{"id":"W3086383380","doi":"10.1007/s00170-020-06027-w","title":"Effect of turning environments and parameters on surface integrity of AA6061-T6: experimental analysis, predictive modeling, and multi-criteria optimization","year":2020,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Surface integrity; Surface roughness; Response surface methodology; Machining; Residual stress; Central composite design; Residual; Lubrication; Design of experiments; Regression analysis; Mechanical engineering; Structural engineering; Engineering; Materials science; Composite material; Computer science; Mathematics; Algorithm; Statistics; Machine learning","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":[0.0001269729,0.0001290816,0.0002703101,0.0001692224,0.00002475005,0.00001009821,0.0002206753,0.00006549407,0.000003989754],"category_scores_gemma":[0.00009953045,0.00009862394,0.00004892753,0.00008372912,0.00008254078,0.0001665149,0.00008226721,0.0002687483,7.049914e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000429834,"about_ca_system_score_gemma":0.000004132654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002250369,"about_ca_topic_score_gemma":3.417005e-7,"domain_scores_codex":[0.9992798,0.00002468342,0.000326589,0.0001178657,0.0001695301,0.00008153066],"domain_scores_gemma":[0.9995014,0.00009756422,0.0002571224,0.00007501935,0.00003730064,0.00003157806],"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":[0.0003257627,0.00001625912,0.0001937784,0.00002259293,0.0003030115,0.000002090076,0.0003084096,0.9734793,0.02266053,0.00001154186,5.182561e-7,0.002676196],"study_design_scores_gemma":[0.0005102672,0.0003323522,0.00003639293,0.00004038852,0.00006139572,0.000005530404,0.0001404447,0.580994,0.4177856,0.00004540717,0.000002638344,0.00004569135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6248565,0.0003374366,0.3745643,0.00008636035,0.00007054828,0.00005811383,0.000004602683,0.00001835903,0.000003841004],"genre_scores_gemma":[0.9465595,0.0006138933,0.05278153,0.00001411205,0.00001141537,0.000001802907,0.000003942367,0.00001299267,8.346339e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.395125,"threshold_uncertainty_score":0.4021767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00898109875614018,"score_gpt":0.2635940265329377,"score_spread":0.2546129277767975,"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."}}