{"id":"W4378893238","doi":"10.1520/mpc20220088","title":"High Cycle Fatigue and Very High Cycle Fatigue Performance of Selective Laser Melting Ti-6Al-4V Titanium Alloy—A Review","year":2023,"lang":"en","type":"article","venue":"Materials Performance and Characterization","topic":"Additive Manufacturing Materials and Processes","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Selective laser melting; Materials science; Titanium alloy; Aerospace; Residual stress; Context (archaeology); Surface roughness; Fatigue limit; Flexibility (engineering); Fatigue testing; Alloy; Surface finish; Porosity; Mechanical engineering; Metallurgy; Composite material; Microstructure; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002641964,0.0002661381,0.0004589053,0.0001029399,0.0001530518,0.00008585375,0.00009486773,0.0001007872,0.0001964932],"category_scores_gemma":[0.00001682702,0.0002475496,0.00001941439,0.0002154891,0.00004952099,0.0006970638,0.00007242958,0.000072827,0.00002964606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002261769,"about_ca_system_score_gemma":0.00001341984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001892531,"about_ca_topic_score_gemma":0.000001298539,"domain_scores_codex":[0.9988003,0.00003640986,0.0004613122,0.0002514542,0.0001585704,0.0002919332],"domain_scores_gemma":[0.999512,0.00002943828,0.000175448,0.0001495937,0.00008052195,0.00005302742],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007742002,0.0000188095,0.001598246,0.01209391,0.00006061588,0.000002292167,0.0003576807,0.001252199,0.978367,0.00003088205,0.0002093526,0.005931579],"study_design_scores_gemma":[0.0002593009,0.00009628051,0.1673752,0.001317367,0.00003759004,0.000006494454,0.00001430878,0.001904407,0.828323,0.00001510435,0.0003600307,0.0002909305],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984107,0.000126004,0.00005126114,0.0000470735,0.000533114,0.0002915443,0.000237432,0.0002364278,0.00006645073],"genre_scores_gemma":[0.9730271,0.0255002,0.00007413555,0.00007227649,0.000220016,0.00007180975,0.0009253595,0.00005152476,0.00005758089],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.165777,"threshold_uncertainty_score":0.9999977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01088147225955302,"score_gpt":0.2111535297961157,"score_spread":0.2002720575365627,"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."}}