{"id":"https://openalex.org/W4385065293","doi":"https://doi.org/10.1080/10910344.2023.2235610","title":"Multi-objective optimization based on machine learning and non-dominated sorting genetic algorithm for surface roughness and tool wear in Ti 6 Al 4 V turning","display_name":"Multi-objective optimization based on machine learning and non-dominated sorting genetic algorithm for surface roughness and tool wear in Ti 6 Al 4 V turning","publication_year":2023,"publication_date":"2023-07-04","ids":{"openalex":"https://openalex.org/W4385065293","doi":"https://doi.org/10.1080/10910344.2023.2235610"},"language":"en","primary_location":{"id":"doi:10.1080/10910344.2023.2235610","doi":"https://doi.org/10.1080/10910344.2023.2235610","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10910344.2023.2235610","pdf_url":null,"source":{"id":"https://openalex.org/S128799162","display_name":"Machining Science and Technology","issn_l":"1091-0344","issn":["1091-0344","1532-2483"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-c","jufo-1","norway-1","poland-100","russia-white-list-1"],"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machining Science and Technology","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071455766","display_name":"Van-Hai Nguyen","orcid":"https://orcid.org/0000-0002-5959-1883","observed_orcids":["https://orcid.org/0000-0002-5959-1883"]},"institutions":[{"id":"https://openalex.org/I4210111036","display_name":"Phenikaa (Vietnam)","ror":"https://ror.org/0250p1d07","country_code":"VN","type":"company","lineage":["https://openalex.org/I4210111036"]},{"id":"https://openalex.org/I4210124651","display_name":"Phenikaa University","ror":"https://ror.org/03anxx281","country_code":"VN","type":"education","lineage":["https://openalex.org/I4210124651"]}],"countries":["VN"],"is_corresponding":true,"raw_author_name":"Van-Hai Nguyen","raw_affiliation_strings":["Faculty of Mechanical Engineering and Mechatronics, PHENIKAA University, Hanoi, Vietnam","PHENIKAA Research and Technology Institute (PRATI), A&a Green Phoenix Group JSC, Hanoi, Vietnam"],"raw_orcid":"https://orcid.org/0000-0002-5959-1883","affiliations":[{"raw_affiliation_string":"Faculty of Mechanical Engineering and Mechatronics, PHENIKAA University, Hanoi, Vietnam","institution_ids":["https://openalex.org/I4210124651"]},{"raw_affiliation_string":"PHENIKAA Research and Technology Institute (PRATI), A&a Green Phoenix Group JSC, Hanoi, Vietnam","institution_ids":["https://openalex.org/I4210111036"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024957412","display_name":"Tien-Thinh Le","orcid":"https://orcid.org/0000-0002-1603-5000","observed_orcids":["https://orcid.org/0000-0002-1603-5000","https://orcid.org/0000-0002-3288-6518"]},"institutions":[{"id":"https://openalex.org/I4210111036","display_name":"Phenikaa (Vietnam)","ror":"https://ror.org/0250p1d07","country_code":"VN","type":"company","lineage":["https://openalex.org/I4210111036"]},{"id":"https://openalex.org/I4210124651","display_name":"Phenikaa University","ror":"https://ror.org/03anxx281","country_code":"VN","type":"education","lineage":["https://openalex.org/I4210124651"]}],"countries":["VN"],"is_corresponding":false,"raw_author_name":"Tien-Thinh Le","raw_affiliation_strings":["Faculty of Mechanical Engineering and Mechatronics, PHENIKAA University, Hanoi, Vietnam","PHENIKAA Research and Technology Institute (PRATI), A&a Green Phoenix Group JSC, Hanoi, Vietnam"],"raw_orcid":"https://orcid.org/0000-0002-1603-5000","affiliations":[{"raw_affiliation_string":"Faculty of Mechanical Engineering and Mechatronics, PHENIKAA University, Hanoi, Vietnam","institution_ids":["https://openalex.org/I4210124651"]},{"raw_affiliation_string":"PHENIKAA Research and Technology Institute (PRATI), A&a Green Phoenix Group JSC, Hanoi, Vietnam","institution_ids":["https://openalex.org/I4210111036"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102009917","display_name":"Minh Vuong