{"id":"W2028365657","doi":"10.1007/s00170-014-6558-0","title":"Volumetric error formulation and mismatch test for five-axis CNC machine compensation using differential kinematics and ephemeral G-code","year":2014,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Advanced Measurement and Metrology Techniques","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kinematics; Cartesian coordinate system; Machine tool; Compensation (psychology); Position (finance); Computer science; Algorithm; Numerical control; Machining; Representation (politics); Simulation; Control theory (sociology); Artificial intelligence; Engineering; Mathematics; Mechanical engineering; Geometry","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":[0.000205659,0.0001398417,0.0002208361,0.0004283917,0.00007592921,0.0000222921,0.0002417233,0.00009098567,0.000003605104],"category_scores_gemma":[0.0002495248,0.0001097673,0.00004110215,0.00005246904,0.00006255462,0.0001919811,0.00006139581,0.0002128714,1.558618e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007867144,"about_ca_system_score_gemma":0.000005165435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002458779,"about_ca_topic_score_gemma":0.00001036184,"domain_scores_codex":[0.9992188,0.00001012715,0.0003539404,0.0001005116,0.000168441,0.0001482454],"domain_scores_gemma":[0.9992809,0.0002047382,0.0002382697,0.000104428,0.0001431083,0.00002858851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003148747,0.0001037223,0.004504927,0.0001639379,0.0003658882,0.000005347289,0.0002617731,0.06379816,0.7261457,0.003263031,0.000096232,0.2009764],"study_design_scores_gemma":[0.002625383,0.0004575965,0.006834445,0.0001369839,0.0001217826,0.0002800062,0.00008711356,0.2344388,0.6825934,0.07024784,0.001885759,0.0002909116],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6407211,0.0001406225,0.3582956,0.0004152496,0.0002209686,0.0001191082,0.000004275637,0.00007238485,0.00001067124],"genre_scores_gemma":[0.9137602,0.0001366473,0.08593225,0.00003117412,0.00009787348,0.000006605891,0.000003609638,0.00001947827,0.00001216263],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2730391,"threshold_uncertainty_score":0.4476179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01602793106868447,"score_gpt":0.2642134182709935,"score_spread":0.2481854872023091,"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."}}