{"id":"W4396597525","doi":"10.1108/rpj-11-2023-0400","title":"Design of a metal additive manufactured aircraft seat leg using topology optimization and part decomposition","year":2024,"lang":"en","type":"article","venue":"Rapid Prototyping Journal","topic":"Topology Optimization in Engineering","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Airworthiness; Topology optimization; Volume (thermodynamics); Conceptual design; Engineering; Component (thermodynamics); Certification; Reduction (mathematics); Automotive engineering; Decomposition; Mechanical engineering; Computer science; Structural engineering; Finite element method","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":[0.0002100479,0.0003225149,0.0002339943,0.0003917343,0.0001387825,0.000378021,0.0004283174,0.0002598602,0.001948834],"category_scores_gemma":[0.0002413554,0.0001533656,0.0004096762,0.0001609124,0.0002037698,0.0001754333,0.0002359632,0.0001982844,0.0003734505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000227804,"about_ca_system_score_gemma":0.0004278857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004720397,"about_ca_topic_score_gemma":0.0009346619,"domain_scores_codex":[0.9998389,0.00002812552,0.000008328974,0.00001962291,0.00008495951,0.00001992953],"domain_scores_gemma":[0.9998785,0.00002318665,0.00003097768,0.00001915876,0.00003898392,0.000009165442],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001730572,0.0001208205,0.002293135,0.0004922949,0.00006573171,0.0006454472,0.0001508428,0.4217502,0.4399094,0.01038252,0.001220599,0.1227959],"study_design_scores_gemma":[0.00005832503,0.001639059,0.003938034,0.0000434896,0.00008683875,0.0008102197,0.000118091,0.867819,0.1054828,0.002399075,0.01756997,0.00003517689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.354118,0.0002560569,0.6242356,0.0001129716,0.0000607793,0.0002044101,0.0002018646,0.0006154048,0.02019485],"genre_scores_gemma":[0.7815782,0.0001375897,0.2133257,0.00003956499,0.000007418631,0.0000998276,0.0001944382,0.00006902587,0.004548055],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001948834,"threshold_uncertainty_score":0.006519496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0153350118909408,"score_gpt":0.2542972340394108,"score_spread":0.23896222214847,"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."}}