{"id":"W2043414788","doi":"10.1115/pvp2004-2745","title":"Prediction of Trimming Process Parameters in Aluminum Sheet Materials Using FE Method","year":2004,"lang":"en","type":"article","venue":"","topic":"Metal Forming Simulation Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"McMaster University","keywords":"Trimming; Materials science; Finite element method; Fracture (geology); Process (computing); Displacement (psychology); Aluminium; Forming limit diagram; Enhanced Data Rates for GSM Evolution; Process variable; Structural engineering; Composite material; Mechanical engineering; Sheet metal; Computer science; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003903228,0.00009727093,0.0001983477,0.0002038591,0.00001202852,0.00001317969,0.00006850948,0.00007716558,0.00002129549],"category_scores_gemma":[0.00004578074,0.00009634779,0.00002665543,0.0002231869,0.00001155888,0.0002431027,0.000009671678,0.00005064769,0.000001173904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000903914,"about_ca_system_score_gemma":0.00001237678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001242393,"about_ca_topic_score_gemma":0.000002882674,"domain_scores_codex":[0.9992275,0.00002073634,0.0003974996,0.0001061705,0.0001190945,0.0001289708],"domain_scores_gemma":[0.9997639,0.00002695678,0.00004779232,0.0001139214,0.00002562513,0.00002179565],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001120215,0.00002128053,0.0002000791,0.0002585691,0.00001914746,0.000001387517,0.0004219413,0.5278645,0.4680322,0.0006877521,0.000004000653,0.002477931],"study_design_scores_gemma":[0.0002542827,0.00001654571,0.0004216946,0.00007633503,0.000009958868,0.000003838509,0.00005701957,0.02855623,0.9672208,0.003295886,0.000005728832,0.00008170244],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.824273,0.0000114597,0.1747752,0.000002729519,0.0001102723,0.0002228131,0.000006447365,0.0002673763,0.0003306977],"genre_scores_gemma":[0.8402238,0.000002650253,0.1597081,0.000005180843,0.00001038438,0.00001785194,0.000004958905,0.0000187225,0.000008267507],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4993082,"threshold_uncertainty_score":0.3928949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0436977648124888,"score_gpt":0.3062047120854077,"score_spread":0.2625069472729189,"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."}}