{"id":"W1587054045","doi":"10.1007/978-3-319-48761-8_52","title":"Modelling Grain Deformation during Extrusion of AA3003 using the Finite Element Method","year":2012,"lang":"en","type":"book-chapter","venue":"ICAA13 Pittsburgh","topic":"Metallurgy and Material Forming","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Rio Tinto (Canada); University of British Columbia; University of Waterloo","funders":"","keywords":"Extrusion; Materials science; Dynamic recrystallization; Finite element method; Die swell; Microstructure; Recrystallization (geology); Metallurgy; Forging; Deformation (meteorology); Composite material; Hot working; Structural engineering; Engineering; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006534042,0.0003726803,0.0004411489,0.0001837045,0.0001570176,0.000030778,0.0002259831,0.000291433,0.0004808917],"category_scores_gemma":[0.000009372347,0.0002964519,0.0001980014,0.00004308894,0.00002874673,0.0002661229,0.0001144162,0.0003267922,0.00001700852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001120168,"about_ca_system_score_gemma":0.00001737259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002433461,"about_ca_topic_score_gemma":9.783655e-7,"domain_scores_codex":[0.9984,0.00002816031,0.0007077039,0.0001817386,0.0003297466,0.0003526718],"domain_scores_gemma":[0.9990963,0.00007278117,0.000265399,0.0004334538,0.00005604777,0.00007600748],"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":[0.00002260278,0.000008361199,0.000001571563,0.0006100285,0.0001870655,0.000003624436,0.0009360456,0.9383531,0.022841,0.02885919,0.00002219498,0.008155233],"study_design_scores_gemma":[0.000531214,0.00003296016,0.000007315467,0.001179663,0.0005202833,0.0000362717,0.00007672195,0.8249287,0.02802749,0.03732425,0.10621,0.001125116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01309715,0.00188523,0.9571221,0.00001098977,0.001509727,0.0005488545,0.00001763851,0.0002006702,0.02560766],"genre_scores_gemma":[0.915243,0.003717222,0.05196187,0.00006651982,0.002041135,0.00007554423,0.0001648727,0.000664126,0.02606565],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9051602,"threshold_uncertainty_score":0.9999487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03600811657203044,"score_gpt":0.2440714824944037,"score_spread":0.2080633659223733,"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."}}