{"id":"W2560257588","doi":"10.1115/detc2016-59627","title":"Embedded Sensors and Feedback Loops for Iterative Improvement in Design Synthesis for Additive Manufacturing","year":2016,"lang":"en","type":"article","venue":"","topic":"Design Education and Practice","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Autodesk (Canada)","funders":"","keywords":"Chassis; Computer science; Design space exploration; Iterative design; Design methods; Automotive industry; Systems engineering; Control engineering; Embedded system; Distributed computing; Computer engineering; Industrial engineering; Engineering; Mechanical 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":[0.002441185,0.001356511,0.0005543898,0.001064179,0.0004736569,0.001058009,0.0009544675,0.0008125997,0.00290344],"category_scores_gemma":[0.006414166,0.0007554312,0.0009391722,0.0005751287,0.001334002,0.00107124,0.001391493,0.001315554,0.0004237292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008264592,"about_ca_system_score_gemma":0.001083195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000932536,"about_ca_topic_score_gemma":0.001240769,"domain_scores_codex":[0.9981502,0.0005786286,0.00009706026,0.0002651184,0.0008167262,0.00009224145],"domain_scores_gemma":[0.9973632,0.001630312,0.0003140331,0.0002785221,0.000375231,0.00003878411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000799445,0.000125518,0.0006083409,0.0003171511,0.0000437769,0.0001061118,0.0005374035,0.8046096,0.02937379,0.04324731,0.0005274697,0.1204236],"study_design_scores_gemma":[0.00003627827,0.0001947349,0.0001468094,0.00006494917,0.0000225622,0.00004224554,0.00005862288,0.9593843,0.01252552,0.02237475,0.005129491,0.00001967957],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006850321,0.000093445,0.9903289,0.00005166965,0.00001651937,0.00007078741,0.000009897938,0.0003337873,0.002244622],"genre_scores_gemma":[0.1970814,0.0001594759,0.8009172,0.00006888893,0.00001432696,0.0003470673,0.00004759871,0.000120635,0.001243453],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00290344,"threshold_uncertainty_score":0.01291037,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02301514500753953,"score_gpt":0.256131322114467,"score_spread":0.2331161771069275,"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."}}