{"id":"https://openalex.org/W4405644405","doi":"https://doi.org/10.1017/s089006042400009x","title":"Design of an intelligent simulator ANN and ANFIS model in the prediction of milling performance (QCE) of alloy 2017A","display_name":"Design of an intelligent simulator ANN and ANFIS model in the prediction of milling performance (QCE) of alloy 2017A","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4405644405","doi":"https://doi.org/10.1017/s089006042400009x"},"language":"en","primary_location":{"id":"doi:10.1017/s089006042400009x","is_oa":true,"landing_page_url":"https://doi.org/10.1017/s089006042400009x","pdf_url":"https://www.cambridge.org/core/services/aop-cambridge-core/content/view/50D5BCC0637A415EE85B9C9747F07491/S089006042400009Xa.pdf/div-class-title-design-of-an-intelligent-simulator-ann-and-anfis-model-in-the-prediction-of-milling-performance-qce-of-alloy-2017a-div.pdf","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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":"hybrid","oa_url":"https://www.cambridge.org/core/services/aop-cambridge-core/content/view/50D5BCC0637A415EE85B9C9747F07491/S089006042400009Xa.pdf/div-class-title-design-of-an-intelligent-simulator-ann-and-anfis-model-in-the-prediction-of-milling-performance-qce-of-alloy-2017a-div.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031563905","display_name":"Kamel Bousnina","orcid":"https://orcid.org/0000-0002-0997-1650"},"institutions":[{"id":"https://openalex.org/I4210131288","display_name":"National Engineering School of Tunis","ror":"https://ror.org/03b1zjt31","country_code":"TN","type":"education","lineage":["https://openalex.org/I4210131288","https://openalex.org/I63596082"]}],"countries":["TN"],"is_corresponding":false,"raw_author_name":"Kamel Bousnina","raw_affiliation_strings":["Higher National Engineering School of Tunis (ENSIT), University of Tunis, Mechanical, Production and Energy Laboratory (LMPE), Avenue Taha Hussein, Montfleury, 1008 Tunis, Tunisia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Higher National Engineering School of Tunis (ENSIT), University of Tunis, Mechanical, Production and Energy Laboratory (LMPE), Avenue Taha Hussein, Montfleury, 1008 Tunis, Tunisia","institution_ids":["https://openalex.org/I4210131288"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071373901","display_name":"Anis Hamza","orcid":"https://orcid.org/0000-0003-4283-5236"},"institutions":[{"id":"https://openalex.org/I4210131288","display_name":"National Engineering School of Tunis","ror":"https://ror.org/03b1zjt31","country_code":"TN","type":"education","lineage":["https://openalex.org/I4210131288","https://openalex.org/I63596082"]}],"countries":["TN"],"is_corresponding":true,"raw_author_name":"Anis Hamza","raw_affiliation_strings":["Higher National Engineering School of Tunis (ENSIT), University of Tunis, Mechanical, Production and Energy Laboratory (LMPE), Avenue Taha Hussein, Montfleury, 1008 Tunis, Tunisia"],"raw_orcid":"https://orcid.org/0000-0003-4283-5236","affiliations":[{"raw_affiliation_string":"Higher National Engineering School of Tunis (ENSIT), University of Tunis, Mechanical, Production and Energy Laboratory (LMPE), Avenue Taha Hussein, Montfleury, 1008 Tunis, Tunisia","institution_ids":["https://openalex.org/I4210131288"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112940048","display_name":"Noureddine Ben Yahia","orcid":"https://orcid.org/0000-0001-8277-041X"},"institutions":[{"id":"https://openalex.org/I4210131288","display_name":"National Engineering School of Tunis","ror":"https://ror.org/03b1zjt31","country_code":"TN","type":"education","lineage":["https://openalex.org/I4210131288","https://openalex.org/I63596082"]}],"countries":["TN"],"is_corresponding":false,"raw_author_name":"Noureddine Ben Yahia","raw_affiliation_strings":["Higher National Engineering School of Tunis (ENSIT), University of Tunis, Mechanical, Production and Energy Laboratory (LMPE), Avenue Taha Hussein, Montfleury, 1008 Tunis, Tunisia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Higher National Engineering School of Tunis (ENSIT), University of Tunis, Mechanical, Production and Energy Laboratory (LMPE), Avenue Taha Hussein, Montfleury, 1008 