Le","orcid":"https://orcid.org/0000-0003-0631-656X","observed_orcids":["https://orcid.org/0000-0003-0631-656X"]},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I2800365227","display_name":"Paris-Est Sup","ror":"https://ror.org/0268ecp52","country_code":"FR","type":"education","lineage":["https://openalex.org/I2800365227"]},{"id":"https://openalex.org/I4210160945","display_name":"Laboratoire Mod\u00e9lisation et Simulation Multi-Echelle","ror":"https://ror.org/04rrzfd14","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I197681013","https://openalex.org/I2800365227","https://openalex.org/I4210095849","https://openalex.org/I4210154111","https://openalex.org/I4210160945"]},{"id":"https://openalex.org/I4405278519","display_name":"P\u00f4le de recherche et d'enseignement sup\u00e9rieur Universit\u00e9 Paris-Est","ror":"https://ror.org/005hkz789","country_code":"FR","type":"education","lineage":["https://openalex.org/I2800365227","https://openalex.org/I4405278519"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Minh Vuong Le","raw_affiliation_strings":["Laboratoire Mod\u00e9lisation et Simulation Multi Echelle, Universit\u00e9 Paris-Est, MSME UMR 8208 CNRS, Marne-la-Vall\u00e9e, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Laboratoire Mod\u00e9lisation et Simulation Multi Echelle, Universit\u00e9 Paris-Est, MSME UMR 8208 CNRS, Marne-la-Vall\u00e9e, France","institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I2800365227","https://openalex.org/I4210160945","https://openalex.org/I4405278519"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Hoang Dao Minh","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405277486","display_name":"Tr\u01b0\u1eddng \u0110\u1ea1i h\u1ecdc Khoa h\u1ecdc v\u00e0 C\u00f4ng ngh\u1ec7 H\u00e0 N\u1ed9i","ror":"https://ror.org/02wdwrr89","country_code":"VN","type":"education","lineage":["https://openalex.org/I4405277486"]},{"id":"https://openalex.org/I94518387","display_name":"Hanoi University of Science and Technology","ror":"https://ror.org/04nyv3z04","country_code":"VN","type":"education","lineage":["https://openalex.org/I94518387"]}],"countries":["VN"],"is_corresponding":false,"raw_author_name":"Hoang Dao Minh","raw_affiliation_strings":["School of Mathematics and Informatics, Hanoi University of Science and Technology, Hanoi, Vietnam"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics and Informatics, Hanoi University of Science and Technology, Hanoi, Vietnam","institution_ids":["https://openalex.org/I4405277486","https://openalex.org/I94518387"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076782738","display_name":"Anh-Tu Nguyen","orcid":"https://orcid.org/0000-0001-7679-9881","observed_orcids":["https://orcid.org/0000-0001-7679-9881"]},"institutions":[{"id":"https://openalex.org/I45756140","display_name":"Hanoi University of Industry","ror":"https://ror.org/05hhz4s12","country_code":"VN","type":"education","lineage":["https://openalex.org/I45756140"]}],"countries":["VN"],"is_corresponding":false,"raw_author_name":"Anh-Tu Nguyen","raw_affiliation_strings":["Faculty of Mechanical Engineering, Hanoi University of Industry, Hanoi, Vietnam"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Mechanical Engineering, Hanoi University of Industry, Hanoi, Vietnam","institution_ids":["https://openalex.org/I45756140"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5071455766"],"corresponding_institution_ids":["https://openalex.org/I4210111036","https://openalex.org/I4210124651"],"apc_list":null,"apc_paid":null,"fwci":3.5316,"has_fulltext":false,"cited_by_count":37,"citation_normalized_percentile":{"value":0.93773695,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"27","issue":"4","first_page":"380","last_page":"421"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10188","display_name":"Advanced machining processes and optimization","score":0.9945999979972839,"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/T10188","display_name":"Advanced machining processes and optimization","score":0.9945999979972839,"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/T11451","display_name":"Advanced Machining and Optimization