{"id":"W4403706201","doi":"10.1016/j.rcim.2024.102887","title":"A hybrid model in a nonlinear disturbance observer for improving compliance error compensation of robotic machining","year":2024,"lang":"en","type":"article","venue":"Robotics and Computer-Integrated Manufacturing","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Alberta Innovates; Natural Sciences and Engineering Research Council of Canada; Korea Institute of Industrial Technology","keywords":"Compensation (psychology); Machining; Control theory (sociology); Nonlinear system; Disturbance (geology); Control engineering; Compliance (psychology); Observer (physics); Nonlinear model; Computer science; Engineering; Control (management); Artificial intelligence; Psychology; Mechanical engineering; Physics; Social psychology; Geology","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":[0.0003995459,0.0004882072,0.000628376,0.0001996035,0.0003024254,0.0006908821,0.0006342811,0.0006867112,0.001288408],"category_scores_gemma":[0.0005603456,0.0002854616,0.0003696649,0.0002025591,0.0004368579,0.0006019644,0.0006551882,0.0006411092,0.0002796105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002981506,"about_ca_system_score_gemma":0.0004094412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00467159,"about_ca_topic_score_gemma":0.005498127,"domain_scores_codex":[0.9997163,0.00005813712,0.00001917046,0.00006728754,0.0001082841,0.00003063816],"domain_scores_gemma":[0.9997441,0.00006331204,0.00004144758,0.00003181838,0.0001073097,0.00001202184],"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":[0.0005372786,0.0001915872,0.001179398,0.0004351853,0.0001281848,0.0002175081,0.0002424835,0.7925594,0.08769632,0.01154141,0.001622046,0.1036492],"study_design_scores_gemma":[0.00001232426,0.00007612127,0.0001771767,0.000003920781,0.00001115953,0.000009149272,0.00000577143,0.9966155,0.002265333,0.0002452666,0.0005735721,0.000004758545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03469328,0.0002214608,0.9617707,0.00008912202,0.000152193,0.00003459073,0.00002218378,0.0003572029,0.002659359],"genre_scores_gemma":[0.9534433,0.0001467905,0.04130135,0.00005964776,0.00003787995,0.00007802911,0.00005415269,0.00003290132,0.004845939],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00467159,"threshold_uncertainty_score":0.009288847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02348347392916331,"score_gpt":0.2473835984774354,"score_spread":0.2239001245482721,"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."}}