{"id":"W4302307325","doi":"10.1007/s00158-022-03394-8","title":"Partitioning a topology-optimized structure into additively manufacturable parts using a feature-mapping approach: a novel decomposition optimization method","year":2022,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Topology Optimization in Engineering","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Topology optimization; Rectangle; Component (thermodynamics); Decomposition; Mathematical optimization; Topology (electrical circuits); Constraint (computer-aided design); Computer science; Algorithm; Decomposition method (queueing theory); Feature (linguistics); Domain decomposition methods; Domain (mathematical analysis); Mathematics; Finite element method; Engineering; Geometry; Structural 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":[0.0003871637,0.001087184,0.0008699652,0.0006700644,0.0003118615,0.000695518,0.0008486179,0.001000563,0.002522437],"category_scores_gemma":[0.0007134604,0.0005755951,0.001075846,0.0006106404,0.0004250185,0.0008872823,0.0008769062,0.000916442,0.0005480996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003625668,"about_ca_system_score_gemma":0.0006290612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001070321,"about_ca_topic_score_gemma":0.001610295,"domain_scores_codex":[0.9998152,0.00004678902,0.000008363902,0.000042143,0.00006822959,0.00001944627],"domain_scores_gemma":[0.999817,0.00009205536,0.00001747261,0.00002913006,0.00003302001,0.00001138221],"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":[0.00003944177,0.00009116423,0.0001901636,0.0001162101,0.00005227553,0.00005700365,0.00005333174,0.8922688,0.02117288,0.01377605,0.001196158,0.07098641],"study_design_scores_gemma":[0.000004101829,0.00001734451,0.00003770749,0.000003515668,0.000006610153,0.00001780442,0.00000584786,0.9962496,0.001147943,0.001990195,0.0005159833,0.000003271386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004795369,0.00004808569,0.9936413,0.00002704619,0.00001383457,0.00001509531,0.00001900663,0.000089464,0.0013509],"genre_scores_gemma":[0.1630985,0.0001870864,0.832872,0.00007619355,0.00003294868,0.0001802391,0.0001682165,0.0002946836,0.003090168],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002522437,"threshold_uncertainty_score":0.008438408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0133971187166717,"score_gpt":0.2638517054564335,"score_spread":0.2504545867397618,"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."}}