{"id":"W2770758745","doi":"10.1177/0954407017737901","title":"Multi-material topology optimization for automotive design problems","year":2017,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering","topic":"Topology Optimization in Engineering","field":"Engineering","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Topology optimization; Automotive industry; Engineering optimization; Conceptual design; Computer science; Mathematical optimization; Chassis; Topology (electrical circuits); Cantilever; Optimization problem; Engineering; Mechanical engineering; Mathematics; Algorithm; 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":[0.001532153,0.001422109,0.001136137,0.001534752,0.0005579963,0.0008377694,0.001043474,0.00139808,0.003568053],"category_scores_gemma":[0.003288552,0.0007364496,0.001180471,0.0009319462,0.0008430941,0.001070484,0.001181046,0.001230561,0.0007149022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007895668,"about_ca_system_score_gemma":0.001014596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009788227,"about_ca_topic_score_gemma":0.00134283,"domain_scores_codex":[0.9993032,0.0002918104,0.00002718329,0.00008889562,0.0002455561,0.00004341948],"domain_scores_gemma":[0.9988338,0.0007863901,0.0001012621,0.00007013999,0.0001726737,0.00003576638],"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":[0.00001158803,0.00002384855,0.0001667351,0.00008279293,0.00001779716,0.00003666718,0.00002524818,0.9597228,0.001189342,0.01372253,0.0003624319,0.02463827],"study_design_scores_gemma":[0.000008054624,0.00001790413,0.00004862088,0.00000894532,0.000003498237,0.00002078822,0.00000853448,0.9902499,0.0004569269,0.00800678,0.001165699,0.000004294206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003336512,0.0001524792,0.9942808,0.00006896691,0.00001534658,0.00004166806,0.00001878793,0.00008220322,0.002003252],"genre_scores_gemma":[0.1551249,0.0004825059,0.8405921,0.00008807304,0.00004709577,0.0004907941,0.0001325028,0.0001938647,0.002848102],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003568053,"threshold_uncertainty_score":0.01193631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0182782927389266,"score_gpt":0.2326568707738414,"score_spread":0.2143785780349148,"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."}}