{"id":"W4366992904","doi":"10.1016/j.matpr.2023.04.323","title":"Multi-response optimization of processing variants to fabricate graphene reinforced Al 2124 metal matrix composite using friction stir processing","year":2023,"lang":"en","type":"article","venue":"Materials Today Proceedings","topic":"Aluminum Alloys Composites Properties","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Department of Mechanical Engineering, University of Alberta; Departamento de Ingeniería Mecánica, Universidad de Chile","keywords":"Materials science; Friction stir processing; Taguchi methods; Composite material; Rotational speed; Composite number; Scanning electron microscope; Graphene; Aerospace; Metal matrix composite; Alloy; Orthogonal array; Powder metallurgy; Mechanical engineering; Microstructure; Nanotechnology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001532885,0.0004315237,0.0002353507,0.0003589248,0.000237631,0.0003376704,0.000285687,0.0002865794,0.00163428],"category_scores_gemma":[0.0001922658,0.0001922909,0.0003422869,0.0003148231,0.0001503146,0.0002584097,0.0002094571,0.0003469334,0.0004009589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002836888,"about_ca_system_score_gemma":0.0002401292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007035432,"about_ca_topic_score_gemma":0.002984488,"domain_scores_codex":[0.9998715,0.000008667706,0.000006761394,0.00003336136,0.00004903069,0.00003066718],"domain_scores_gemma":[0.9999183,0.00001628607,0.00002350308,0.00000818135,0.00002478556,0.000008953686],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001236854,0.00004288061,0.0001736536,0.00007233574,0.000008530499,0.00003702085,0.00002306803,0.001857628,0.9945542,0.00009948736,0.00005765239,0.002949824],"study_design_scores_gemma":[0.0000081913,0.0003523991,0.001096416,0.000003273021,0.00001862223,0.00002722431,0.00002806223,0.007844133,0.9897181,0.00003207006,0.0008601188,0.00001141378],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9904357,0.0003830175,0.006282169,0.0000312904,0.00002737492,0.00002136671,0.0001052747,0.000125797,0.002588037],"genre_scores_gemma":[0.9916344,0.000166022,0.006693548,0.00001204669,0.000004178373,0.00002260346,0.00008863421,0.0000391761,0.001339407],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00163428,"threshold_uncertainty_score":0.005467176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02351375806842728,"score_gpt":0.2578213634800405,"score_spread":0.2343076054116132,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}