{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007464638,0.001226861,0.0008733173,0.001055688,0.0003376171,0.0006821283,0.0008629127,0.001143024,0.001122251],"category_scores_gemma":[0.001668875,0.0006087126,0.0005024586,0.0006681834,0.000398052,0.0006046889,0.0002878697,0.0006978685,0.0002825492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008350752,"about_ca_system_score_gemma":0.0009788353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008807978,"about_ca_topic_score_gemma":0.006638546,"domain_scores_codex":[0.999697,0.00006742801,0.00001662473,0.00005694878,0.0001034145,0.00005850385],"domain_scores_gemma":[0.9994823,0.0002300869,0.00005465148,0.00002837529,0.0001858816,0.00001872009],"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":[0.0001110971,0.00007187481,0.000696513,0.00009550777,0.00002831982,0.0000457218,0.00004074314,0.9406269,0.009970721,0.0007410242,0.0003675881,0.0472041],"study_design_scores_gemma":[0.00001636663,0.00003091196,0.000323964,0.000005624496,0.00001001387,0.000007271984,0.000008227595,0.9957278,0.003486702,0.0001823134,0.0001946434,0.000006185311],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1905055,0.0004746434,0.8008746,0.0001168057,0.00005761659,0.0002198922,0.00008337837,0.001273443,0.006394107],"genre_scores_gemma":[0.7632148,0.0002584541,0.233344,0.00003859018,0.00001475159,0.0003595394,0.000171608,0.0001064878,0.002491857],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008807978,"threshold_uncertainty_score":0.01751345,"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."}}