{"id":"W2299948121","doi":"10.4271/2016-01-0499","title":"Using Neural Networks to Examine the Sensitivity of Composite Material Mechanical Properties to Processing Parameters","year":2016,"lang":"en","type":"article","venue":"SAE International Journal of Materials and Manufacturing","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Composite number; Artificial neural network; Materials science; Sensitivity (control systems); Composite material; Materials processing; Computer science; Engineering; Process engineering; Artificial intelligence; Electronic engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001645177,0.001234308,0.0005175138,0.0009674061,0.0002527384,0.0008190047,0.0006197589,0.001272658,0.001002156],"category_scores_gemma":[0.007245254,0.0006062143,0.0006679269,0.0006094249,0.0004596317,0.0009133014,0.0004333832,0.0009690602,0.0001165067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001030937,"about_ca_system_score_gemma":0.0005110425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01404381,"about_ca_topic_score_gemma":0.0090428,"domain_scores_codex":[0.999558,0.000151437,0.00002870442,0.0001051626,0.00008620064,0.00007051168],"domain_scores_gemma":[0.9963415,0.00294691,0.0003285116,0.0001014304,0.0002457986,0.00003581838],"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":[0.0001916388,0.0001305154,0.004152122,0.00005105519,0.00009191889,0.00003468187,0.00002174411,0.9816598,0.004148629,0.0001797107,0.00004930764,0.009288895],"study_design_scores_gemma":[0.000003844695,0.00006402891,0.001551231,0.000002694627,0.00001164975,0.000006440391,0.000006197226,0.9964365,0.001757215,0.0001282022,0.00002503363,0.000006962588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9212588,0.0002857402,0.0756169,0.0001766934,0.00003093213,0.00009454452,0.0001765166,0.0003025424,0.002057289],"genre_scores_gemma":[0.9905608,0.00007342298,0.008560745,0.00002109649,0.000004917241,0.00005881181,0.0001192367,0.00001405238,0.0005868735],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01404381,"threshold_uncertainty_score":0.02792412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02089040918940083,"score_gpt":0.2466323832493497,"score_spread":0.2257419740599489,"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."}}