{"id":"W4404631526","doi":"10.1016/j.mtcomm.2024.111078","title":"Optimization of blind riveting process parameters of aluminum alloy based on finite element simulation","year":2024,"lang":"en","type":"article","venue":"Materials Today Communications","topic":"Metallurgy and Material Forming","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"MD Precision (Canada)","funders":"","keywords":"Rivet; Materials science; Finite element method; Alloy; Aluminium; Process (computing); Metallurgy; Mechanical engineering; Process optimization; Composite material; Structural engineering; Computer science; Engineering; Chemical 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004204221,0.0001124164,0.0001908814,0.0001543842,0.00005420639,0.00004191499,0.0002640072,0.00006266472,0.0002160941],"category_scores_gemma":[0.00008563569,0.0001121467,0.0000384358,0.0001761343,0.00004474267,0.0001290823,0.00004465489,0.00005336869,0.000008428729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002446628,"about_ca_system_score_gemma":0.00002236827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001495601,"about_ca_topic_score_gemma":0.000002460321,"domain_scores_codex":[0.9990791,0.00008352356,0.0005051892,0.0000986113,0.0001249583,0.0001085901],"domain_scores_gemma":[0.9989628,0.0002963229,0.00009467765,0.0005568243,0.00006642863,0.00002295788],"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":[0.00002191197,0.00002980793,0.000003375091,0.0003695456,0.0000277169,1.704244e-7,0.0003361167,0.9336601,0.06478142,0.0004195042,0.000006855092,0.000343409],"study_design_scores_gemma":[0.0001477745,0.00003287608,0.000009324678,0.0002888096,0.00003304645,1.460785e-7,0.00004223433,0.7994394,0.1995136,0.00008548299,0.0003200255,0.00008723341],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8743252,0.0001901283,0.1202133,0.000183863,0.0009944489,0.0008216428,0.0002067057,0.0004078873,0.002656852],"genre_scores_gemma":[0.9805773,0.00005694514,0.01897724,0.00001113397,0.00001690156,0.00005105153,0.0002652354,0.00002779967,0.00001638172],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1347322,"threshold_uncertainty_score":0.457321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03610155843784216,"score_gpt":0.2937522581129296,"score_spread":0.2576506996750875,"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."}}