{"id":"W4246491075","doi":"10.1115/pvp2005-71324","title":"Improving the Reliability of Tube Hydroforming Process by the Taguchi Method","year":2005,"lang":"en","type":"article","venue":"","topic":"Metal Forming Simulation Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Hydroforming; Taguchi methods; Reliability (semiconductor); Process (computing); Design of experiments; Tube (container); Thinning; Optimal design; Orthogonal array; Process variable; Finite element method; Structural engineering; Engineering; Mechanical engineering; Materials science; Computer science; Mathematics; Statistics; Composite material","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":[0.001032076,0.0001093784,0.000126328,0.0000256113,0.00008054883,0.00001789276,0.0002914405,0.0000523262,0.00007855971],"category_scores_gemma":[0.000163019,0.000056054,0.00005874272,0.0001695945,0.00004019284,0.0002263872,0.00003433383,0.0001685523,0.000005623901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003568962,"about_ca_system_score_gemma":0.000009906084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006010611,"about_ca_topic_score_gemma":0.000006368296,"domain_scores_codex":[0.9991976,0.00003416716,0.0003115826,0.0001110997,0.0001839403,0.0001616317],"domain_scores_gemma":[0.999259,0.0002338139,0.0000526471,0.000381689,0.00005039381,0.0000224583],"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":[0.00001652544,0.00007295043,0.000704483,0.0005397985,0.00006977471,2.014363e-7,0.003181673,0.1385518,0.1657505,0.009980583,0.004718916,0.6764128],"study_design_scores_gemma":[0.0000514648,0.000009693313,0.00004507285,0.000006811472,0.00001070357,0.000002044938,0.00007944959,0.4497727,0.5446854,0.001782905,0.003475611,0.00007814246],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7548564,0.000201217,0.2295533,0.0003503018,0.00007560191,0.0005079322,0.000005109751,0.0007282902,0.01372184],"genre_scores_gemma":[0.9750851,0.000004688718,0.0243692,0.00007138648,0.00003052571,0.00002957637,0.00000150954,0.00001972598,0.0003883197],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6763347,"threshold_uncertainty_score":0.2285816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007359448011889165,"score_gpt":0.2696329008457966,"score_spread":0.2622734528339075,"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."}}