{"id":"W2896709194","doi":"10.2351/1.5062940","title":"System identification and height control of laser cladding using adaptive neuro-fuzzy inference systems","year":2013,"lang":"en","type":"article","venue":"","topic":"Additive Manufacturing Materials and Processes","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Laurentian University; University of Waterloo","funders":"Government of Ontario","keywords":"Adaptive neuro fuzzy inference system; Control theory (sociology); Neuro-fuzzy; Controller (irrigation); Fuzzy control system; Computer science; Control system; Adaptive control; Artificial intelligence; Control engineering; Fuzzy logic; Engineering; Control (management)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004594305,0.0003759396,0.0004175241,0.0002414288,0.0004862104,0.0005416741,0.0004744713,0.0004609015,0.0006250922],"category_scores_gemma":[0.000823669,0.0002552786,0.0003624682,0.0002327678,0.0003086678,0.0003439154,0.0003874505,0.0004434454,0.0001393028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004740592,"about_ca_system_score_gemma":0.000629527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006645481,"about_ca_topic_score_gemma":0.006877271,"domain_scores_codex":[0.9998106,0.00003195537,0.00001468318,0.00004279112,0.00008192559,0.00001804286],"domain_scores_gemma":[0.9997113,0.0001308315,0.00004997262,0.00001890662,0.00008042072,0.000008507606],"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":[0.00012283,0.00006231373,0.0008500468,0.000174519,0.00003588047,0.0001481872,0.000222305,0.8411854,0.050859,0.00272436,0.0004514534,0.1031636],"study_design_scores_gemma":[0.000005239406,0.0000212169,0.0002706815,0.00000586587,0.000005297618,0.00001466675,0.00000807701,0.9946009,0.004458063,0.0003168766,0.0002875317,0.000005645161],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0909838,0.000411415,0.9040375,0.0001220672,0.00005313588,0.00008061573,0.00004811078,0.0006020398,0.003661317],"genre_scores_gemma":[0.9237652,0.0001737067,0.07402606,0.00002060412,0.0000123777,0.0001055302,0.00004956729,0.00001294523,0.001834182],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006645481,"threshold_uncertainty_score":0.01321357,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01486886557903495,"score_gpt":0.2040493678934693,"score_spread":0.1891805023144344,"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."}}