{"id":"W2886374926","doi":"10.1007/s00170-018-2420-0","title":"Tool condition monitoring using spectral subtraction and convolutional neural networks in milling process","year":2018,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":203,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure","funders":"Ames Research Center; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Tool wear; Artificial intelligence; Artificial neural network; Convolutional neural network; Engineering; Support vector machine; Machining; Wavelet; Downtime; Machine tool; Fault detection and isolation; Feature extraction; Computer science; Pattern recognition (psychology); Machine learning; Reliability engineering; Mechanical engineering; Actuator","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001306886,0.0001174135,0.0001326061,0.0003025541,0.00007814165,0.00003195105,0.0002449989,0.00008118506,0.000006895852],"category_scores_gemma":[0.00004888818,0.0001012423,0.00002522868,0.0001118545,0.0001051912,0.0003693093,0.00003412107,0.0003950117,3.4361e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001592649,"about_ca_system_score_gemma":0.0000139131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002250184,"about_ca_topic_score_gemma":0.000005143465,"domain_scores_codex":[0.9992152,0.000007672885,0.0003289282,0.0001041674,0.0001727989,0.0001712316],"domain_scores_gemma":[0.9995142,0.0000505453,0.000187376,0.00006571022,0.0001621744,0.00002005876],"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":[0.0000734424,0.000008267266,0.001199086,0.000008214908,0.00002798439,0.00001177005,0.00008053481,0.9777244,0.007722036,0.0001337074,0.000001143044,0.01300947],"study_design_scores_gemma":[0.0008879204,0.000117218,0.004807059,0.0001949318,0.00001964459,0.0007423763,0.0003931687,0.7416402,0.2393527,0.01156861,0.00007797476,0.0001982242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9014806,0.000389276,0.09665582,0.0001773001,0.001140453,0.00005960136,0.000001192873,0.00007716144,0.00001866128],"genre_scores_gemma":[0.9909458,0.0002745563,0.008254489,0.00001655435,0.0004834891,0.000002868766,0.000001531457,0.0000181034,0.000002573424],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2360841,"threshold_uncertainty_score":0.412854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008125261086522183,"score_gpt":0.2699558143832492,"score_spread":0.2618305532967271,"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."}}