{"id":"W2360495543","doi":"","title":"Optimization of die casting processing parameters based on BP neural network and GA algorithm","year":2011,"lang":"en","type":"article","venue":"","topic":"Industrial Technology and Control Systems","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"CAE (Canada)","funders":"","keywords":"Die casting; Casting; Die (integrated circuit); Mold; Artificial neural network; Mechanical engineering; Materials science; Finite element method; Stress (linguistics); Thermal; Process (computing); Backpropagation; Engineering; Metallurgy; Composite material; Structural engineering; Computer science; Artificial intelligence","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":[0.00009523597,0.00007456954,0.0001169977,0.00004035761,0.00003969883,0.000007298354,0.00004141983,0.0001109161,0.000008737504],"category_scores_gemma":[0.00001460331,0.00006643216,0.00001484609,0.00008325637,0.00002374648,0.00005135268,0.000005903627,0.00009892104,4.946431e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009787525,"about_ca_system_score_gemma":0.000005139409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000281839,"about_ca_topic_score_gemma":0.000002980289,"domain_scores_codex":[0.9995877,0.00001370473,0.0001428852,0.00007888776,0.00004288777,0.0001339005],"domain_scores_gemma":[0.9998337,0.00002763566,0.00003439091,0.00006892731,0.00001644723,0.00001886767],"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":[0.00001037477,0.000004679185,0.0003696623,0.00001592488,0.000007555095,0.000002705428,0.00004070333,0.885748,0.00001761459,0.00002237519,0.00001707299,0.1137433],"study_design_scores_gemma":[0.000281467,0.00005462975,0.00008504312,0.00004801231,0.00001008701,0.000002019866,0.00003950559,0.9987763,0.0006016219,0.00002057675,0.00001177236,0.00006896246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05021506,0.0003299299,0.9359986,0.00003004233,0.0004414233,0.0003133472,0.00000198784,0.0005812481,0.01208837],"genre_scores_gemma":[0.9696695,0.000001995187,0.03022631,0.0000229613,0.00004389888,0.00000838458,0.000001255741,0.00001107886,0.00001459196],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9194545,"threshold_uncertainty_score":0.2709025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02616662472288096,"score_gpt":0.1948121871096886,"score_spread":0.1686455623868076,"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."}}