{"id":"W4285161620","doi":"10.1016/j.procir.2022.03.066","title":"Effect of Chip Segmentation on Machining-Induced Residual Stresses during Turning of Ti6Al4V","year":2022,"lang":"en","type":"article","venue":"Procedia CIRP","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; ArcelorMittal (Canada); National Research Council Canada; Polytechnique Montréal","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Machining; Residual stress; Chip formation; Segmentation; Chip; Surface integrity; Materials science; Finite element method; Residual; Structural engineering; Titanium alloy; Vibration; Mechanical engineering; Tool wear; Engineering; Composite material; Metallurgy; Computer science; Artificial intelligence; Acoustics; Alloy; Physics; Algorithm","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":[0.0001629261,0.0002610394,0.0002593622,0.0001840093,0.0001862094,0.0002376346,0.0002398586,0.0002872361,0.0005524131],"category_scores_gemma":[0.0004749654,0.0001861599,0.0002016542,0.0001410086,0.0003568095,0.0001598749,0.0001240296,0.0001949427,0.00006450996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002222302,"about_ca_system_score_gemma":0.000209365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00106948,"about_ca_topic_score_gemma":0.002488136,"domain_scores_codex":[0.999861,0.00001381352,0.00000752232,0.0000227752,0.00005359014,0.00004120431],"domain_scores_gemma":[0.9995844,0.000228411,0.00007273121,0.00003113116,0.00005990094,0.00002351435],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0008051515,0.0000972478,0.004597365,0.0001348286,0.00003095616,0.0002885658,0.0002484063,0.03343683,0.9510323,0.0001218868,0.00007907827,0.009127344],"study_design_scores_gemma":[0.0000338177,0.002413647,0.05656021,0.00001695016,0.00005850652,0.0002161435,0.0002389604,0.1145258,0.8252207,0.00009803802,0.0005747775,0.00004229971],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985186,0.00007950944,0.001047721,0.000005582167,0.000005015437,0.000005597733,0.0000186227,0.00002493714,0.0002944534],"genre_scores_gemma":[0.9995185,0.00002127975,0.0003765703,0.000003246854,8.984377e-7,0.000002639215,0.00001662968,0.000004612668,0.00005558114],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00106948,"threshold_uncertainty_score":0.002126515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005015013329562401,"score_gpt":0.2355065816422031,"score_spread":0.2304915683126407,"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."}}