{"id":"W4231125518","doi":"10.1115/detc2020-22458","title":"Scalable Set-Based Design Optimization and Remanufacturing for Meeting Changing Requirements","year":2020,"lang":"en","type":"article","venue":"","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Scalability; Component (thermodynamics); Remanufacturing; Computer science; Set (abstract data type); Parametric statistics; Process (computing); Functional requirement; Engineering design process; Reliability engineering; Systems engineering; Industrial engineering; Manufacturing engineering; Engineering; Software engineering; Mechanical 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":[0.002161003,0.0008868834,0.0009907043,0.0006712468,0.0003637432,0.0008470551,0.0007347328,0.0007939492,0.001883575],"category_scores_gemma":[0.002774188,0.000633968,0.001148075,0.0004938815,0.0008358398,0.0006194548,0.0009962483,0.0009988105,0.000188388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001047392,"about_ca_system_score_gemma":0.001018127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001378773,"about_ca_topic_score_gemma":0.001316562,"domain_scores_codex":[0.9990402,0.0003530146,0.00004308545,0.0001051494,0.000373706,0.00008475532],"domain_scores_gemma":[0.9988067,0.0007346671,0.0001794809,0.00009507511,0.0001542442,0.00002988178],"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":[0.00001652,0.00001795982,0.00008979937,0.00002775622,0.00001249309,0.00001992631,0.00001806621,0.9870514,0.002302433,0.005141037,0.00006471918,0.005237927],"study_design_scores_gemma":[0.000005511103,0.00004985221,0.00004994086,0.000006110942,0.000004269068,0.000007178441,0.00000549212,0.9949524,0.0008893163,0.00366151,0.0003645015,0.000003884874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02945044,0.000132261,0.9651262,0.0001439916,0.00002058214,0.00009245058,0.00003514307,0.0001423726,0.004856602],"genre_scores_gemma":[0.6984212,0.0001657329,0.2984468,0.00007836938,0.00001260698,0.0003464657,0.00009375633,0.00006522031,0.002369872],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002161003,"threshold_uncertainty_score":0.01142859,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03937179238052269,"score_gpt":0.231245444727818,"score_spread":0.1918736523472953,"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."}}