{"id":"W2801929927","doi":"10.1139/tcsme-2016-0072","title":"OPTIMAL PREDICTION AND DESIGN OF SURFACE ROUGHNESS FOR CNC TURNING OF AL7075-T6 BY USING THE TAGUCHI HYBRID QPSO ALGORITHM","year":2016,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Science and Technology, Taiwan","keywords":"Taguchi methods; Particle swarm optimization; Surface roughness; Machining; Orthogonal array; Numerical control; Response surface methodology; Mechanical engineering; Engineering; Algorithm; Mathematical optimization; Computer science; Materials science; Mathematics; Composite material; Machine learning","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":[0.0004349374,0.0005711358,0.0005471538,0.0004624755,0.0002346733,0.0005447402,0.0005841756,0.000684473,0.000526907],"category_scores_gemma":[0.000608905,0.0004049566,0.000837232,0.0002523962,0.0003040757,0.0002375279,0.0002146062,0.0004319635,0.0001278656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003988331,"about_ca_system_score_gemma":0.0008906734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005144077,"about_ca_topic_score_gemma":0.004477572,"domain_scores_codex":[0.9998161,0.00003040131,0.000009020613,0.00003354077,0.00008403932,0.00002690737],"domain_scores_gemma":[0.9998102,0.00007008787,0.00003139532,0.0000126112,0.00006348194,0.0000122506],"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":[0.00007581274,0.00007814458,0.001268956,0.0001079229,0.00003059348,0.00005437426,0.0000480753,0.9200135,0.03655916,0.000803898,0.0002236913,0.04073581],"study_design_scores_gemma":[0.000007560128,0.00004976592,0.0003177787,0.000002144106,0.000007796393,0.000006610955,0.000006815087,0.9969797,0.002420425,0.00009946623,0.00009820171,0.000003801065],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2341115,0.0002814818,0.7618933,0.00009656027,0.00002833362,0.0001026809,0.00003910827,0.0003572325,0.003089719],"genre_scores_gemma":[0.9108071,0.0001233977,0.08821484,0.000032844,0.00000685752,0.00009776164,0.00006545085,0.00002550578,0.0006263195],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005144077,"threshold_uncertainty_score":0.01022828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01019875545258532,"score_gpt":0.205399976096457,"score_spread":0.1952012206438717,"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."}}