Techniques","score":0.0038999998942017555,"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/T13113","display_name":"Engineering Technology and Methodologies","score":0.00019999999494757503,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/titanium-alloy-turning","display_name":"titanium alloy turning","score":0.9110000133514404},{"id":"https://openalex.org/keywords/surface-roughness-prediction","display_name":"surface roughness prediction","score":0.8519999980926514},{"id":"https://openalex.org/keywords/tool-wear-prediction","display_name":"tool wear prediction","score":0.9240000247955322},{"id":"https://openalex.org/keywords/multi-objective-optimization","display_name":"multi-objective optimization","score":0.9679999947547913},{"id":"https://openalex.org/keywords/nsga-ii","display_name":"NSGA-II","score":0.9879999756813049},{"id":"https://openalex.org/keywords/machine-learning-regression","display_name":"machine learning regression","score":0.6579999923706055},{"id":"https://openalex.org/keywords/neural-network","display_name":"artificial neural networks","score":0.8659999966621399},{"id":"https://openalex.org/keywords/support-vector-regression","display_name":"support vector regression","score":0.8050000071525574},{"id":"https://openalex.org/keywords/extreme-gradient-boosting","display_name":"extreme gradient boosting","score":0.9340000152587891},{"id":"https://openalex.org/keywords/dry-machining","display_name":"dry machining","score":0.7590000033378601},{"id":"https://openalex.org/keywords/cutting-parameters","display_name":"cutting parameters","score":0.7039999961853027},{"id":"https://openalex.org/keywords/pareto-optimal-solutions","display_name":"Pareto optimal solutions","score":0.7730000019073486},{"id":"https://openalex.org/keywords/hybrid-machining","display_name":"hybrid machining","score":0.3269999921321869}],"concepts":[{"id":"https://openalex.org/C523214423","wikidata":"https://www.wikidata.org/wiki/Q192047","display_name":"Machining","level":2,"score":0.6208970546722412},{"id":"https://openalex.org/C107365816","wikidata":"https://www.wikidata.org/wiki/Q114817","display_name":"Surface roughness","level":2,"score":0.6019530296325684},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5723327398300171},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5595163702964783},{"id":"https://openalex.org/C2776450708","wikidata":"https://www.wikidata.org/wiki/Q6008734","display_name":"Tool wear","level":3,"score":0.5469449162483215},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5315755605697632},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5162215232849121},{"id":"https://openalex.org/C2775926494","wikidata":"https://www.wikidata.org/wiki/Q192034","display_name":"Machinability","level":3,"score":0.47562363743782043},{"id":"https://openalex.org/C5941749","wikidata":"https://www.wikidata.org/wiki/Q19768","display_name":"Machine tool","level":2,"score":0.45593446493148804},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.453917533159256},{"id":"https://openalex.org/C111696304","wikidata":"https://www.wikidata.org/wiki/Q2303697","display_name":"Sorting","level":2,"score":0.4520924985408783},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4340098202228546},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4176400303840637},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.40871137380599976},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28279542922973633},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.2719305157661438},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2646838426589966},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.126490980386734},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/10910344.2023.2235610","doi":"https://doi.org/10.1080/10910344.2023.2235610","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10910344.2023.2235610","pdf_url":null,"source":{"id":"https://openalex.org/S128799162","display_name":"Machining Science and