{"id":"W4283210731","doi":"10.2514/6.2022-3724","title":"Multi-Material Topology Optimization for the Conceptual Design of an Additively Manufactured Aerospace Smart Table","year":2022,"lang":"en","type":"article","venue":"AIAA AVIATION 2022 Forum","topic":"Topology Optimization in Engineering","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Topology optimization; Aerospace; Table (database); Computer science; Stiffness; 3D printing; Mechanical engineering; Manufacturing engineering; Topology (electrical circuits); Engineering drawing; Engineering; Structural engineering; Finite element method; Aerospace 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002444152,0.000152598,0.0001800669,0.00009693643,0.0003252983,0.00001818429,0.0002344056,0.00008535225,0.002050258],"category_scores_gemma":[0.00006620844,0.0001556862,0.00004251517,0.0001992591,0.00008075962,0.0002149091,0.00006427919,0.0001411074,0.000001980444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001177577,"about_ca_system_score_gemma":0.00003228432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001922981,"about_ca_topic_score_gemma":0.000008989829,"domain_scores_codex":[0.9990076,0.00009015668,0.000287145,0.0001872405,0.0001467527,0.0002811409],"domain_scores_gemma":[0.99937,0.0002010316,0.0001069957,0.0002170524,0.00006808197,0.00003682628],"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":[0.00004619196,0.00002423502,0.00003686839,0.000007913452,0.00004688581,4.124468e-7,0.0004633816,0.9927421,0.002546887,0.0008159915,0.003069473,0.0001996465],"study_design_scores_gemma":[0.0007731772,0.0001592056,0.0001456318,0.000001675922,0.00002878368,0.000006090923,0.001431337,0.9831477,0.00984209,0.00004536726,0.004257705,0.0001612659],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002789586,0.00009092068,0.9933525,0.0002446389,0.001965648,0.0007367799,0.0005685412,0.0002273655,0.00002396078],"genre_scores_gemma":[0.8874165,0.00002827886,0.1098851,0.0001527313,0.00009631519,0.0009865692,0.001005632,0.00008166226,0.0003471966],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8846269,"threshold_uncertainty_score":0.998862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01270809198208673,"score_gpt":0.2202986680018618,"score_spread":0.2075905760197751,"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."}}