{"id":"W3008869636","doi":"10.3390/jmmp4010016","title":"A Unique Methodology for Tool Life Prediction in Machining","year":2020,"lang":"en","type":"article","venue":"Journal of Manufacturing and Materials Processing","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Rake; Flank; Machining; Tool wear; Face (sociological concept); von Mises yield criterion; Calibration; Finite element method; Computer science; Function (biology); Materials science; Mechanical engineering; Structural engineering; Mathematics; Engineering; Statistics","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":[0.0004188881,0.0001023535,0.0002631334,0.00008016119,0.0000470283,0.0000653992,0.00005730735,0.00006508049,0.000006374307],"category_scores_gemma":[0.0001979294,0.00009146246,0.00001996644,0.00003778398,0.00001050208,0.0003276464,0.0000147025,0.0001186251,1.526572e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001698112,"about_ca_system_score_gemma":0.00002606812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001065256,"about_ca_topic_score_gemma":3.191187e-7,"domain_scores_codex":[0.9992756,0.00003089929,0.0004158801,0.00008878663,0.00005929372,0.0001295238],"domain_scores_gemma":[0.9996479,0.00006802582,0.0001660696,0.00002531924,0.00003302584,0.00005963798],"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":[0.0008312279,0.00002402257,0.00007789936,0.005051033,0.00006637118,0.00001879954,0.005667609,0.4896528,0.1246524,0.0001053898,0.0001104907,0.373742],"study_design_scores_gemma":[0.001698358,0.0002832144,0.001143815,0.0004695839,0.00005876481,0.00008786475,0.0004005689,0.04022349,0.9517692,0.002195591,0.00140029,0.0002692246],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5448152,0.0003272025,0.4544355,0.000138431,0.0001581434,0.00006053311,0.00000421998,0.00004177441,0.00001903013],"genre_scores_gemma":[0.9059241,0.0001604307,0.09352218,0.0001244402,0.0002360112,0.00000475694,0.00000256921,0.00002287779,0.000002643481],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8271168,"threshold_uncertainty_score":0.3729731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03854059991764456,"score_gpt":0.2686314966079568,"score_spread":0.2300908966903123,"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."}}