{"id":"https://openalex.org/W4318587331","doi":"https://doi.org/10.3390/pr11020391","title":"Prediction of the Ultimate Tensile Strength (UTS) of Asymmetric Friction Stir Welding Using Ensemble Machine Learning Methods","display_name":"Prediction of the Ultimate Tensile Strength (UTS) of Asymmetric Friction Stir Welding Using Ensemble Machine Learning Methods","publication_year":2023,"publication_date":"2023-01-27","ids":{"openalex":"https://openalex.org/W4318587331","doi":"https://doi.org/10.3390/pr11020391"},"language":"en","primary_location":{"id":"doi:10.3390/pr11020391","doi":"https://doi.org/10.3390/pr11020391","is_oa":true,"landing_page_url":"https://doi.org/10.3390/pr11020391","pdf_url":"https://www.mdpi.com/2227-9717/11/2/391/pdf?version=1676536467","source":{"id":"https://openalex.org/S4210201879","display_name":"Processes","issn_l":"2227-9717","issn":["2227-9717"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","poland-70","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Processes","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2227-9717/11/2/391/pdf?version=1676536467","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5054399378","display_name":"Surasak Matitopanum","orcid":"https://orcid.org/0009-0003-3380-6879","observed_orcids":["https://orcid.org/0009-0003-3380-6879"]},"institutions":[{"id":"https://openalex.org/I4405277981","display_name":"Ratchathani University","ror":"https://ror.org/04hbw0v61","country_code":"TH","type":"education","lineage":["https://openalex.org/I4405277981"]},{"id":"https://openalex.org/I72091625","display_name":"Ubon Ratchathani University","ror":"https://ror.org/045nemn19","country_code":"TH","type":"education","lineage":["https://openalex.org/I72091625"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Surasak Matitopanum","raw_affiliation_strings":["Department of Industrial Engineering, Ubon Ratchathani University, 85 Sathonlamark, Muang SriKai, Warin Chamrap, Ubon Ratchathani 34190, Thailand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering, Ubon Ratchathani University, 85 Sathonlamark, Muang SriKai, Warin Chamrap, Ubon Ratchathani 34190, Thailand","institution_ids":["https://openalex.org/I4405277981","https://openalex.org/I72091625"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053637817","display_name":"Rapeepan Pitakaso","orcid":"https://orcid.org/0000-0002-5896-4895","observed_orcids":["https://orcid.org/0000-0002-5896-4895"]},"institutions":[{"id":"https://openalex.org/I4405277981","display_name":"Ratchathani University","ror":"https://ror.org/04hbw0v61","country_code":"TH","type":"education","lineage":["https://openalex.org/I4405277981"]},{"id":"https://openalex.org/I72091625","display_name":"Ubon Ratchathani University","ror":"https://ror.org/045nemn19","country_code":"TH","type":"education","lineage":["https://openalex.org/I72091625"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Rapeepan Pitakaso","raw_affiliation_strings":["Department of Industrial Engineering, Ubon Ratchathani University, 85 Sathonlamark, Muang SriKai, Warin Chamrap, Ubon Ratchathani 34190, Thailand"],"raw_orcid":"https://orcid.org/0000-0002-5896-4895","affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering, Ubon Ratchathani University, 85 Sathonlamark, Muang SriKai, Warin Chamrap, Ubon Ratchathani 34190, Thailand","institution_ids":["https://openalex.org/I4405277981","https://openalex.org/I72091625"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072294176","display_name":"Kanchana Sethanan","orcid":"https://orcid.org/0000-0002-3340-2538","observed_orcids":["https://orcid.org/0000-0002-3340-2538"]},"institutions":[{"id":"https://openalex.org/I179193067","display_name":"Khon Kaen University","ror":"https://ror.org/03cq4gr50","country_code":"TH","type":"education","lineage":["https://openalex.org/I179193067"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Kanchana Sethanan","raw_affiliation_strings":["Research Unit on System Modelling for Industry, Department of Industrial Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand"],"raw_orcid":"https://orcid.org/0000-0002-3340-2538","affiliations":[{"raw_affiliation_string":"Research Unit on System Modelling for Industry, Department of Industrial Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand","institution_ids":["https://openalex.org/I179193067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082371549","display_name":"Thanatkij