{"id":"W2291436247","doi":"10.1139/tcsme-2010-0024","title":"FUZZY TAGUCHI DEDUCTION OPTIMIZATION ON MULTI-ATTRIBUTE CNC TURNING","year":2010,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science Council","keywords":"TOPSIS; Taguchi methods; Ideal solution; Orthogonal array; Numerical control; Machining; Benchmark (surveying); Fuzzy logic; Similarity (geometry); Preference; Ideal (ethics); Computer science; Mathematics; Mathematical optimization; Engineering; Artificial intelligence; Mechanical engineering; Statistics; Operations research","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":[0.001708285,0.000601671,0.0007672993,0.0007686837,0.0004281985,0.0008296864,0.0005460792,0.0004658889,0.0004965888],"category_scores_gemma":[0.001985306,0.000320823,0.0006554124,0.0007241716,0.0005955708,0.0004053222,0.0003508096,0.000511302,0.00008999812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007445943,"about_ca_system_score_gemma":0.0009125067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002751351,"about_ca_topic_score_gemma":0.002949274,"domain_scores_codex":[0.9982792,0.0004667936,0.00006654222,0.0001761191,0.0009032855,0.000108099],"domain_scores_gemma":[0.9992078,0.0003892665,0.00009258714,0.00005511248,0.0002337141,0.00002162958],"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":[0.000237293,0.0001655969,0.001599474,0.0002364838,0.00004724148,0.0001081714,0.0002264395,0.7826691,0.04916919,0.006265082,0.0003370401,0.1589389],"study_design_scores_gemma":[0.00002599971,0.0003192557,0.0008632931,0.00001017329,0.00002793842,0.00004061126,0.00003188776,0.9843598,0.01207867,0.001634132,0.0005913609,0.00001680867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1747321,0.0001492008,0.8220314,0.00004184638,0.00002066097,0.0001073954,0.00001620019,0.0001166913,0.002784578],"genre_scores_gemma":[0.8226024,0.00007236256,0.1763568,0.00001938221,0.000005901294,0.00008217946,0.00003607975,0.0000138318,0.0008110883],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002751351,"threshold_uncertainty_score":0.009034395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009087746676520651,"score_gpt":0.2109017885349232,"score_spread":0.2018140418584025,"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."}}