{"id":"W2075272867","doi":"10.1007/s00170-009-2505-x","title":"Multi-objective optimization and sensitivity analysis of tube hydroforming","year":2010,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Metal Forming Simulation Techniques","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Hydroforming; Sensitivity (control systems); Tube (container); Industrial and production engineering; Materials science; Engineering; Mechanical engineering; Computer science; Manufacturing engineering; Structural engineering; Electronic engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001927979,0.0008273831,0.001196488,0.001160773,0.000524102,0.001007581,0.0006810017,0.001352089,0.001776696],"category_scores_gemma":[0.004683374,0.0008624471,0.001073784,0.000711371,0.0008923286,0.0007519338,0.0009771172,0.0007761828,0.00009301501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001182358,"about_ca_system_score_gemma":0.0007993231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008237391,"about_ca_topic_score_gemma":0.004278779,"domain_scores_codex":[0.9993476,0.0004058408,0.00001661196,0.00004816618,0.0001201204,0.00006166356],"domain_scores_gemma":[0.9969364,0.002658234,0.0001412582,0.00006139955,0.0001617754,0.00004084564],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001015387,0.000008227176,0.0000753967,0.00001049699,0.000007172532,0.00001089093,0.00000469232,0.9983432,0.0002080087,0.0005558537,0.00002388419,0.0007419965],"study_design_scores_gemma":[0.00000129756,0.000005145863,0.00005309993,0.000001148223,0.000001732923,0.000001673026,0.000001800931,0.9995598,0.0001368153,0.0002141422,0.00002184838,0.000001513017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.409244,0.001113686,0.5688007,0.0006631913,0.0001192033,0.0001694349,0.0003023431,0.0002605723,0.01932682],"genre_scores_gemma":[0.9768967,0.0001579027,0.02080422,0.00004140055,0.00001527597,0.00006937163,0.0000517203,0.00004634334,0.001917111],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008237391,"threshold_uncertainty_score":0.01637888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00563793322799906,"score_gpt":0.2488250030030173,"score_spread":0.2431870697750182,"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."}}