{"id":"W2088115729","doi":"10.1115/msec2009-84211","title":"Laser Assisted Finish Turning of Inconel 718: Process Optimization","year":2009,"lang":"en","type":"article","venue":"","topic":"Additive Manufacturing Materials and Processes","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Machinability; Inconel; Materials science; Superalloy; Machining; Tool wear; Metallurgy; Surface roughness; Carbide; Surface finish; Surface integrity; Laser; Composite material; Microstructure; Optics; Alloy","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":[0.00004260433,0.00008497734,0.0001191982,0.00003753499,0.00002203205,0.0000233743,0.00006222438,0.00004404666,0.0005540646],"category_scores_gemma":[0.00002160021,0.00007458405,0.00001667933,0.00007459748,0.000009699117,0.0001221191,0.000005419131,0.00003931207,0.000006275734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008149274,"about_ca_system_score_gemma":0.000008078231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001858662,"about_ca_topic_score_gemma":8.653456e-7,"domain_scores_codex":[0.9996008,0.000006024041,0.0001481283,0.00007666674,0.00007076901,0.00009764571],"domain_scores_gemma":[0.9998127,0.00002058272,0.00003191447,0.00006279298,0.00004786292,0.00002413885],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000007804235,0.00002400766,0.0000439716,0.0001638182,0.00001527072,0.000002333376,0.000158264,0.9862467,0.004137134,0.00003610871,0.0005435647,0.00862102],"study_design_scores_gemma":[0.0002750498,0.00005476377,0.004763442,0.00007171626,0.00001330273,0.000004466551,0.00006520506,0.04586061,0.9478716,0.000345806,0.0004601874,0.0002138224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8393636,0.00006979149,0.1116854,0.00005286154,0.0001878019,0.0001208671,0.0000200173,0.0005250847,0.04797465],"genre_scores_gemma":[0.9955029,0.00001483219,0.004227164,0.00004526669,0.00004575428,0.000003256298,0.00002320028,0.00001120647,0.0001263763],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9437345,"threshold_uncertainty_score":0.6066619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00873973595763764,"score_gpt":0.2183698283289523,"score_spread":0.2096300923713147,"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."}}