{"id":"W4391435507","doi":"10.1016/j.cirpj.2024.01.005","title":"Digital twin assisted intelligent machining process monitoring and control","year":2024,"lang":"en","type":"article","venue":"CIRP journal of manufacturing science and technology","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Sandvik Coromant; Pratt and Whitney Canada","keywords":"Machining; Process (computing); Engineering; Sensor fusion; Tool wear; Accelerometer; Transient (computer programming); Control engineering; Real-time computing; Computer science; Mechanical engineering; Artificial intelligence","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":[0.0002326114,0.00009982583,0.0001398366,0.0005305335,0.0001153789,0.0002540058,0.0001679036,0.00005709814,0.000001334482],"category_scores_gemma":[0.0001091339,0.00008243578,0.00001366527,0.0002967124,0.000206668,0.0007351692,0.00003347915,0.0003029664,6.172873e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000425937,"about_ca_system_score_gemma":0.00004180345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.361975e-7,"about_ca_topic_score_gemma":8.390177e-8,"domain_scores_codex":[0.9992992,0.000001950428,0.0002030544,0.0001394495,0.0001710877,0.0001852986],"domain_scores_gemma":[0.9997022,0.00003901214,0.00004983196,0.00006022156,0.00008467971,0.00006406071],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000006677668,0.000007652216,0.002037797,0.0001902281,0.00003401333,0.00006283514,0.0002858019,0.02350189,0.004355723,0.0002038679,0.000003831624,0.9693097],"study_design_scores_gemma":[0.001087816,0.0005206195,0.006637819,0.001921444,0.0001236659,0.004712061,0.002608594,0.1201046,0.8425115,0.0146289,0.004329218,0.0008138165],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9250517,0.004599248,0.06911121,0.0002226007,0.0004502817,0.00004851972,0.000001062517,0.0002013589,0.0003140114],"genre_scores_gemma":[0.9985549,0.0003696661,0.0009936598,0.000003978564,0.00005458239,0.000001518664,1.095442e-7,0.0000119977,0.000009514777],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9684958,"threshold_uncertainty_score":0.3361634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006733099673252616,"score_gpt":0.2486867050387485,"score_spread":0.2419536053654959,"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."}}