Tunis, Tunisia","institution_ids":["https://openalex.org/I4210131288"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5071373901"],"corresponding_institution_ids":["https://openalex.org/I4210131288"],"apc_list":null,"apc_paid":null,"fwci":1.4202,"has_fulltext":true,"cited_by_count":7,"citation_normalized_percentile":{"value":0.79943131,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"38","issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10188","display_name":"Advanced machining processes and optimization","score":0.996999979019165,"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":0.996999979019165,"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":0.9919999837875366,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T11159","display_name":"Manufacturing Process and Optimization","score":0.9879999756813049,"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/adaptive-neuro-fuzzy-inference-system","display_name":"Adaptive neuro fuzzy inference system","score":0.5041972398757935},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.43897637724876404},{"id":"https://openalex.org/keywords/alloy","display_name":"Alloy","score":0.4243800640106201},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.37624862790107727},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3330399990081787},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.17741721868515015},{"id":"https://openalex.org/keywords/metallurgy","display_name":"Metallurgy","score":0.1010427474975586},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.07686677575111389}],"concepts":[{"id":"https://openalex.org/C186108316","wikidata":"https://www.wikidata.org/wiki/Q352530","display_name":"Adaptive neuro fuzzy inference system","level":4,"score":0.5041972398757935},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.43897637724876404},{"id":"https://openalex.org/C2780026712","wikidata":"https://www.wikidata.org/wiki/Q37756","display_name":"Alloy","level":2,"score":0.4243800640106201},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.37624862790107727},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3330399990081787},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.17741721868515015},{"id":"https://openalex.org/C191897082","wikidata":"https://www.wikidata.org/wiki/Q11467","display_name":"Metallurgy","level":1,"score":0.1010427474975586},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.07686677575111389},{"id":"https://openalex.org/C195975749","wikidata":"https://www.wikidata.org/wiki/Q1475705","display_name":"Fuzzy control system","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1017/s089006042400009x","is_oa":true,"landing_page_url":"https://doi.org/10.1017/s089006042400009x","pdf_url":"https://www.cambridge.org/core/services/aop-cambridge-core/content/view/50D5BCC0637A415EE85B9C9747F07491/S089006042400009Xa.pdf/div-class-title-design-of-an-intelligent-simulator-ann-and-anfis-model-in-the-prediction-of-milling-performance-qce-of-alloy-2017a-div.pdf","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Artificial Intelligence for Engineering Design, Analysis and Manufacturing","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1017/s089006042400009x","is_oa":true,"landing_page_url":"https://doi.org/10.1017/s089006042400009x","pdf_url":"https://www.cambridge.org/core/services/aop-cambridge-core/content/view/50D5BCC0637A415EE85B9C9747F07491/S089006042400009Xa.pdf/div-class-title-design-of-an-intelligent-simulator-ann-and-anfis-model-in-the-prediction-of-milling-performance-qce-of-alloy-2017a-div.pdf","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Artificial Intelligence for Engineering Design, Analysis and