{"id":"W2080565684","doi":"10.1007/s00170-011-3648-0","title":"A hybrid-constrained MOGA and local search method to optimize the load path for tube hydroforming","year":2011,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Metal Forming Simulation Techniques","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Hydroforming; Finite element method; Formability; Multi-objective optimization; Genetic algorithm; Mathematical optimization; Engineering; Robustness (evolution); Structural engineering; Computer science; Tube (container); Mechanical engineering; Mathematics; Materials science","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":[0.0007945176,0.0001469866,0.0002009217,0.0002903871,0.00007835467,0.00002543526,0.0008333718,0.00006358913,0.00001958847],"category_scores_gemma":[0.0001287127,0.00009342754,0.000080293,0.00006206562,0.000116828,0.0001825862,0.0001622118,0.0003508028,0.000002156178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001561317,"about_ca_system_score_gemma":0.00003001985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007704321,"about_ca_topic_score_gemma":0.000002232558,"domain_scores_codex":[0.9989705,0.00001832997,0.0003934302,0.0001179051,0.000282492,0.000217326],"domain_scores_gemma":[0.999196,0.0002378886,0.0001177866,0.0001982969,0.0002022911,0.00004772607],"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":[0.0004056852,0.00003074592,0.00001767273,0.00003152497,0.0003933051,0.00004735551,0.0009832332,0.1317739,0.03553959,0.01482225,0.0001790698,0.8157757],"study_design_scores_gemma":[0.0005060241,0.000147841,0.00005102396,0.0000624026,0.00002217461,0.00059463,0.0003118474,0.01593294,0.9112421,0.06774104,0.003267739,0.0001202651],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4222799,0.00008852824,0.5760763,0.0008112301,0.0002135046,0.0002129227,0.000005712396,0.0001283265,0.0001835791],"genre_scores_gemma":[0.7567517,0.00004800495,0.2429914,0.00009179737,0.00004925501,0.00002025791,4.875395e-7,0.00002059019,0.00002648226],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8757025,"threshold_uncertainty_score":0.3809865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01746687374488244,"score_gpt":0.2816961550958847,"score_spread":0.2642292813510023,"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."}}