{"id":"W3211704489","doi":"10.1115/imece2000-1900","title":"Numerical Analysis of Cutting With Chamfered and Worn Edge Tools","year":2000,"lang":"en","type":"article","venue":"Manufacturing engineering","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Chamfer (geometry); Enhanced Data Rates for GSM Evolution; Finite element method; Materials science; Chip formation; Thrust; Machining; Cutting tool; Chip; Rake; Structural engineering; Tool wear; Mechanical engineering; Computer science; Geometry; Engineering; Mathematics; Metallurgy","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":[0.00003999366,0.0001528314,0.0002493517,0.0001746341,0.00002941256,0.0000315125,0.00006556045,0.0000418331,0.00005742594],"category_scores_gemma":[0.000007927652,0.0001426544,0.00003620349,0.0002822107,0.000009331401,0.0001679268,0.000009543845,0.0001163985,0.000001264463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000214644,"about_ca_system_score_gemma":0.00000214593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007362839,"about_ca_topic_score_gemma":0.000001022592,"domain_scores_codex":[0.9993899,0.000002699691,0.0001671021,0.0001515621,0.00009315224,0.0001956071],"domain_scores_gemma":[0.9997364,0.00004655006,0.00002068063,0.0001317777,0.000009402418,0.00005523355],"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":[0.000008555898,0.000003604171,0.0002677466,0.0001036089,0.0001769621,0.000002287654,0.0001559999,0.9669638,0.0001301334,0.00001108488,0.000001166175,0.03217508],"study_design_scores_gemma":[0.0001765557,0.00001843546,0.01451073,0.00005844172,0.0001446141,0.000004485653,0.00001558843,0.9686031,0.01567206,0.000003246937,0.0005801662,0.0002125702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7715737,0.0002197676,0.2270886,0.00000474639,0.00003018671,0.00005931334,0.000004413207,0.0003319264,0.000687341],"genre_scores_gemma":[0.9858866,0.00008569565,0.0139011,0.00000555758,0.00002781698,0.000007706268,0.00001366743,0.00003659464,0.00003531722],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2143129,"threshold_uncertainty_score":0.5817279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004790884749977564,"score_gpt":0.181661767878415,"score_spread":0.1768708831284374,"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."}}