{"id":"W4378193189","doi":"10.1016/j.cirp.2023.04.012","title":"Wide-bandwidth cutting force monitoring via motor current and accelerometer signals","year":2023,"lang":"en","type":"article","venue":"CIRP Annals","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Accelerometer; Dynamometer; Machining; Kalman filter; Smoothing; Acceleration; Engineering; Bandwidth (computing); Control theory (sociology); Automotive engineering; Control engineering; Computer science; Mechanical engineering; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000208868,0.0005496099,0.000337911,0.0007092648,0.0001711258,0.0003802257,0.0003349776,0.000598081,0.001463233],"category_scores_gemma":[0.000786458,0.00017268,0.00008772933,0.00045117,0.0001416162,0.000503545,0.0002953584,0.0002524526,0.0004963079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001483255,"about_ca_system_score_gemma":0.000157635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004849621,"about_ca_topic_score_gemma":0.001445409,"domain_scores_codex":[0.9996338,0.00004317886,0.00001327981,0.0000852329,0.0001917641,0.0000327773],"domain_scores_gemma":[0.9996033,0.0001191389,0.00006562388,0.00004263936,0.0001471285,0.00002204124],"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":[0.001080871,0.000135827,0.009693906,0.0002685951,0.00003809137,0.0001746704,0.0001630803,0.004136839,0.691706,0.000641924,0.002152462,0.2898076],"study_design_scores_gemma":[0.000104263,0.001310103,0.1159241,0.0001701382,0.0001263784,0.001973836,0.0003608718,0.2525459,0.6075186,0.001921684,0.01790196,0.0001421512],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5448747,0.001871901,0.4303511,0.0004015733,0.0002235488,0.0001541761,0.0008119239,0.003360947,0.01795014],"genre_scores_gemma":[0.9712676,0.0002142609,0.02574677,0.00008991994,0.00003752259,0.00003960141,0.0001393103,0.00004224974,0.002422836],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001463233,"threshold_uncertainty_score":0.004895031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04194315411724781,"score_gpt":0.3029979524753902,"score_spread":0.2610547983581424,"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."}}