{"id":"W2022392197","doi":"10.1016/j.ijmachtools.2015.03.002","title":"Chatter identification in end milling process based on EEMD and nonlinear dimensionless indicators","year":2015,"lang":"en","type":"article","venue":"International Journal of Machine Tools and Manufacture","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":179,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"University of British Columbia; National Natural Science Foundation of China","keywords":"Hilbert–Huang transform; Vibration; Nonlinear system; Frequency domain; Control theory (sociology); Dimensionless quantity; Harmonics; Machining; Spectral density; Bispectrum; Filter (signal processing); Time domain; Engineering; Acoustics; Mathematics; Computer science; Mathematical analysis; Artificial intelligence; Mechanical engineering; Mechanics; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001875391,0.000108161,0.0001274269,0.0002615198,0.00001903762,0.00007481428,0.0001061818,0.00005246825,0.00001127724],"category_scores_gemma":[0.00006085851,0.00008677669,0.00001910374,0.00005909083,0.00001762582,0.0002904616,0.00001410822,0.0002228534,6.159639e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000359058,"about_ca_system_score_gemma":0.00001944563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002817378,"about_ca_topic_score_gemma":0.000005729689,"domain_scores_codex":[0.9992842,0.00001167753,0.0002692779,0.00009615589,0.0002606736,0.00007798206],"domain_scores_gemma":[0.9996085,0.0000447728,0.0001306393,0.00004557071,0.00009756945,0.0000729454],"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":[0.0001401122,0.00004775815,0.003623812,0.00004889148,0.00003120388,0.00003191123,0.0005865045,0.9250513,0.0002325383,0.00004527111,0.00005841656,0.07010231],"study_design_scores_gemma":[0.004018885,0.0002504124,0.03050192,0.0004451551,0.00004234331,0.0001616008,0.000308789,0.9414252,0.01421302,0.002190016,0.005973993,0.0004686452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9414498,0.0009418622,0.05540276,0.001151729,0.0006299191,0.0001021842,0.00004448884,0.00002814388,0.0002490868],"genre_scores_gemma":[0.9972455,0.0001890447,0.002131541,0.0002556063,0.000114371,0.000001871282,0.00003651866,0.00001511773,0.00001044415],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06963366,"threshold_uncertainty_score":0.3538651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009338313873965253,"score_gpt":0.2588872437547264,"score_spread":0.2495489298807612,"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."}}