{"id":"W4399245390","doi":"10.1016/j.eswa.2024.124247","title":"Precision refined: Integrating micromachining constraints for enhanced product accuracy through topology optimization","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Topology Optimization in Engineering","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Tech University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Computer science; Topology optimization; Topology (electrical circuits); Product (mathematics); Surface micromachining; Mathematical optimization; Mathematics; Physics; Geometry","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":[0.0007555744,0.0009132292,0.0008220573,0.0005578905,0.0003337303,0.001149875,0.001515063,0.000930164,0.004445538],"category_scores_gemma":[0.002449298,0.0006321664,0.0005704274,0.0005876631,0.0005954553,0.001655629,0.001560826,0.001177138,0.0007808291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004981039,"about_ca_system_score_gemma":0.0009412922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001265509,"about_ca_topic_score_gemma":0.003138665,"domain_scores_codex":[0.9992769,0.0001049439,0.00003423998,0.0001233848,0.0004086274,0.00005197784],"domain_scores_gemma":[0.9992365,0.0002056753,0.0001027228,0.0002631758,0.0001664591,0.00002559832],"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":[0.00009377635,0.00007820541,0.0005198157,0.0001809201,0.00006064903,0.00008977075,0.0001018663,0.7907207,0.06347373,0.03662815,0.001103727,0.1069487],"study_design_scores_gemma":[0.00002015855,0.00009999941,0.0002283595,0.00002149461,0.00002644575,0.00006130063,0.00001752688,0.9636534,0.01978979,0.01204722,0.004015409,0.00001885881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03053814,0.0002104145,0.9598602,0.0001024703,0.00005096738,0.00003226303,0.00006356285,0.0004788493,0.008663111],"genre_scores_gemma":[0.5766341,0.0002550714,0.4166761,0.00009647781,0.00003558989,0.00007115429,0.0001728973,0.0005097941,0.005548707],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004445538,"threshold_uncertainty_score":0.01487184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01137071555146123,"score_gpt":0.2777937294845028,"score_spread":0.2664230139330416,"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."}}