{"id":"W2087753470","doi":"10.1115/imece2002-33633","title":"3D Finite Element Analysis for the High Speed Machining of Hardened Steel","year":2002,"lang":"en","type":"article","venue":"Manufacturing","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Machining; Finite element method; Chip; Materials science; Chip formation; Slip (aerodynamics); Stress (linguistics); Mechanical engineering; Structural engineering; Hardened steel; Computer science; Composite material; Engineering; Metallurgy; Tool wear","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":[],"consensus_categories":[],"category_scores_codex":[0.00009188785,0.0001216448,0.0001781912,0.0001059778,0.0001029206,0.00002824498,0.0001374074,0.00003158147,0.0002617087],"category_scores_gemma":[0.00002093161,0.00009594365,0.00008958307,0.0001283442,0.00001143733,0.00007966519,0.0000266037,0.00008152096,0.00000286716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003216305,"about_ca_system_score_gemma":0.000001544655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001359048,"about_ca_topic_score_gemma":0.00001403255,"domain_scores_codex":[0.999346,0.000005621584,0.000220481,0.0001284644,0.0001151565,0.0001842907],"domain_scores_gemma":[0.9995219,0.0001774038,0.00006558491,0.0001905864,0.00001917653,0.00002538629],"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":[0.000005194447,0.000004905005,0.000129824,0.00005609411,0.000252251,3.455152e-7,0.0002494511,0.9869981,0.00005364314,0.00002183213,0.00003140508,0.0121969],"study_design_scores_gemma":[0.000283877,0.00001748405,0.001149524,0.00001208003,0.0002386025,2.585241e-7,0.00005315316,0.9716566,0.0240663,0.00005865825,0.002342984,0.0001204832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04300542,0.0005057136,0.9549521,0.00005696427,0.0001747875,0.0001979354,0.0000232207,0.0001471679,0.0009367174],"genre_scores_gemma":[0.9834716,0.000162183,0.01590506,0.00002900653,0.00005716107,0.00001419194,0.00001884535,0.00002477251,0.0003171754],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9404662,"threshold_uncertainty_score":0.3912469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01568754928202217,"score_gpt":0.2170513387235259,"score_spread":0.2013637894415037,"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."}}