{"id":"W3154788462","doi":"10.1016/j.cirp.2021.03.024","title":"Novel sensor-based tool wear monitoring approach for seamless implementation in high speed milling applications","year":2021,"lang":"en","type":"article","venue":"CIRP Annals","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; National Research Council Canada","funders":"National Research Council Canada","keywords":"Tool wear; Machine tool; Vibration; Computer science; Condition monitoring; Cutting tool; Engineering; Control engineering; Mechanical engineering; Machining; Acoustics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002408765,0.0005365469,0.0004437323,0.0004302309,0.0001682286,0.0005461198,0.001174614,0.0005752493,0.001466817],"category_scores_gemma":[0.0004651241,0.0002621221,0.0002097167,0.0003895654,0.0001867373,0.0007671312,0.0004392214,0.0004036133,0.0004470428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000204314,"about_ca_system_score_gemma":0.0002732737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003453069,"about_ca_topic_score_gemma":0.001115341,"domain_scores_codex":[0.9995475,0.00003130152,0.00001793758,0.00009759441,0.0002741884,0.00003141834],"domain_scores_gemma":[0.9996099,0.0000650167,0.00007100066,0.00006913326,0.0001666169,0.00001837493],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002524835,0.0001534075,0.001779468,0.0002024293,0.00003234391,0.0001308686,0.00009224374,0.002766919,0.8395887,0.0008626399,0.00206536,0.152073],"study_design_scores_gemma":[0.00003486893,0.0008341763,0.007162464,0.00002356862,0.00005496188,0.0006472484,0.0000907591,0.2234963,0.7570542,0.0007209074,0.009806981,0.00007372522],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2024173,0.001288735,0.7856008,0.0002532412,0.0004063408,0.0001581628,0.0004084611,0.003549953,0.005917001],"genre_scores_gemma":[0.8257784,0.0002956388,0.1696294,0.0001601596,0.00006184233,0.00006432278,0.0001770501,0.00006530276,0.003767952],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001466817,"threshold_uncertainty_score":0.004906952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04165363374364302,"score_gpt":0.3243470128936295,"score_spread":0.2826933791499865,"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."}}