Technology","issn_l":"1091-0344","issn":["1091-0344","1532-2483"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-c","jufo-1","norway-1","poland-100","russia-white-list-1"],"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machining Science and Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":89,"referenced_works":["https://openalex.org/W17072261","https://openalex.org/W245099413","https://openalex.org/W1553373771","https://openalex.org/W1963536096","https://openalex.org/W1966120440","https://openalex.org/W1966990174","https://openalex.org/W1967640926","https://openalex.org/W1970360626","https://openalex.org/W1978770342","https://openalex.org/W1979049578","https://openalex.org/W1979777795","https://openalex.org/W1980679368","https://openalex.org/W1985777615","https://openalex.org/W1986070682","https://openalex.org/W1987884944","https://openalex.org/W1992894697","https://openalex.org/W1995341919","https://openalex.org/W1995436689","https://openalex.org/W2003622768","https://openalex.org/W2007789237","https://openalex.org/W2028144650","https://openalex.org/W2033612199","https://openalex.org/W2051629899","https://openalex.org/W2060306506","https://openalex.org/W2060600711","https://openalex.org/W2064891003","https://openalex.org/W2066113561","https://openalex.org/W2066996458","https://openalex.org/W2078249260","https://openalex.org/W2079503926","https://openalex.org/W2080340369","https://openalex.org/W2086823387","https://openalex.org/W2088730795","https://openalex.org/W2126105956","https://openalex.org/W2129749232","https://openalex.org/W2136463933","https://openalex.org/W2136922672","https://openalex.org/W2137034166","https://openalex.org/W2139427956","https://openalex.org/W2276742132","https://openalex.org/W2295598076","https://openalex.org/W2322095984","https://openalex.org/W2476141494","https://openalex.org/W2480645619","https://openalex.org/W2490247523","https://openalex.org/W2521461771","https://openalex.org/W2524886302","https://openalex.org/W2533491448","https://openalex.org/W2544883878","https://openalex.org/W2547192801","https://openalex.org/W2552806983","https://openalex.org/W2626642010","https://openalex.org/W2772404947","https://openalex.org/W2799380858","https://openalex.org/W2801226017","https://openalex.org/W2801469870","https://openalex.org/W2908541468","https://openalex.org/W2911455822","https://openalex.org/W2952068402","https://openalex.org/W2952297896","https://openalex.org/W2953121811","https://openalex.org/W2994669395","https://openalex.org/W2999356315","https://openalex.org/W3017268209","https://openalex.org/W3022199973","https://openalex.org/W3022465998","https://openalex.org/W3038959022","https://openalex.org/W3039866585","https://openalex.org/W3044339716","https://openalex.org/W3088669176","https://openalex.org/W3095807143","https://openalex.org/W3097586586","https://openalex.org/W3114046055","https://openalex.org/W3135524811","https://openalex.org/W3135609456","https://openalex.org/W3151394883","https://openalex.org/W3174058747","https://openalex.org/W3185348343","https://openalex.org/W3198484697","https://openalex.org/W3201751510","https://openalex.org/W3202661960","https://openalex.org/W4200066165","https://openalex.org/W4200150857","https://openalex.org/W4210411380","https://openalex.org/W4210660142","https://openalex.org/W4230674625","https://openalex.org/W4239510810","https://openalex.org/W4251364655","https://openalex.org/W4292122152"],"related_works":["https://openalex.org/W7208710821","https://openalex.org/W2801158513","https://openalex.org/W3204195970","https://openalex.org/W4293176306","https://openalex.org/W3151056563","https://openalex.org/W4281649841","https://openalex.org/W7162519243","https://openalex.org/W4282981451","https://openalex.org/W4285249495","https://openalex.org/W4381930321"],"abstract_inverted_index":{"Titanium":[0],"alloys":[1],"are":[2,8,81,90,184,212,271,297,324],"notoriously":[3],"difficult":[4],"to":[5,23,52,154,186,245,311],"machine.":[6],"They":[7],"used":[9,77,185],"in":[10,31,63,95,117,265,273],"the":[11,73,104,195,213,223,247,274,294,307,313,344,362,368],"manufacture":[12],"of":[13,16,