{"id":"W4280561232","doi":"10.1016/j.ijfatigue.2022.107017","title":"Microstructure and low-cycle fatigue performance of selective electron beam melted Ti6Al4V alloy","year":2022,"lang":"en","type":"article","venue":"International Journal of Fatigue","topic":"Additive Manufacturing Materials and Processes","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China; Natural Science Foundation of Shandong Province; China Scholarship Council; Ryerson University","keywords":"Misorientation; Materials science; Titanium alloy; Microstructure; Ultimate tensile strength; Cathode ray; Alloy; Beam (structure); Composite material; Hysteresis; Process window; Deformation (meteorology); Texture (cosmology); Metallurgy; Electron; Structural engineering; Optoelectronics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000123414,0.0001097278,0.0001750671,0.0001394606,0.00005281044,0.00002773776,0.0002462388,0.00002795042,0.0001964017],"category_scores_gemma":[0.00002605881,0.0001017908,0.00004334141,0.00006728043,0.0000307052,0.0001820175,0.00005350764,0.000227852,4.48792e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009831564,"about_ca_system_score_gemma":0.00003944623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008344407,"about_ca_topic_score_gemma":0.000001527765,"domain_scores_codex":[0.9991709,0.000022098,0.0003092047,0.00007672627,0.0003076106,0.0001134864],"domain_scores_gemma":[0.9994509,0.00004886791,0.0001992657,0.00004521224,0.0002201883,0.00003558654],"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":[0.000517451,0.00008322045,0.004559111,0.0002065931,0.0007326513,0.00003836363,0.001774314,0.3412429,0.6421351,0.000135636,0.002378072,0.006196545],"study_design_scores_gemma":[0.0007073957,0.0003239647,0.02408352,0.00009044693,0.00002336824,0.0002609001,0.0001170977,0.002081793,0.9698982,0.0004247126,0.001808761,0.0001798453],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983528,0.0003049657,0.000296478,0.00006316622,0.0005954183,0.00004817957,0.00008130952,0.00001595938,0.0002417168],"genre_scores_gemma":[0.9993306,0.0001658863,0.0002556489,0.00003388652,0.0001570228,0.000003855776,0.00001757482,0.00001694753,0.0000185534],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3391611,"threshold_uncertainty_score":0.4150909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007621197751357676,"score_gpt":0.2324287182410129,"score_spread":0.2248075204896552,"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."}}