{"id":"W4403673327","doi":"10.2139/ssrn.4957890","title":"Optimization of Machining Parameters for Nimonic PE16 Using Machine Learning Models","year":2024,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Nimonic; Machining; Computer science; Materials science; Machine learning; Artificial intelligence; Mechanical engineering; Metallurgy; Engineering; Superalloy","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":[0.0005650077,0.0001635458,0.0001859918,0.0001968041,0.0001424506,0.00007052148,0.0001153784,0.0000692129,0.000005981955],"category_scores_gemma":[0.00004033212,0.0001594822,0.0001073578,0.0002369859,0.00001608493,0.0004885998,0.00001404169,0.0009770149,4.857143e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004119329,"about_ca_system_score_gemma":0.0002997829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001328613,"about_ca_topic_score_gemma":0.00001852722,"domain_scores_codex":[0.998477,0.00002115229,0.0003166976,0.0001520845,0.000133047,0.000900024],"domain_scores_gemma":[0.9996755,0.00007507959,0.00007871372,0.00006762705,0.0000613391,0.00004174936],"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":[0.00002020262,0.000005355082,0.00002466401,0.0001074298,0.0001134842,6.111864e-7,0.0001805631,0.9783571,0.0002748028,0.01019013,0.000001138321,0.01072454],"study_design_scores_gemma":[0.0002533546,0.0001205061,2.61752e-7,0.000112803,0.00007242707,0.00009757098,0.0002012424,0.9637864,0.0002434704,0.03488956,0.00006368664,0.0001587453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0136543,0.01606308,0.9695843,0.00002265211,0.0002524713,0.0001125167,0.000004781858,0.0001920194,0.000113841],"genre_scores_gemma":[0.9083049,0.007059746,0.0843416,0.000005512919,0.00008039673,0.000006019017,0.00002315284,0.00009117331,0.00008754875],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8946506,"threshold_uncertainty_score":0.6503495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01730138606543605,"score_gpt":0.2475594449567783,"score_spread":0.2302580588913423,"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."}}