{"id":"W2044505799","doi":"10.1016/j.apm.2011.04.025","title":"Optimization on the cold drawing process of 6063 aluminium tubes","year":2011,"lang":"en","type":"article","venue":"Applied Mathematical Modelling","topic":"Metal Forming Simulation Techniques","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval; National Research Council Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies; Université Laval","keywords":"Finite element method; Process (computing); Aluminium; Structural engineering; Process optimization; Mechanical engineering; Reduction (mathematics); Materials science; Engineering; Engineering drawing; Computer science; Metallurgy; Mathematics; Geometry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004507775,0.0004546354,0.0008618343,0.000381069,0.0005416973,0.0007939076,0.0004842392,0.0008322088,0.002374229],"category_scores_gemma":[0.0008532371,0.0003707854,0.0006215593,0.0005802529,0.0004389844,0.0003063841,0.0003001493,0.000388717,0.0002787383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008052929,"about_ca_system_score_gemma":0.001033641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008579651,"about_ca_topic_score_gemma":0.007459534,"domain_scores_codex":[0.9998494,0.00004726815,0.000004599433,0.00001585314,0.00004767751,0.00003507455],"domain_scores_gemma":[0.9997095,0.0001695187,0.00003161665,0.00001773013,0.00006149845,0.00001015684],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004406541,0.00002172933,0.0002518942,0.0000358746,0.000008372795,0.00002979228,0.00001674533,0.9912622,0.002924976,0.001137986,0.000116856,0.004149486],"study_design_scores_gemma":[0.00001334816,0.00006449108,0.0003779926,0.000003413762,0.00001044672,0.000008977961,0.00002360103,0.9956934,0.002881855,0.0004457566,0.0004707333,0.000006048634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8015891,0.0006833731,0.1617089,0.0003087875,0.00006351289,0.00009752998,0.0001841749,0.0002937131,0.03507093],"genre_scores_gemma":[0.984003,0.0001495207,0.01210167,0.00002011504,0.000006274258,0.00004489378,0.00007909264,0.00005928625,0.003536209],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008579651,"threshold_uncertainty_score":0.01705939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04428134026867605,"score_gpt":0.2299862979102256,"score_spread":0.1857049576415496,"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."}}