{"id":"W3133746497","doi":"10.1007/s11665-021-05576-9","title":"Experimental Optimization for Fatigue Life Maximization of Additively Manufactured Ti-6Al-4V Alloy Employing Ultrasonic Impact Treatment","year":2021,"lang":"en","type":"article","venue":"Journal of Materials Engineering and Performance","topic":"Additive Manufacturing Materials and Processes","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Materials science; Residual stress; Fatigue limit; Surface roughness; Composite material; Ultrasonic sensor; Welding; Alloy; Maximization; Stress (linguistics); Acoustics; Mathematics","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":[0.0001288865,0.0002171759,0.0004119544,0.00009620246,0.00004747955,0.00007252979,0.00006266656,0.00007656835,0.0002688844],"category_scores_gemma":[0.00004976292,0.0001808808,0.00007049636,0.00005326225,0.00001365784,0.0002885883,0.00001136362,0.00004529434,3.952065e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007418272,"about_ca_system_score_gemma":0.00004597094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003104188,"about_ca_topic_score_gemma":1.744456e-7,"domain_scores_codex":[0.9991177,0.00001591278,0.0004674085,0.000103017,0.0001118588,0.0001841011],"domain_scores_gemma":[0.9994682,0.00006777563,0.000187244,0.00007711013,0.0001171265,0.0000825203],"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":[0.00009216889,0.00002399636,0.00003339911,0.0003963645,0.0001130779,0.000002436359,0.000219312,0.7253644,0.2733095,0.000003473598,0.00004749469,0.0003943273],"study_design_scores_gemma":[0.000763332,0.0003983642,0.002218986,0.0002379126,0.00004400818,0.00006215587,0.0000419958,0.02700825,0.9687174,0.000002024904,0.000326841,0.0001787387],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9795994,0.0009346794,0.0185182,0.000008058938,0.0005835106,0.0001138481,0.0001924889,0.00003928241,0.00001051783],"genre_scores_gemma":[0.9916476,0.001320384,0.006599417,0.000005267707,0.000278139,0.0000150934,0.00007971399,0.00004270633,0.00001166491],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6983562,"threshold_uncertainty_score":0.7376106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01805516429684688,"score_gpt":0.2393645086586171,"score_spread":0.2213093443617702,"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."}}