{"id":"W2328831662","doi":"10.1007/s10845-016-1209-y","title":"A sensor fusion and support vector machine based approach for recognition of complex machining conditions","year":2016,"lang":"en","type":"article","venue":"Journal of Intelligent Manufacturing","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":87,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"National Natural Science Foundation of China","keywords":"Machining; Support vector machine; SIGNAL (programming language); Sensor fusion; Pattern recognition (psychology); Acceleration; Artificial intelligence; Condition monitoring; Engineering; Feature extraction; Tool wear; Machine tool; Vibration; Process (computing); Computer science; Mechanical engineering; Acoustics","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":[0.0004779543,0.0006409578,0.0008109601,0.0006980081,0.0002722356,0.0006419582,0.0007295291,0.0008667665,0.00132931],"category_scores_gemma":[0.000855308,0.0003321021,0.0004748561,0.0007639338,0.0002565875,0.0009675001,0.0005886685,0.0007680316,0.0005681539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002209164,"about_ca_system_score_gemma":0.0004732948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001287982,"about_ca_topic_score_gemma":0.001538347,"domain_scores_codex":[0.9996044,0.00005470697,0.00002762733,0.00007909466,0.0001898276,0.00004430614],"domain_scores_gemma":[0.999647,0.00007768517,0.00003901513,0.00003522361,0.0001831734,0.00001789242],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004638125,0.000303819,0.001447081,0.0001929326,0.0000989527,0.0001370066,0.00008962339,0.0638458,0.1317534,0.002603494,0.002362698,0.7967015],"study_design_scores_gemma":[0.0000124624,0.0002164007,0.00180258,0.00001022393,0.00003044435,0.0001173111,0.00002888186,0.969025,0.02629456,0.001273999,0.001161315,0.00002671984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03394816,0.0004990486,0.9633053,0.00009917136,0.0001228086,0.0000485057,0.00008961403,0.0007858032,0.001101554],"genre_scores_gemma":[0.7054368,0.0005295945,0.2905664,0.000125744,0.00008815033,0.0001255548,0.0002644322,0.00004298858,0.002820392],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00132931,"threshold_uncertainty_score":0.004446983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03054415581207957,"score_gpt":0.2603750140233659,"score_spread":0.2298308582112863,"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."}}