{"id":"W2032584736","doi":"10.1007/s00170-014-6583-z","title":"A machining-based methodology to identify material constitutive law for finite element simulation","year":2014,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":70,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Rake angle; Constitutive equation; Finite element method; Machining; Rake; Materials science; Chip formation; Chip; Mechanical engineering; Structural engineering; Mechanics; Engineering; Metallurgy; Tool wear; Physics","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":[0.0004128566,0.0001416954,0.0002137804,0.0002903612,0.00008245094,0.0000386171,0.0005397887,0.00008072822,0.00001742902],"category_scores_gemma":[0.0004382622,0.0001141508,0.0000635722,0.00006386685,0.00007067251,0.0001538171,0.00006489386,0.0001918352,0.000002737783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001050977,"about_ca_system_score_gemma":0.00001757927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002086323,"about_ca_topic_score_gemma":0.00001047085,"domain_scores_codex":[0.9990812,0.0000269863,0.000410457,0.0001248513,0.0001762162,0.0001802889],"domain_scores_gemma":[0.998754,0.0006050112,0.0002376641,0.0001382764,0.0002300273,0.00003500367],"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":[0.0002536455,0.000009756858,0.000004284266,0.00001364294,0.00006451178,0.000002209282,0.00005592022,0.9584448,0.007285974,0.008127728,0.00001533725,0.02572216],"study_design_scores_gemma":[0.00141544,0.0003041156,0.00003585275,0.00009812675,0.00004267048,0.00002960172,0.00008319032,0.2175708,0.6902809,0.06032488,0.02960904,0.0002053721],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07155463,0.00002886989,0.9252166,0.001174561,0.001564025,0.0001786875,0.00001706588,0.0001305151,0.0001350647],"genre_scores_gemma":[0.81398,0.000009318658,0.1854182,0.0003727179,0.0001641725,0.00002050567,0.000008726532,0.00002089991,0.000005490674],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7424254,"threshold_uncertainty_score":0.4654936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02120721402112698,"score_gpt":0.3310276834568907,"score_spread":0.3098204694357637,"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."}}