Manufacturing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4405644405.pdf","grobid_xml":"https://content.openalex.org/works/W4405644405.grobid-xml"},"referenced_works_count":40,"referenced_works":["https://openalex.org/W419168134","https://openalex.org/W565456890","https://openalex.org/W1969868418","https://openalex.org/W1979052428","https://openalex.org/W1979203698","https://openalex.org/W1983136089","https://openalex.org/W1984066530","https://openalex.org/W1992674248","https://openalex.org/W1993275616","https://openalex.org/W2007978032","https://openalex.org/W2019879871","https://openalex.org/W2097487677","https://openalex.org/W2098677060","https://openalex.org/W2257850429","https://openalex.org/W2420804263","https://openalex.org/W2488339603","https://openalex.org/W2552789174","https://openalex.org/W2604905036","https://openalex.org/W2606906937","https://openalex.org/W2624294946","https://openalex.org/W2765725851","https://openalex.org/W2884009078","https://openalex.org/W2888901020","https://openalex.org/W2891445318","https://openalex.org/W2917920937","https://openalex.org/W2923054208","https://openalex.org/W2936620298","https://openalex.org/W2959110034","https://openalex.org/W2993032300","https://openalex.org/W3006675440","https://openalex.org/W3009927626","https://openalex.org/W3085328507","https://openalex.org/W3085493150","https://openalex.org/W3127988635","https://openalex.org/W3130780349","https://openalex.org/W3156287542","https://openalex.org/W4296365683","https://openalex.org/W4297521399","https://openalex.org/W4307378337","https://openalex.org/W6782813314"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2114654021","https://openalex.org/W2263529430","https://openalex.org/W2389800468","https://openalex.org/W4390103748","https://openalex.org/W2763641192","https://openalex.org/W2150377753","https://openalex.org/W2392430664"],"abstract_inverted_index":{"Abstract":[0],"Artificial":[1],"neural":[2,155],"networks":[3],"(ANNs)":[4],"and":[5,18,33,60,70,123,132,140,147,191,204],"adaptive":[6],"neuro-fuzzy":[7],"inference":[8],"systems":[9],"(ANFISs)":[10],"are":[11],"machine":[12,82],"learning":[13,83],"techniques":[14,86],"that":[15,54,152,215],"enable":[16],"modeling":[17],"prediction":[19],"of":[20,27,51,128,176],"various":[21,61],"properties":[22],"in":[23,113],"the":[24,56,93,108,125,153,160,185,194,217,230],"milling":[25],"process":[26],"alloy":[28],"2017A,":[29],"including":[30],"quality,":[31,137],"cost,":[32,139],"energy":[34,141],"consumption":[35,142],"(QCE).":[36],"To":[37],"utilize":[38],"ANNs":[39],"or":[40,89],"ANFIS":[41],"for":[42,157,183,193],"QCE":[43,59,115],"prediction,":[44],"researchers":[45],"must":[46],"gather":[47],"a":[48,81,166,207,221],"dataset":[49,75],"consisting":[50],"input\u2013output":[52],"pairs":[53],"establish":[55],"relationship":[57],"between":[58],"input":[62,104,110],"variables":[63,197],"such":[64],"as":[65,117],"machining":[66,130,138],"parameters,":[67],"tool":[68],"properties,":[69],"material":[71],"characteristics.":[72],"Subsequently,":[73],"this":[74],"can":[76,99],"be":[77,100],"employed":[78],"to":[79,229],"train":[80],"model":[84,94],"using":[85,143,216],"like":[87],"backpropagation":[88],"gradient":[90],"descent.":[91],"Once":[92],"has":[95],"been":[96],"trained,":[97],"predictions":[98],"made":[101],"on":[102,135],"new":[103],"data":[105],"by":[106],"providing":[107],"desired":[109],"variables,":[111],"resulting":[112],"predicted":[114],"values":[116],"output.":[118],"This":[119],"study":[120],"comprehensively":[121],"examines":[122],"identifies":[124],"scientific":[126],"contributions":[127],"strategies,":[129],"sequences,":[131],"cutting":[133],"parameters":[134],"surface":[136],"artificial":[144],"intelligence":[145],"(ANN":[146],"ANFIS).":[148],"The":[149,212],"findings":[150],"indicate":[151],"optimal":[154,186],"architecture":[156,168],"ANNs,":[158],"utilizing":[159],"Bayesian":[161],"regularization":[162],"(BR)":[163],"algorithm,":[164],"is":[165,206],"{3-10-3}":[167],"with":[169,220],"an":[170],"overall":[171],"mean":[172],"square":[173],"error":[174,190],"(MSE)":[175],"2.74":[177],"\u00d7":[178],"10":[179],"\u22123":[180],".":[181],"Similarly,":[182],"ANFIS,":[184],"structure":[187],"yielding":[188],"better":[189],"correlation":[192],"three":[195],"output":[196,223],"(E":[198],"tot":[199,202],",":[200,203],"C":[201],"Ra)":[205],"{2,":[208],"2,":[209],"2}":[210],"structure.":[211],"results":[213],"demonstrate":[214],"BR":[218],"algorithm":[219],"multi-criteria":[222],"response":[224],"yields":[225],"favorable":[226],"outcomes":[227],"compared":[228],"ANFIS.":[231]},"counts_by_year":[{"year":2025,"cited_by_count":7}],"updated_date":"2026-03-08T06:56:09.383167","created_date":"2025-10-10T00:00:00"}
