{"id":"W4323041803","doi":"10.18280/mmep.100121","title":"Experimental and Metamodel Based Optimization of Cutting Parameters for Milling Inconel-800 Superalloy Under Nanofluid MQL Condition","year":2023,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Trường Đại học Nha Trang","keywords":"Inconel; Machining; Machinability; Lubrication; Superalloy; Nanofluid; Materials science; Mechanical engineering; Tool wear; Metallurgy; Process engineering; Computer science; Nanoparticle; Composite material; Engineering; Microstructure; Nanotechnology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004822384,0.0004489549,0.0004029211,0.000329331,0.0003079987,0.0003870924,0.0003463672,0.0005963069,0.001457785],"category_scores_gemma":[0.0005717297,0.000175958,0.0003995548,0.0003664129,0.0002493071,0.000317573,0.0002043116,0.0003477634,0.0001182024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002998876,"about_ca_system_score_gemma":0.0003332765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001845903,"about_ca_topic_score_gemma":0.002948918,"domain_scores_codex":[0.9998797,0.00001441661,0.000007155865,0.00002897167,0.00004767886,0.0000220103],"domain_scores_gemma":[0.9996657,0.000131546,0.00006871013,0.00003784386,0.00008414576,0.00001199797],"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":[0.0004727195,0.0005328098,0.004236495,0.0008375706,0.00004548582,0.0001921979,0.0002308693,0.68675,0.2700567,0.00165325,0.0007976887,0.03419423],"study_design_scores_gemma":[0.00004476175,0.00100347,0.005030524,0.00001838456,0.00003212497,0.00003263756,0.0001291226,0.8554398,0.1370866,0.0003112788,0.0008392621,0.00003197639],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9667206,0.0002381735,0.02768945,0.00009605906,0.00002612579,0.00004748947,0.000216311,0.0001935148,0.004772139],"genre_scores_gemma":[0.9903018,0.00005002658,0.009093457,0.000005814777,0.000001092057,0.00003080937,0.00007079729,0.00001052418,0.0004356945],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001845903,"threshold_uncertainty_score":0.004876733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02510583982267969,"score_gpt":0.2348329800829437,"score_spread":0.209727140260264,"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."}}