{"id":"W2092863104","doi":"10.4271/2015-01-0582","title":"Design For Six Sigma (DFSS) for Optimization of Stamping Simulation Parameters to Improve Springback Prediction","year":2015,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Metal Forming Simulation Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Chrysler (Canada)","funders":"","keywords":"Design for Six Sigma; Stamping; Six Sigma; Computer science; Manufacturing engineering; Sigma; Engineering; Engineering drawing; Mechanical engineering; Lean manufacturing","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":[0.000458479,0.0006071638,0.0003670625,0.0004274625,0.0002686805,0.0003650452,0.0005712681,0.0004855317,0.004997376],"category_scores_gemma":[0.001093816,0.0003559467,0.0004677396,0.000255357,0.0001749764,0.0002988617,0.0002735852,0.0005323518,0.0007323697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002956305,"about_ca_system_score_gemma":0.0006465307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001595983,"about_ca_topic_score_gemma":0.002450818,"domain_scores_codex":[0.9997745,0.00004544989,0.00001624868,0.00003149196,0.0001099364,0.00002250332],"domain_scores_gemma":[0.9994819,0.0001898543,0.00009566982,0.00004898205,0.0001659387,0.00001767716],"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":[0.0003602893,0.0002637927,0.004685691,0.0005393956,0.00008739872,0.0001932527,0.0003053769,0.6464778,0.1383842,0.005939178,0.002834096,0.1999295],"study_design_scores_gemma":[0.00003426916,0.0002746399,0.000792119,0.00002362171,0.00002261438,0.00004315086,0.00003682973,0.9677063,0.02634289,0.00043593,0.00427294,0.00001466269],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08112729,0.0001780535,0.9100451,0.000110036,0.00005995031,0.0001758405,0.0001742335,0.00262544,0.005504161],"genre_scores_gemma":[0.6341547,0.0001146588,0.3618881,0.00005317806,0.000009679864,0.0003514743,0.0002811108,0.0002840076,0.002863128],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004997376,"threshold_uncertainty_score":0.01671785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03568827068010118,"score_gpt":0.2807106415158833,"score_spread":0.2450223708357821,"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."}}