{"id":"W3109371868","doi":"10.1016/j.mechatronics.2020.102445","title":"Dynamic model identification for CNC machine tool feed drives from in-process signals for virtual process planning","year":2020,"lang":"en","type":"article","venue":"Mechatronics","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Machine tool; Process (computing); Machining; Identification (biology); Contouring; Control engineering; Computer science; Numerical control; Engineering; Control theory (sociology); Artificial intelligence; Engineering drawing; Control (management); Mechanical engineering","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":[0.000304862,0.0006203529,0.0005063333,0.0003781579,0.0004098495,0.000836525,0.0005939453,0.0005612869,0.002763909],"category_scores_gemma":[0.0009071152,0.0003285132,0.0003436445,0.0004075014,0.0002541974,0.0005913391,0.0003821589,0.0008233811,0.0007012743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005715322,"about_ca_system_score_gemma":0.001061915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01239619,"about_ca_topic_score_gemma":0.01212303,"domain_scores_codex":[0.9998096,0.00003488185,0.000007911659,0.00004192794,0.00008568844,0.00002014645],"domain_scores_gemma":[0.9997396,0.0001012142,0.00002901948,0.00003021845,0.00009209899,0.000007904453],"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":[0.000155213,0.00005890279,0.0005977149,0.0002111233,0.00003192685,0.00009905358,0.0001054807,0.9123374,0.01495994,0.004557201,0.001294955,0.06559111],"study_design_scores_gemma":[0.000004663963,0.00002518839,0.0002686201,0.000008219382,0.000004897642,0.00001517137,0.00001007825,0.9945356,0.003510845,0.0006146193,0.0009974095,0.000004742704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02675041,0.0001513114,0.9662114,0.000125772,0.00004644074,0.00005787598,0.0001573974,0.0008259318,0.00567334],"genre_scores_gemma":[0.9545695,0.0001506389,0.04011336,0.00003125031,0.000007508749,0.0000903975,0.0002689944,0.00007818888,0.004690103],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01239619,"threshold_uncertainty_score":0.02464807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01406356176670695,"score_gpt":0.269253286695444,"score_spread":0.255189724928737,"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."}}