas, El Paso, TX79968, USA","institution_ids":["https://openalex.org/I164936912"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jianguo Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jianguo Wu","raw_affiliation_strings":["College of Engineering, Industrial and Systems Engineering, Peking University, Beijing100871, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Engineering, Industrial and Systems Engineering, Peking University, Beijing100871, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086326544","display_name":"Yuxin Wen","orcid":"https://orcid.org/0000-0002-2352-5622"},"institutions":[{"id":"https://openalex.org/I185071736","display_name":"Chapman University","ror":"https://ror.org/0452jzg20","country_code":"US","type":"education","lineage":["https://openalex.org/I185071736"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuxin Wen","raw_affiliation_strings":["Department of Electrical and Computer Science, Chapman University, Orange, CA92866, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Science, Chapman University, Orange, CA92866, USA","institution_ids":["https://openalex.org/I185071736"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055995399","display_name":"Yirong Lin","orcid":"https://orcid.org/0000-0001-8755-1996"},"institutions":[{"id":"https://openalex.org/I164936912","display_name":"The University of Texas at El Paso","ror":"https://ror.org/04d5vba33","country_code":"US","type":"education","lineage":["https://openalex.org/I164936912"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yirong Lin","raw_affiliation_strings":["Department of Mechanical Engineering, The University of Texas, El Paso, TX79968, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, The University of Texas, El Paso, TX79968, USA","institution_ids":["https://openalex.org/I164936912"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":0.2709,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.60288217,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"36","issue":null,"first_page":null,"last_page":null},"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/T12039","display_name":"Electron and X-Ray Spectroscopy Techniques","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/2508","display_name":"Surfaces, Coatings and Films"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9943000078201294,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7423095703125},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6502825021743774},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6343274116516113},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6125215291976929},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5875925421714783},{"id":"https://openalex.org/keywords/mathematical-morphology","display_name":"Mathematical morphology","score":0.5811117887496948},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5626013278961182},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5410240292549133},{"id":"https://openalex.org/keywords/filler","display_name":"Filler (materials)","score":0.47205692529678345},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4177754819393158},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4160851538181305},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.3233450651168823},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.31056106090545654},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2103745937347412},{"id":"https://openalex.org/keywords/composite-material","display_name":"Composite material","score":0.1505778729915619}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7423095703125},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6502825021743774},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6343274116516113},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6125215291976929},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5875925421714783},{"id":"https://openalex.org/C185568154","wikidata":"https://www.wikidata.org/wiki/Q530242","display_name":"Mathematical morphology","level":4,"score":0.5811117887496948},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5626013278961182},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5410240292549133},{"id":"https://openalex.org/C2778223994","wikidata":"https://www.wikidata.org/wiki/Q905592","display_name":"Filler (materials)","level":2,"score":0.47205692529678345},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4177754819393158},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4160851538181305},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3233450651168823},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.31056106090545654},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2103745937347412},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.1505778729915619},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1017/s0890060421000330","is_oa":false,"landing_page_url":"https://doi.org/10.1017/s0890060421000330","pdf_url":null,"source":{"id":"https://openalex.org/S4210193102","display_name":"Artificial intelligence for engineering design analysis and manufacturing","issn_l":"0890-0604","issn":["0890-0604","1469-1760"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310311721","host_organization_name":"Cambridge University Press","host_organization_lineage":["https://openalex.org/P4310311721","https://openalex.org/P4310311702"],"host_organization_lineage_names":["Cambridge University Press","University of Cambridge"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Artificial Intelligence for Engineering Design, Analysis and Manufacturing","raw_type":"journal-article"},{"id":"pmh:oai:digitalcommons.chapman.edu:engineering_articles-1138","is_oa":true,"landing_page_url":"https://digitalcommons.chapman.edu/engineering_articles/139","pdf_url":null,"source":{"id":"https://openalex.org/S4377196324","display_name":"Chapman University Digital Commons (Chapman