{"id":"W4296112062","doi":"10.1007/s00170-022-10007-7","title":"Dynamic errors compensation of high-speed coordinate measuring machines using ANN-based predictive modeling","year":2022,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Advanced Measurement and Metrology Techniques","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Trois-Rivières; Université du Québec à Rimouski","funders":"","keywords":"Compensation (psychology); Coordinate-measuring machine; Metrology; Artificial neural network; Computer science; Taguchi methods; Observational error; Machine tool; Accuracy and precision; Control engineering; Engineering; Control theory (sociology); Machine learning; Artificial intelligence; Mechanical engineering","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":[0.0005201914,0.0007016607,0.0006683035,0.0004642354,0.0004302748,0.0006878476,0.0005527876,0.0006638115,0.000908739],"category_scores_gemma":[0.001165608,0.0004180251,0.0005070152,0.0004994901,0.0002657408,0.0006305388,0.0003665833,0.0006038838,0.000270951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005026902,"about_ca_system_score_gemma":0.0005287195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007642173,"about_ca_topic_score_gemma":0.007451747,"domain_scores_codex":[0.9996862,0.00005232396,0.00002295529,0.00009157123,0.000114116,0.00003266639],"domain_scores_gemma":[0.9995002,0.0001926647,0.00008145491,0.00004267832,0.0001723674,0.00001057968],"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":[0.0001206142,0.00005015593,0.0008583885,0.00007669368,0.00004067565,0.00006093922,0.00004010014,0.9165056,0.005908401,0.0006310847,0.0003727722,0.07533468],"study_design_scores_gemma":[0.000001967337,0.0000116338,0.0003203194,0.00000328561,0.000004657331,0.000005454145,0.000001836579,0.9986461,0.0008135152,0.0001066182,0.00008224035,0.000002406976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1314535,0.0007123928,0.8607645,0.0001780191,0.0001412733,0.00004320499,0.00006362683,0.001242592,0.005400949],"genre_scores_gemma":[0.9806774,0.0001425683,0.01776576,0.00002036275,0.00001389443,0.00002763532,0.00004505712,0.00001922655,0.001287916],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007642173,"threshold_uncertainty_score":0.01519537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01669281823114943,"score_gpt":0.2503010230823321,"score_spread":0.2336082048511827,"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."}}