{"id":"W4409017727","doi":"10.4271/2025-01-8318","title":"CFD Modeling of Elastomeric Silicone Material Dispensing to Enable Equipment Design & Optimization for Efficient Automotive Manufacturing","year":2025,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dow Chemical (Canada)","funders":"","keywords":"Automotive industry; Silicone; Elastomer; Automotive engineering; Manufacturing engineering; Computer science; Mechanical engineering; Materials science; Engineering; Composite material; Aerospace 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":[0.0003635466,0.0007519076,0.000623113,0.0006047615,0.0005552309,0.001374869,0.0007408236,0.001517491,0.008390647],"category_scores_gemma":[0.001153672,0.0004859424,0.000958304,0.0005093582,0.0003630134,0.0005749322,0.0005646519,0.0005911303,0.001588038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001218189,"about_ca_system_score_gemma":0.001708734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01213127,"about_ca_topic_score_gemma":0.00708043,"domain_scores_codex":[0.9997702,0.0000334579,0.0000131047,0.0000262981,0.0001173048,0.0000395867],"domain_scores_gemma":[0.9995788,0.0002079345,0.00003830481,0.00002988751,0.0001181972,0.00002699597],"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":[0.00004673256,0.000035088,0.0006125,0.00006991331,0.000006876284,0.00008882239,0.00003772832,0.9819235,0.005115667,0.002473257,0.001093125,0.00849668],"study_design_scores_gemma":[0.000005496342,0.00001296193,0.0001108598,0.000008255883,0.000002538362,0.00001227761,0.000008154067,0.9960327,0.001603674,0.0002062771,0.001991488,0.000005231473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1866553,0.001948366,0.6619371,0.001309238,0.0005840849,0.0004207537,0.002927848,0.003911448,0.1403059],"genre_scores_gemma":[0.8419917,0.001392658,0.1150452,0.0002021378,0.00005057095,0.0003591742,0.001499943,0.0005046481,0.03895389],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01213127,"threshold_uncertainty_score":0.0280695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01752550461904177,"score_gpt":0.2447125393627944,"score_spread":0.2271870347437527,"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."}}