Srichok","orcid":"https://orcid.org/0000-0001-7720-7630","observed_orcids":["https://orcid.org/0000-0001-7720-7630"]},"institutions":[{"id":"https://openalex.org/I4405277981","display_name":"Ratchathani University","ror":"https://ror.org/04hbw0v61","country_code":"TH","type":"education","lineage":["https://openalex.org/I4405277981"]},{"id":"https://openalex.org/I72091625","display_name":"Ubon Ratchathani University","ror":"https://ror.org/045nemn19","country_code":"TH","type":"education","lineage":["https://openalex.org/I72091625"]}],"countries":["TH"],"is_corresponding":true,"raw_author_name":"Thanatkij Srichok","raw_affiliation_strings":["Department of Industrial Engineering, Ubon Ratchathani University, 85 Sathonlamark, Muang SriKai, Warin Chamrap, Ubon Ratchathani 34190, Thailand"],"raw_orcid":"https://orcid.org/0000-0001-7720-7630","affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering, Ubon Ratchathani University, 85 Sathonlamark, Muang SriKai, Warin Chamrap, Ubon Ratchathani 34190, Thailand","institution_ids":["https://openalex.org/I4405277981","https://openalex.org/I72091625"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057708169","display_name":"Peerawat Chokanat","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405277981","display_name":"Ratchathani University","ror":"https://ror.org/04hbw0v61","country_code":"TH","type":"education","lineage":["https://openalex.org/I4405277981"]},{"id":"https://openalex.org/I72091625","display_name":"Ubon Ratchathani University","ror":"https://ror.org/045nemn19","country_code":"TH","type":"education","lineage":["https://openalex.org/I72091625"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Peerawat Chokanat","raw_affiliation_strings":["Artificial Intelligence Optimization SMART Laboratory, Department of Industrial Engineering, Faculty of Engineering, Ubon Ratchathani University, 85 Sathonlamark, Muang SriKai, Warin Chamrap, Ubon Ratchathani 34190, Thailand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Artificial Intelligence Optimization SMART Laboratory, Department of Industrial Engineering, Faculty of Engineering, Ubon Ratchathani University, 85 Sathonlamark, Muang SriKai, Warin Chamrap, Ubon Ratchathani 34190, Thailand","institution_ids":["https://openalex.org/I4405277981","https://openalex.org/I72091625"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5082371549"],"corresponding_institution_ids":["https://openalex.org/I4405277981","https://openalex.org/I72091625"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2224},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2224},"fwci":3.9567,"has_fulltext":true,"cited_by_count":41,"citation_normalized_percentile":{"value":0.95055246,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"11","issue":"2","first_page":"391","last_page":"391"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10723","display_name":"Advanced Welding Techniques Analysis","score":0.9994999766349792,"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"}},"topics":[{"id":"https://openalex.org/T10723","display_name":"Advanced Welding Techniques Analysis","score":0.9994999766349792,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.0003000000142492354,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"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/T10834","display_name":"Welding Techniques and Residual Stresses","score":0.0,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/friction-stir-welding","display_name":"friction stir welding","score":0.9399999976158142},{"id":"https://openalex.org/keywords/ultimate-tensile-strength-prediction","display_name":"ultimate tensile strength prediction","score":0.9829999804496765},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"ensemble learning","score":0.8519999980926514},{"id":"https://openalex.org/keywords/gaussian-process-regression","display_name":"Gaussian process regression","score":0.8960000276565552},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"support vector machine","score":0.9980000257492065},{"id":"https://openalex.org/keywords/differential-evolution","display_name":"differential evolution","score":0.6399999856948853},{"id":"https://openalex.org/keywords/decision-level-fusion","display_name":"decision fusion","score":0.8920000195503235},{"id":"https://openalex.org/keywords/aluminum-alloys","display_name":"aluminum