{"id":"W4403249858","doi":"10.1016/j.procir.2024.08.395","title":"A Generalized Multi-Stage Deep Machine Learning Framework for Tool Wear Level Prediction in Milling Operations","year":2024,"lang":"en","type":"article","venue":"Procedia CIRP","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; National Research Council Canada","funders":"","keywords":"Stage (stratigraphy); Engineering; Computer science; Manufacturing engineering; Artificial intelligence; Tool wear; Industrial engineering; Mechanical engineering; Systems engineering; Geology; Machining","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001330579,0.0001559096,0.0001412148,0.0001428126,0.0001179155,0.0001131676,0.00007410078,0.0001197629,0.00003875668],"category_scores_gemma":[0.0003147182,0.0001625724,0.00004431875,0.0002943404,0.000009960368,0.0003188064,0.00001557363,0.0003437896,0.00001071636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007413384,"about_ca_system_score_gemma":0.00003003179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001094737,"about_ca_topic_score_gemma":0.00008311989,"domain_scores_codex":[0.9991677,0.000008835877,0.0002547475,0.0002453447,0.00009016894,0.0002331721],"domain_scores_gemma":[0.999738,0.0000837546,0.00001505543,0.00008038359,0.00004359515,0.00003916071],"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":[0.00000928068,0.00001218142,0.0003293963,0.0004530289,0.00001695897,0.000001730472,0.001497289,0.988123,0.001349503,0.00282931,0.000009645457,0.005368658],"study_design_scores_gemma":[0.0003745733,0.00002822147,0.00008180838,0.0001641975,0.0000163447,0.000002442591,0.00007512423,0.9946089,0.0008205643,0.0005891355,0.003060878,0.000177812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01256348,0.003543393,0.982188,0.00005344838,0.0004666783,0.0003790522,0.00006759937,0.0006884906,0.00004987087],"genre_scores_gemma":[0.5205718,0.0007098287,0.4773306,0.00003306551,0.0002218704,0.0003391093,0.0001973771,0.00008464256,0.0005116682],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5080084,"threshold_uncertainty_score":0.6629509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04130665859509122,"score_gpt":0.2964831447971225,"score_spread":0.2551764862020313,"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."}}