67,97,108,194,202,239,262,276],"various":[14],"types":[15],"lightweight":[17],"components.":[18],"It":[19],"is":[20,243,256,366],"therefore":[21],"important":[22],"improve":[24],"their":[25,301],"machinability":[26],"and":[27,48,93,106,113,131,148,156,159,178,189,208,219,237,253,278,282,285,287,322,339,349,354],"thus":[28],"achieve":[29],"sustainability":[30],"machining":[32,50,64],"such":[33,226],"alloys,":[34],"by":[35,85,126,326],"selecting":[36],"appropriate":[37],"influential":[38],"factors:":[39],"cutting":[40,204,206,263,316],"parameters,":[41],"tool":[42,114,124,209,220],"material,":[43],"geometric":[44],"form,":[45],"coolant":[46],"types,":[47],"hybrid":[49],"methods,":[51],"deliver":[53],"efficient":[54,94],"output.":[55],"Nowadays,":[56],"meta-heuristic":[57],"algorithms":[58],"effectively":[59],"solve":[60],"multi-objective":[61,260],"optimization":[62,80,107,261],"problems":[65],"instead":[66],"single-objective":[68,79],"one.":[69],"Along":[70],"with":[71,136,306],"that,":[72],"mathematical":[74],"predictive":[75,249],"models":[76,164],"for":[78,259,347],"gradually":[82],"being":[83],"replaced":[84],"machine":[86,145],"learning":[87,146],"algorithms,":[88],"which":[89,333],"highly":[91],"robust":[92,315,329],"terms":[96],"prediction":[98,105],"performance.":[99,250],"Therefore,":[100],"this":[101,292,342],"work":[102],"addresses":[103],"average":[109],"surface":[110,217],"roughness":[111],"(Ra)":[112],"wear":[115],"(VB)":[116],"Ti6Al4V":[118,266],"alloy":[119],"turning,":[120],"using":[121],"a":[122,141,328],"WC":[123],"coated":[125],"chemical":[127],"vapor":[128,133],"deposition":[129,134],"(CVD)":[130],"physical":[132],"(PVD),":[135],"dry":[137],"machining.":[138],"We":[139],"apply":[140],"two-pronged":[142],"approach":[143,255],"combining":[144],"(ML)":[147],"Non-Dominated":[149],"Sorting":[150],"Genetic":[151],"Algorithm":[152],"(NSGA-II),":[153],"model":[155],"optimize":[157],"Ra":[158,188,277,321,348],"VB.":[160,190],"The":[161,191,318],"four":[162],"ML":[163,252],"\u2013":[165,183,198,211],"Linear":[166],"Regression":[167,172],"(LIN),":[168],"Support":[169],"Vector":[170],"Machine":[171],"(SVR),":[173],"Extreme":[174],"Gradient":[175],"Boosting":[176],"(XGB),":[177],"Artificial":[179],"Neural":[180],"Network":[181],"(ANN)":[182],"predict":[187],"input":[192],"variables":[193],"turning":[196],"process":[197],"feed":[199],"rate,":[200],"depth":[201],"cut,":[203],"speed,":[205],"time,":[207],"materials":[210],"major":[214],"factors":[215],"affecting":[216],"quality":[218],"wear.":[221],"By":[222],"error":[224,231,235],"metrics":[225],"as":[227],"root":[228],"mean":[229,233],"squared":[230],"(RMSE),":[232],"absolute":[234],"(MAE),":[236],"coefficient":[238],"determination":[240],"(R2),":[241],"ANN":[242],"found":[244],"offer":[246],"best":[248],"An":[251],"NSGA-II-based":[254],"then":[257],"employed":[258,310],"parameters":[264],"turning.":[267],"Fifty":[268],"Pareto":[269,295],"solutions":[270,296],"identified":[272],"range":[275],"VB":[279,323,350],"between":[280],"(1.332":[281],"1.441":[283],"\u00b5m)":[284],"(0.100":[286],"0.125":[288],"mm),":[289],"respectively.":[290,357],"In":[291],"work,":[293],"selected":[298],"based":[299],"on":[300],"ranked":[302],"performances.":[303],"This":[304],"aligns":[305],"decision":[308,337],"criterion":[309],"select":[312],"most":[314],"parameters.":[317],"definitive":[319],"optimal":[320,345],"obtained":[325],"formulating":[327],"decisive":[330],"multi-criterion":[331],"function":[332],"integrates":[334],"performance,":[335],"preferred":[336],"criterion,":[338],"trustworthiness.":[340],"Finally,":[341],"produces":[343],"solution":[346,365],"\u2212":[351],"1.439":[352],"\u00b5m":[353],"0.100":[355],"mm,":[356],"Experimental":[358],"validation":[359],"confirms":[360],"that":[361],"final":[363],"optimum":[364],"within":[367],"acceptable":[369],"range.":[370]},"counts_by_year":[{"year":2026,"cited_by_count":18},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":6}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2023-07-23T00:00:00"}