alloys","score":0.5540000200271606},{"id":"https://openalex.org/keywords/5083-aluminum-alloy","display_name":"5083 aluminum alloy","score":0.9769999980926514},{"id":"https://openalex.org/keywords/welding-parameter-optimization","display_name":"welding parameter optimization","score":0.5649999976158142}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7237871289253235},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.673989474773407},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6549869775772095},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6529957056045532},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.6297380924224854},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5914197564125061},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5445369482040405},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5424723029136658},{"id":"https://openalex.org/C81692654","wikidata":"https://www.wikidata.org/wiki/Q225926","display_name":"Kriging","level":2,"score":0.5358845591545105},{"id":"https://openalex.org/C40367268","wikidata":"https://www.wikidata.org/wiki/Q2068127","display_name":"Friction stir welding","level":3,"score":0.5036930441856384},{"id":"https://openalex.org/C112950240","wikidata":"https://www.wikidata.org/wiki/Q76005","display_name":"Ultimate tensile strength","level":2,"score":0.49101021885871887},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.48930785059928894},{"id":"https://openalex.org/C19474535","wikidata":"https://www.wikidata.org/wiki/Q131172","display_name":"Welding","level":2,"score":0.4584580957889557},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.17657604813575745},{"id":"https://openalex.org/C191897082","wikidata":"https://www.wikidata.org/wiki/Q11467","display_name":"Metallurgy","level":1,"score":0.0731637179851532}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3390/pr11020391","doi":"https://doi.org/10.3390/pr11020391","is_oa":true,"landing_page_url":"https://doi.org/10.3390/pr11020391","pdf_url":"https://www.mdpi.com/2227-9717/11/2/391/pdf?version=1676536467","source":{"id":"https://openalex.org/S4210201879","display_name":"Processes","issn_l":"2227-9717","issn":["2227-9717"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","poland-70","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Processes","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.3390/pr11020391","doi":"https://doi.org/10.3390/pr11020391","is_oa":true,"landing_page_url":"https://doi.org/10.3390/pr11020391","pdf_url":"https://www.mdpi.com/2227-9717/11/2/391/pdf?version=1676536467","source":{"id":"https://openalex.org/S4210201879","display_name":"Processes","issn_l":"2227-9717","issn":["2227-9717"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","poland-70","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Processes","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.4943999946117401,"id":"https://metadata.un.org/sdg/9"}],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320328544","display_name":"Khon Kean University","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4318587331.pdf","grobid_xml":"https://content.openalex.org/works/W4318587331.grobid-xml"},"referenced_works_count":77,"referenced_works":["https://openalex.org/W920554106","https://openalex.org/W1506819938","https://openalex.org/W1746819321","https://openalex.org/W1974745237","https://openalex.org/W1985390702","https://openalex.org/W1988758821","https://openalex.org/W1997052242","https://openalex.org/W1999440248","https://openalex.org/W2015077476","https://openalex.org/W2017831723","https://openalex.org/W2019117580","https://openalex.org/W2047632198","https://openalex.org/W2048174350","https://openalex.org/W2066925537","https://openalex.org/W2101269184","https://openalex.org/W2150841456","https://openalex.org/W2181435063","https://openalex.org/W2191596536","https://openalex.org/W2343210681","https://openalex.org/W2346911154","https://openalex.org/W2467043830","https://openalex.org/W2497091427","https://openalex.org/W2523282407","https://openalex.org/W2526351474","https://openalex.org/W2556231193","https://openalex.org/W2595061000","https://openalex.org/W2607974993","https://openalex.org/W2758587229","https://openalex.org/W2779432440","https://openalex.org/W2791263920","https://openalex.org/W2807563986","https://openalex.org/W2886003185","https://openalex.org/W2887175204","https://openalex.org/W2889559672","https://openalex.org/W2897254107","https://openalex.org/W2902756