{"id":"W2037976190","doi":"10.2495/hpsm140031","title":"Identification of material constitutive law constants using machining tests: a response surface methodology based approach","year":2014,"lang":"en","type":"article","venue":"WIT transactions on the built environment","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Machining; Constitutive equation; Finite element method; Chip formation; Mechanical engineering; Materials science; Structural engineering; Law; Computer science; Engineering; 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":[0.0007422612,0.0001528608,0.000178653,0.0000484609,0.000185859,0.00002152733,0.0001248037,0.00007150575,0.00007790002],"category_scores_gemma":[0.00002935563,0.0001309827,0.00004440511,0.00008706604,0.0002481859,0.00008473863,0.000003506324,0.0001436295,0.000005551355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009075572,"about_ca_system_score_gemma":0.00001428871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001843729,"about_ca_topic_score_gemma":0.000001621985,"domain_scores_codex":[0.9989179,0.0002950442,0.0002988692,0.0001932597,0.0001430025,0.0001519531],"domain_scores_gemma":[0.9991076,0.0004555704,0.00009706333,0.0002942448,0.0000116556,0.00003386701],"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":[0.0001956109,0.00004395472,0.00000457531,0.00002479248,0.00002651319,1.917847e-7,0.0001555092,0.8629038,0.1344346,0.001706069,5.776935e-7,0.0005037938],"study_design_scores_gemma":[0.0004063933,0.00006403611,0.00004214445,0.00003164994,0.00007401506,0.000006870674,0.0001773079,0.776256,0.2221167,0.0003601375,0.0002948192,0.0001699283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1033703,0.00002362675,0.8957942,0.00004395579,0.0001340969,0.0002117,0.00006834243,0.00007233729,0.0002814645],"genre_scores_gemma":[0.9203535,0.00001482808,0.07951184,0.00004257022,0.000008786027,0.00002013798,0.000007680575,0.00002581289,0.00001480593],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8169833,"threshold_uncertainty_score":0.5341319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03579568313807786,"score_gpt":0.2559970513735576,"score_spread":0.2202013682354798,"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."}}