{"id":"https://openalex.org/W7225383176","doi":"https://doi.org/10.14445/23488360/ijme-v13i10p103","title":"Comparative Analysis of Machine Learning Methods for Predicting Tensile Strength of Dual-Extruded FDM 3D- Printed ABS/PC Composite","display_name":"Comparative Analysis of Machine Learning Methods for Predicting Tensile Strength of Dual-Extruded FDM 3D- Printed ABS/PC Composite","publication_year":2026,"publication_date":"2026-11-06","ids":{"openalex":"https://openalex.org/W7225383176","doi":"https://doi.org/10.14445/23488360/ijme-v13i10p103"},"language":"en","primary_location":{"id":"doi:10.14445/23488360/ijme-v13i10p103","doi":"https://doi.org/10.14445/23488360/ijme-v13i10p103","is_oa":true,"landing_page_url":"https://doi.org/10.14445/23488360/ijme-v13i10p103","pdf_url":"https://www.internationaljournalssrg.org/../IJME/2026/Volume13-Issue10/IJME-V13I10P103.pdf","source":{"id":"https://openalex.org/S4210178612","display_name":"International Journal of Mechanical Engineering","issn_l":"2348-8360","issn":["2348-8360","2349-9168"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Mechanical Engineering","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://www.internationaljournalssrg.org/../IJME/2026/Volume13-Issue10/IJME-V13I10P103.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5161060880","display_name":"D S Madhuri","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I134892692","display_name":"Chaitanya Bharathi Institute of Technology","ror":"https://ror.org/047ymzq84","country_code":"IN","type":"education","lineage":["https://openalex.org/I134892692"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"D S Madhuri","raw_affiliation_strings":["Department of Mechanical Engineering , Chaitanya Bharathi Institute of Technology , Telangana , India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering , Chaitanya Bharathi Institute of Technology , Telangana , India","institution_ids":["https://openalex.org/I134892692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5161098821","display_name":"Venkata Sushma Chinta","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I134892692","display_name":"Chaitanya Bharathi Institute of Technology","ror":"https://ror.org/047ymzq84","country_code":"IN","type":"education","lineage":["https://openalex.org/I134892692"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Venkata Sushma Chinta","raw_affiliation_strings":["Department of Mechanical Engineering , Chaitanya Bharathi Institute of Technology , Telangana , India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering , Chaitanya Bharathi Institute of Technology , Telangana , India","institution_ids":["https://openalex.org/I134892692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007310453","display_name":"Nagini Yarramsetty","orcid":"https://orcid.org/0000-0001-9405-9019","observed_orcids":["https://orcid.org/0000-0001-9405-9019"]},"institutions":[{"id":"https://openalex.org/I134892692","display_name":"Chaitanya Bharathi Institute of Technology","ror":"https://ror.org/047ymzq84","country_code":"IN","type":"education","lineage":["https://openalex.org/I134892692"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Nagini Yarramsetty","raw_affiliation_strings":["Department of Mechanical Engineering , Chaitanya Bharathi Institute of Technology , Telangana , India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering , Chaitanya Bharathi Institute of Technology , Telangana , India","institution_ids":["https://openalex.org/I134892692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103815261","display_name":"Katta Sugamya","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I134892692","display_name":"Chaitanya Bharathi Institute of Technology","ror":"https://ror.org/047ymzq84","country_code":"IN","type":"education","lineage":["https://openalex.org/I134892692"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Katta Sugamya","raw_affiliation_strings":["Department of Information Technology , Chaitanya Bharathi Institute of Technology , Telangana , India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Technology , Chaitanya Bharathi Institute of Technology , Telangana , India","institution_ids":["https://openalex.org/I134892692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066619227","display_name":"K. Chaitanya","orcid":"https://orcid.org/0000-0002-2252-2219","observed_orcids":["https://orcid.org/0000-0002-2252-2219"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"K Lakshmi Chaitanya","raw_affiliation_strings":["Department of Mechanical Engineering , R V R & J C College of Engineering , Andhra Pradesh , India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering , R V R & J C College of Engineering , Andhra Pradesh , India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":"10","first_page":"22","last_page":"35"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10783","display_name":"Additive Manufacturing and 3D Printing Technologies","score":0.9937000274658203,"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/T10783","display_name":"Additive Manufacturing and 3D Printing