961","https://openalex.org/W2905033075","https://openalex.org/W2921294189","https://openalex.org/W2935089237","https://openalex.org/W2936744098","https://openalex.org/W2946018869","https://openalex.org/W3012175223","https://openalex.org/W3045269176","https://openalex.org/W3048123707","https://openalex.org/W3049100352","https://openalex.org/W3082264903","https://openalex.org/W3092636098","https://openalex.org/W3127109848","https://openalex.org/W3162027826","https://openalex.org/W3164826834","https://openalex.org/W3187725725","https://openalex.org/W3194700853","https://openalex.org/W3206949445","https://openalex.org/W3212596928","https://openalex.org/W4205312762","https://openalex.org/W4211049957","https://openalex.org/W4225006677","https://openalex.org/W4225794811","https://openalex.org/W4226100150","https://openalex.org/W4229008876","https://openalex.org/W4232478844","https://openalex.org/W4280548187","https://openalex.org/W4285195707","https://openalex.org/W4286283014","https://openalex.org/W4286433609","https://openalex.org/W4286433693","https://openalex.org/W4286566374","https://openalex.org/W4293218294","https://openalex.org/W4297973345","https://openalex.org/W4307261888","https://openalex.org/W4310072448","https://openalex.org/W4313219554","https://openalex.org/W4313414694","https://openalex.org/W4404843399","https://openalex.org/W6685963704","https://openalex.org/W6727457170","https://openalex.org/W6945234997"],"related_works":["https://openalex.org/W4413447718","https://openalex.org/W4378551988","https://openalex.org/W4226100150","https://openalex.org/W4376644603","https://openalex.org/W7203598294","https://openalex.org/W4318815860","https://openalex.org/W4406064180","https://openalex.org/W2889559672","https://openalex.org/W4408217879","https://openalex.org/W4417421221"],"abstract_inverted_index":{"This":[0],"research":[1],"aims":[2],"to":[3,42,84,101,155],"develop":[4],"ensemble":[5,36,87,212],"machine-learning":[6],"methods":[7],"for":[8,105,137,143,206],"forecasting":[9],"the":[10,24,44,47,59,69,75,86,103,106,112,118,138,144,148,152,157,165,184,203],"ultimate":[11],"tensile":[12],"strength":[13],"(UTS)":[14],"of":[15,29,46,81,117,200],"friction":[16,48],"stir":[17],"welding":[18],"(FSW).":[19],"The":[20,65,115,125,160],"substance":[21],"utilized":[22],"in":[23],"experiment":[25],"was":[26,40,109,120],"a":[27,62,97],"mixture":[28],"aluminum":[30],"alloys":[31],"AA5083":[32],"and":[33,58,74,127,141,183,187,194],"AA5061.":[34],"An":[35],"machine":[37,78,82,88],"learning":[38,83,89,213],"model":[39,80,119,167],"created":[41],"predict":[43],"UTS":[45,60],"stir-welded":[49],"seam,":[50],"utilizing":[51],"11":[52],"FSW":[53],"parameters":[54],"as":[55,61,176],"input":[56],"factors":[57],"response":[63],"variable.":[64],"proposed":[66,113,166],"approach":[67],"used":[68],"Gaussian":[70],"process":[71],"regression":[72],"(GPR)":[73],"support":[76],"vector":[77],"(SVM)":[79],"build":[85],"model.":[90,114],"In":[91,198],"addition,":[92],"an":[93],"efficient":[94],"technique":[95,205],"using":[96,122],"differential":[98],"evolution":[99],"algorithm":[100],"optimize":[102],"weight":[104],"decision":[107,207],"fusion":[108,208],"incorporated":[110],"into":[111,132],"effectiveness":[116],"evaluated":[121],"three":[123],"datasets.":[124],"first":[126],"second":[128],"datasets":[129],"were":[130],"divided":[131],"two":[133],"groups,":[134],"with":[135],"80%":[136],"training":[139],"dataset":[140,150],"20%":[142],"testing":[145],"dataset,":[146],"while":[147],"third":[149],"comprised":[151],"test":[153],"data":[154],"validate":[156],"model\u2019s":[158],"accuracy.":[159],"computational":[161],"results":[162],"indicated":[163],"that":[164],"provides":[168],"more":[169],"accurate":[170],"forecasts":[171],"than":[172],"existing":[173],"methods,":[174],"such":[175],"random":[177],"forest,":[178],"gradient":[179],"boosting,":[180,182],"ADA":[181],"original":[185],"SVM":[186],"GPR,":[188],"by":[189,215],"30.67,":[190],"49.18,":[191],"16.50,":[192],"48.87,":[193],"49.33":[195],"%,":[196],"respectively.":[197],"terms":[199],"prediction":[201],"accuracy,":[202],"suggested":[204],"surpasses":[209],"unweighted":[210],"average":[211],"(UWE)":[214],"10.32%.":[216]},"counts_by_year":[{"year":2026,"cited_by_count":12},{"year":2025,"cited_by_count":19},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":4}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2023-01-31T00:00:00"}