{"id":"W2023504215","doi":"10.1007/s00170-012-4303-0","title":"Robustness study and reliability growth based on exploratory design of experiments and statistical analysis: a case study using a train door test bench","year":2012,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Bombardier (Canada)","funders":"Ministère de l'Education Nationale, de l'Enseignement Superieur et de la Recherche","keywords":"Robustness (evolution); Reliability engineering; Test bench; Computer science; Mechatronics; Design of experiments; Multivariate statistics; Reliability (semiconductor); Engineering; Artificial intelligence; Machine learning; Statistics; Mathematics","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":[0.003409366,0.000168727,0.000433794,0.0008874917,0.00009607339,0.0000479082,0.0006173488,0.00006012836,0.00001437699],"category_scores_gemma":[0.003059905,0.0001037872,0.00004929332,0.0003354551,0.0002255647,0.0002601545,0.0001752383,0.0002802677,2.257322e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009836844,"about_ca_system_score_gemma":0.00005130028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001598844,"about_ca_topic_score_gemma":0.000003407888,"domain_scores_codex":[0.9976841,0.0002485206,0.0007824613,0.0002555496,0.0008427437,0.000186648],"domain_scores_gemma":[0.9960601,0.002652279,0.0004988554,0.0003354274,0.000368504,0.00008478936],"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":[0.0005465568,0.00270356,0.05219344,0.000008302823,0.0005224914,0.0008243681,0.002780837,0.9324394,0.001680778,0.0001548106,0.00001205588,0.006133374],"study_design_scores_gemma":[0.01335514,0.01060892,0.1155127,0.0002232817,0.002469327,0.005289909,0.1686117,0.5967852,0.06256995,0.02325171,0.00001721564,0.001304983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.605347,0.00005270258,0.3941655,0.00008575775,0.0001164737,0.0002152903,0.000005311134,0.0000109117,0.000001092283],"genre_scores_gemma":[0.954531,0.000004216639,0.0453963,0.00001483731,0.00002998012,0.00001247286,1.661817e-7,0.00000911293,0.000001975466],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3491839,"threshold_uncertainty_score":0.4232319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07297041722439629,"score_gpt":0.3627122915227605,"score_spread":0.2897418742983642,"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."}}