{"id":"W3216255475","doi":"10.1016/j.cirpj.2021.11.003","title":"Shot peen forming pattern optimization to achieve cylindrical and saddle target shapes: The inverse problem","year":2021,"lang":"en","type":"article","venue":"CIRP journal of manufacturing science and technology","topic":"Laser and Thermal Forming Techniques","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Peening; Shot peening; Curvature; Fuselage; Inverse; Structural engineering; Saddle; Shot (pellet); Sheet metal; Process (computing); Plane (geometry); Materials science; Mechanics; Engineering; Geometry; Computer science; Mathematics; Physics; Composite material; Residual stress; Metallurgy","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":[],"consensus_categories":[],"category_scores_codex":[0.0004272748,0.0000976267,0.0001436398,0.0002711533,0.0001902296,0.00008088099,0.0002955054,0.00007543327,0.00001026944],"category_scores_gemma":[0.00006346223,0.00006849215,0.00001983245,0.0002874921,0.000198109,0.0003218066,0.000170062,0.0002700013,9.659655e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004598604,"about_ca_system_score_gemma":0.00004657677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002281689,"about_ca_topic_score_gemma":0.000006629456,"domain_scores_codex":[0.9992446,0.000007966847,0.0002020064,0.0001320416,0.0001873271,0.0002260736],"domain_scores_gemma":[0.999615,0.00002571542,0.00005393216,0.0001155179,0.0001114718,0.00007834038],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005772102,0.0001294566,0.005361293,0.0003438978,0.0001861257,0.0005218408,0.0052456,0.1165955,0.08161207,0.001827118,0.001487575,0.7866318],"study_design_scores_gemma":[0.0004224607,0.0003710814,0.001347325,0.0002066057,0.00003823193,0.001828316,0.0009688152,0.03347611,0.9434376,0.01237039,0.005190144,0.0003429625],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9605162,0.0002104751,0.03626471,0.002084988,0.0001100301,0.0001039334,0.000001734578,0.000117836,0.0005901373],"genre_scores_gemma":[0.9864768,0.0001431293,0.01319381,0.0001294677,0.00003225624,0.000003221401,2.77611e-7,0.000009375427,0.00001159163],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8618255,"threshold_uncertainty_score":0.2793029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0104334474063241,"score_gpt":0.223312639063015,"score_spread":0.2128791916566909,"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."}}