University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I185071736","host_organization_name":"Chapman University","host_organization_lineage":["https://openalex.org/I185071736"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Engineering Faculty Articles and Research","raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:digitalcommons.chapman.edu:engineering_articles-1138","is_oa":true,"landing_page_url":"https://digitalcommons.chapman.edu/engineering_articles/139","pdf_url":null,"source":{"id":"https://openalex.org/S4377196324","display_name":"Chapman University Digital Commons (Chapman University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I185071736","host_organization_name":"Chapman University","host_organization_lineage":["https://openalex.org/I185071736"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Engineering Faculty Articles and Research","raw_type":"text"},"sustainable_development_goals":[{"score":0.6000000238418579,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G3823657967","display_name":null,"funder_award_id":"51875003","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4195747565","display_name":null,"funder_award_id":"ERC-ASPIRE-1941524","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5050068621","display_name":null,"funder_award_id":"ECR-PEER-1935454","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7236899727","display_name":null,"funder_award_id":"71932006","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W437014240","https://openalex.org/W1536680647","https://openalex.org/W1861492603","https://openalex.org/W1889208159","https://openalex.org/W1965696824","https://openalex.org/W1971238182","https://openalex.org/W1987143479","https://openalex.org/W1995376165","https://openalex.org/W1999478155","https://openalex.org/W2004019879","https://openalex.org/W2012279278","https://openalex.org/W2017443491","https://openalex.org/W2020572552","https://openalex.org/W2022382877","https://openalex.org/W2027592523","https://openalex.org/W2030727108","https://openalex.org/W2048033112","https://openalex.org/W2054455822","https://openalex.org/W2073097449","https://openalex.org/W2085302376","https://openalex.org/W2087178876","https://openalex.org/W2098976465","https://openalex.org/W2100690466","https://openalex.org/W2102605133","https://openalex.org/W2109517492","https://openalex.org/W2116707521","https://openalex.org/W2118372964","https://openalex.org/W2121947440","https://openalex.org/W2130006024","https://openalex.org/W2132763323","https://openalex.org/W2140327046","https://openalex.org/W2158074918","https://openalex.org/W2167564912","https://openalex.org/W2322870089","https://openalex.org/W2336082409","https://openalex.org/W2395395188","https://openalex.org/W2557728737","https://openalex.org/W2565639579","https://openalex.org/W2609727186","https://openalex.org/W2611942203","https://openalex.org/W2783834309","https://openalex.org/W2794284562","https://openalex.org/W2892910691","https://openalex.org/W2897593716","https://openalex.org/W2950800384","https://openalex.org/W2963150697","https://openalex.org/W2963698633","https://openalex.org/W2992904262","https://openalex.org/W2997004889","https://openalex.org/W3129251458","https://openalex.org/W3150149828","https://openalex.org/W4236489423","https://openalex.org/W4254779640","https://openalex.org/W6639688338","https://openalex.org/W6714138976","https://openalex.org/W6771252890"],"related_works":["https://openalex.org/W2357143406","https://openalex.org/W2372753117","https://openalex.org/W2349459011","https://openalex.org/W1658259736","https://openalex.org/W2058740282","https://openalex.org/W2041406494","https://openalex.org/W2163448995","https://openalex.org/W2375111338","https://openalex.org/W2220864757","https://openalex.org/W2352304637"],"abstract_inverted_index":{"Abstract":[0],"Automatic":[1],"extraction":[2],"of":[3,31,39,53,117,180],"filler":[4,32,86,145],"morphology":[5,33,115],"(size,":[6],"orientation,":[7],"and":[8,42,55,57,105,119,163,173,178,189],"spatial":[9],"distribution)":[10],"in":[11,19,27,61,78,89,108,170],"Scanning":[12],"Electron":[13],"Microscopic":[14],"(SEM)":[15],"images":[16],"is":[17,45,148,155],"essential":[18],"many":[20],"applications":[21],"such":[22],"as":[23],"automatic":[24,85,120],"quality":[25,121],"inspection":[26],"composite":[28],"manufacturing.":[29],"Extraction":[30],"greatly":[34],"depends":[35],"on":[36,150],"accurate":[37],"segmentation":[38],"fillers":[40,107,118,165],"(fibers":[41],"particles),":[43],"which":[44],"a":[46,93,126,139],"challenging":[47],"task":[48],"due":[49],"to":[50,71,132],"the":[51,134,151,158,171,181],"overlap":[52],"fibers":[54],"particles":[56],"their":[58],"obscure":[59,174],"presence":[60],"SEM":[62,90,109,128],"images.":[63,80,153,192],"Convolution":[64],"Neural":[65],"Networks":[66],"(CNNs)":[67],"have":[68],"been":[69],"shown":[70,156],"be":[72],"very":[73],"effective":[74],"at":[75],"object":[76],"recognition":[77],"digital":[79],"This":[81],"paper":[82],"proposes":[83],"an":[84],"detection":[87,146],"system":[88,100,147,183],"images,":[91,110],"utilizing":[92],"Mask":[94],"Region-based":[95],"CNN":[96,141],"architecture.":[97,142],"The":[98,143,176],"proposed":[99,144,182],"can":[101,161],"simultaneously":[102],"classify,":[103],"detect,":[104],"segment":[106,164],"making":[111],"it":[112],"suitable":[113],"for":[114,137],"analysis":[116],"inspection.":[122],"We":[123],"also":[124],"propose":[125],"novel":[127],"image":[129],"simulation":[130],"procedure":[131],"overcome":[133],"data":[135],"scarcity":[136],"training":[138],"deep":[140],"trained":[149,159],"simulated":[152,188],"It":[154],"that":[157],"network":[160],"detect":[162],"with":[166],"higher":[167],"accuracy":[168],"even":[169],"overlapping":[172],"situations.":[175],"performance":[177],"robustness":[179],"are":[184],"evaluated":[185],"using":[186],"both":[187],"real":[190],"microscopic":[191]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}