{"id":"W2946829374","doi":"10.1007/s00170-019-03863-3","title":"Hard turning multi-performance optimization for improving the surface integrity of 300M ultra-high strength steel","year":2019,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; École de Technologie Supérieure; Polytechnique Montréal","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Héroux-Devtek; Mitacs; Consortium de Recherche et d’innovation en Aérospatiale au Québec; Pratt and Whitney Canada","keywords":"Surface integrity; Hardened steel; Surface roughness; Materials science; Taguchi methods; Context (archaeology); Mechanical engineering; Boron nitride; Response surface methodology; Machining; Composite material; Metallurgy; Engineering; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002140052,0.0005591415,0.0004852352,0.0004478539,0.0003147466,0.0007091873,0.0005380054,0.0005744012,0.001151639],"category_scores_gemma":[0.0002333258,0.0002847413,0.0004538867,0.0002593743,0.0002537765,0.0002770168,0.000368164,0.0003378386,0.0001649688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005296246,"about_ca_system_score_gemma":0.0004011245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001659322,"about_ca_topic_score_gemma":0.002981696,"domain_scores_codex":[0.9998079,0.00001645242,0.000004972852,0.00002903699,0.00009507579,0.00004650389],"domain_scores_gemma":[0.9998498,0.00003385673,0.00002948666,0.00001259468,0.00006054565,0.00001377608],"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":[0.0003942087,0.0001555272,0.001701735,0.0001650565,0.00008254916,0.0001671263,0.00007963143,0.6570194,0.2946189,0.001346496,0.0006032075,0.04366628],"study_design_scores_gemma":[0.00002244382,0.0005639485,0.003037337,0.000004852548,0.00004080744,0.00004214378,0.00004044317,0.9401385,0.0549436,0.0003418476,0.0008064015,0.00001780789],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9147419,0.0006393956,0.07398009,0.0001197026,0.00005206229,0.00002762709,0.000050923,0.0003274646,0.01006078],"genre_scores_gemma":[0.994749,0.00003976967,0.004142201,0.000008117335,0.000003301926,0.000007040219,0.00003198785,0.00002131575,0.0009973305],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001659322,"threshold_uncertainty_score":0.003852665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007657788777065,"score_gpt":0.2312116513605131,"score_spread":0.2235538625834481,"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."}}