Technologies","score":0.9937000274658203,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.0038999998942017555,"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/T10705","display_name":"Additive Manufacturing Materials and Processes","score":0.0005000000237487257,"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/tensile-strength-prediction","display_name":"tensile strength prediction","score":0.972000002861023},{"id":"https://openalex.org/keywords/polymer-composites","display_name":"polymer composites","score":0.550000011920929},{"id":"https://openalex.org/keywords/additive-manufacturing-process-parameters","display_name":"additive manufacturing process parameters","score":0.7580000162124634},{"id":"https://openalex.org/keywords/machine-learning-regression","display_name":"machine learning regression","score":0.8029999732971191},{"id":"https://openalex.org/keywords/support-vector-regression","display_name":"support vector regression","score":0.9139999747276306},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"gradient boosting","score":0.9570000171661377},{"id":"https://openalex.org/keywords/adaboost","display_name":"AdaBoost","score":0.9810000061988831},{"id":"https://openalex.org/keywords/linear-regression","display_name":"linear regression","score":0.9279999732971191},{"id":"https://openalex.org/keywords/permutation-feature-importance","display_name":"permutation importance","score":0.9760000109672546},{"id":"https://openalex.org/keywords/layer-thickness","display_name":"layer thickness","score":0.8330000042915344},{"id":"https://openalex.org/keywords/interlayer-bonding","display_name":"interlayer bonding","score":0.6140000224113464},{"id":"https://openalex.org/keywords/taguchi-orthogonal-array","display_name":"Taguchi orthogonal array","score":0.703000009059906}],"concepts":[{"id":"https://openalex.org/C112950240","wikidata":"https://www.wikidata.org/wiki/Q76005","display_name":"Ultimate tensile strength","level":2,"score":0.7620000243186951},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.6261000037193298},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.5936999917030334},{"id":"https://openalex.org/C2781431410","wikidata":"https://www.wikidata.org/wiki/Q143496","display_name":"Acrylonitrile butadiene styrene","level":2,"score":0.5425999760627747},{"id":"https://openalex.org/C104779481","wikidata":"https://www.wikidata.org/wiki/Q50707","display_name":"Composite number","level":2,"score":0.5268999934196472},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.5005999803543091},{"id":"https://openalex.org/C83469408","wikidata":"https://www.wikidata.org/wiki/Q2036525","display_name":"Taguchi methods","level":2,"score":0.4982999861240387},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4433000087738037},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.44110000133514404},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4097000062465668},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.384799987077713},{"id":"https://openalex.org/C150077022","wikidata":"https://www.wikidata.org/wiki/Q3136137","display_name":"Response surface methodology","level":2,"score":0.37869998812675476},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37310001254081726},{"id":"https://openalex.org/C42632107","wikidata":"https://www.wikidata.org/wiki/Q2031860","display_name":"Orthogonal array","level":3,"score":0.3653999865055084},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.3644999861717224},{"id":"https://openalex.org/C182508753","wikidata":"https://www.wikidata.org/wiki/Q115605","display_name":"Tensile testing","level":3,"score":0.3626999855041504},{"id":"https://openalex.org/C2776540798","wikidata":"https://www.wikidata.org/wiki/Q62246","display_name":"Polycarbonate","level":2,"score":0.3522999882698059},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.329800009727478},{"id":"https://openalex.org/C34559072","wikidata":"https://www.wikidata.org/wiki/Q2334061","display_name":"Design of experiments","level":2,"score":0.3160000145435333},{"id":"https://openalex.org/C207948433","wikidata":"https://www.wikidata.org/wiki/Q685487","display_name":"Tempering","level":2,"score":0.2897000014781952},{"id":"https://openalex.org/C199639397","wikidata":"https://www.wikidata.org/wiki/Q1788588","display_name":"Engineering drawing","level":1,"score":0.2824999988079071},{"id":"https://openalex.org/C2777973245","wikidata":"https://www.wikidata.org/wiki/Q146174","display_name":"Polypropylene","level":2,"score":0.2809000015258789},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.27970001101493835},{"id":"https://openalex.org/C128990827","wikidata":"https://www.wikidata.org/wiki/Q192830","display_name":"Coefficient of determination","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.27489998936653137},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.25380000472068787},{"id":"https://openalex.org/C196029304","wikidata":"https://www.wikidata.org/wiki/Q2091584","display_name":"Young's modulus","level":2,"score":0.2531999945640564},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.251800000667572},{"id":"https://openalex.org/C2780329122","wikidata":"https://www.wikidata.org/wiki/Q18349149","display_name":"Fused