{"id":"W3158502284","doi":"10.18280/jesa.540213","title":"Using Artificial Neural Networks to Predict the Effect of Input Parameters on Weld Bead Geometry for SAW Process","year":2021,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Welding Techniques and Residual Stresses","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Mosul","keywords":"Welding; Artificial neural network; Process (computing); Submerged arc welding; Mechanical engineering; Voltage; Computer science; Arc welding; Engineering; Artificial intelligence; Electrical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003940356,0.0007079135,0.0003477855,0.0004566649,0.0001758296,0.0004940025,0.0004719648,0.0007554477,0.0007894232],"category_scores_gemma":[0.00108239,0.0003516781,0.0004761515,0.0003757775,0.0001684589,0.0006047962,0.0002146369,0.0005337267,0.0002231343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004156638,"about_ca_system_score_gemma":0.0004073895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0052049,"about_ca_topic_score_gemma":0.004794557,"domain_scores_codex":[0.999822,0.0000358431,0.00001375246,0.00004393859,0.00006668676,0.00001784258],"domain_scores_gemma":[0.9997001,0.0001549157,0.00004333262,0.00002003706,0.00007403577,0.000007639281],"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":[0.00008063192,0.00006186587,0.002576326,0.00007378949,0.00004494297,0.0000543699,0.00002336568,0.9493944,0.00752618,0.0003281239,0.0002216364,0.03961446],"study_design_scores_gemma":[0.000001842877,0.00002397524,0.0005093775,0.000003401999,0.000006461249,0.000004615845,0.000002780202,0.9975055,0.001687814,0.000145178,0.0001056237,0.000003468244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.268307,0.0008894442,0.7257249,0.0001750695,0.00008862458,0.00007441261,0.0002349669,0.00124192,0.003263585],"genre_scores_gemma":[0.9452526,0.0003928678,0.05159041,0.00003910021,0.000012978,0.000106368,0.0002938493,0.0000314286,0.002280515],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0052049,"threshold_uncertainty_score":0.01034915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02475047060734653,"score_gpt":0.281825895944562,"score_spread":0.2570754253372154,"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."}}