deposition modeling","level":3,"score":0.2502000033855438},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.25}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.14445/23488360/ijme-v13i10p103","doi":"https://doi.org/10.14445/23488360/ijme-v13i10p103","is_oa":true,"landing_page_url":"https://doi.org/10.14445/23488360/ijme-v13i10p103","pdf_url":"https://www.internationaljournalssrg.org/../IJME/2026/Volume13-Issue10/IJME-V13I10P103.pdf","source":{"id":"https://openalex.org/S4210178612","display_name":"International Journal of Mechanical Engineering","issn_l":"2348-8360","issn":["2348-8360","2349-9168"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Mechanical Engineering","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.14445/23488360/ijme-v13i10p103","doi":"https://doi.org/10.14445/23488360/ijme-v13i10p103","is_oa":true,"landing_page_url":"https://doi.org/10.14445/23488360/ijme-v13i10p103","pdf_url":"https://www.internationaljournalssrg.org/../IJME/2026/Volume13-Issue10/IJME-V13I10P103.pdf","source":{"id":"https://openalex.org/S4210178612","display_name":"International Journal of Mechanical Engineering","issn_l":"2348-8360","issn":["2348-8360","2349-9168"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Mechanical Engineering","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.4943999946117401}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7225383176.pdf","grobid_xml":"https://content.openalex.org/works/W7225383176.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7119498821","https://openalex.org/W6968481468","https://openalex.org/W7128951447","https://openalex.org/W4412598612","https://openalex.org/W7171712821","https://openalex.org/W7143964976","https://openalex.org/W4414499116","https://openalex.org/W7148834185","https://openalex.org/W4392919413","https://openalex.org/W4416281913"],"abstract_inverted_index":{"The":[0,79,117,137,227],"mechanical":[1],"behaviour":[2],"of":[3,25,33,55,58,70,151,181,195,237,241],"FDM":[4,27,243],"composite":[5,65],"materials":[6],"is":[7,120,130,217],"largely":[8],"influenced":[9],"by":[10,198],"process":[11,81,152],"variables,":[12],"material":[13],"composition,":[14],"&":[15],"inter-layer":[16],"bonding.":[17],"In":[18],"order":[19],"to":[20],"predict":[21],"the":[22,68,134,172,178,185,191,223,238],"tensile":[23,135,138,239],"strength":[24,240],"dual-extruded":[26],"composites":[28],"fabricated":[29],"from":[30],"alternating":[31],"layers":[32,106],"Acrylonitrile":[34],"Butadiene":[35],"Styrene":[36],"(ABS)":[37],"and":[38,110,127,144,167,184,203],"Polycarbonate":[39],"(PC)":[40],"using":[41],"a":[42,148],"dual-extrusion":[43,242],"printer,":[44],"this":[45],"study":[46],"compares":[47],"various":[48],"Machine":[49],"Learning":[50],"(ML)":[51],"algorithms.":[52],"Such":[53],"kind":[54],"systematic":[56],"comparison":[57],"ML":[59,155],"modelling":[60,232],"on":[61,122,177],"an":[62],"alternating-layer":[63],"ABS/PC":[64],"along":[66],"with":[67,171],"use":[69],"permutation":[71],"importance":[72],"analysis":[73,207],"has":[74],"never":[75],"been":[76],"performed":[77],"before.":[78],"following":[80],"parameters":[82],"are":[83,169,212],"taken":[84],"into":[85],"account:":[86],"layer":[87,220],"height":[88],"(0.20,":[89],"0.25,":[90],"0.30":[91],"mm),":[92],"nozzle":[93],"temperature":[94,112],"(250,":[95],"255,":[96],"260":[97],"\u00b0C),":[98],"print":[99],"speed":[100],"(50,":[101],"55,":[102],"60":[103],"mm/s),":[104],"shell/wall":[105],"(2,":[107],"3,":[108],"4),":[109],"bed":[111],"(90,":[113],"95,":[114],"100":[115],"\u00b0C).":[116],"experimental":[118],"design":[119],"based":[121,176],"Taguchi":[123],"L27":[124],"orthogonal":[125],"array":[126],"ASTM":[128],"D638":[129],"used":[131],"for":[132],"preparing":[133],"specimens.":[136],"results":[139,228],"range":[140],"between":[141],"22.98":[142],"MPa":[143],"49.38":[145],"MPa,":[146],"indicating":[147],"strong":[149],"influence":[150],"parameters.":[153],"Four":[154],"regression":[156],"models-Linear":[157],"Regression":[158,162],"(LR),":[159],"Support":[160],"Vector":[161],"(SVR),":[163],"Gradient":[164],"Boosting":[165],"(GB),":[166],"AdaBoost":[168,199],"used,":[170],"best":[173],"model":[174],"selected":[175],"highest":[179,192],"Coefficient":[180],"Determination":[182],"(R\u00b2)":[183],"lowest":[186],"RMSE":[187],"value.":[188],"LR":[189],"achieves":[190],"R\u00b2":[193],"value":[194],"0.4595,":[196],"followed":[197],"(0.4478),":[200],"SVR":[201],"(0.4211),":[202],"GB":[204],"(0.3947).":[205],"Learning-curve":[206],"reveals":[208],"that":[209,230],"ensemble":[210],"models":[211],"overfitting.":[213],"Permutation":[214],"importance,":[215],"which":[216],"model-independent,":[218],"predicts":[219],"thickness":[221],"as":[222],"most":[224],"important":[225],"parameter.":[226],"show":[229],"data-driven":[231],"can":[233],"provide":[234],"reasonable":[235],"predictions":[236],"composites.":[244]},"counts_by_year":[],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2026-11-07T00:00:00"}
