{"id":"W4220725518","doi":"10.1007/s42452-022-04987-0","title":"Teaching machines to optimizing machining parameters: using independent fuzzy logic controller and image data","year":2022,"lang":"en","type":"article","venue":"SN Applied Sciences","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Machining; Computer science; Fuzzy logic; Artificial neural network; Controller (irrigation); Control engineering; Industrial engineering; Mechanical engineering; Artificial intelligence; Engineering","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":[0.0004749471,0.000632058,0.0002935452,0.0002432646,0.00026835,0.0005426741,0.0007656483,0.0006353723,0.002629191],"category_scores_gemma":[0.001562721,0.0002382365,0.0002549542,0.0001855743,0.0003381722,0.0006490317,0.0004467129,0.0007869131,0.0003891176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004065202,"about_ca_system_score_gemma":0.0006505576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002931825,"about_ca_topic_score_gemma":0.003941318,"domain_scores_codex":[0.9998104,0.00002675313,0.00001029634,0.00005545995,0.00008158322,0.00001558841],"domain_scores_gemma":[0.9996452,0.0001682161,0.00003813793,0.00002800174,0.0001029233,0.00001742269],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001998549,0.0003704541,0.001272092,0.0003093162,0.00003938939,0.000108255,0.0003759833,0.1965542,0.0778162,0.008314833,0.002241999,0.7123975],"study_design_scores_gemma":[0.00002513613,0.0001266579,0.0005140473,0.00003045488,0.00002003801,0.00004323775,0.00003518008,0.9730334,0.02098707,0.002411108,0.002758683,0.00001494767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02580996,0.0001582103,0.9670982,0.0001430586,0.00004137622,0.0001091715,0.00001485298,0.0008962313,0.005728935],"genre_scores_gemma":[0.5726513,0.0002934568,0.4215015,0.0001786278,0.00004420158,0.0002067777,0.00004486589,0.00008159578,0.00499765],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002931825,"threshold_uncertainty_score":0.0087955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04387373409956748,"score_gpt":0.2988088521964413,"score_spread":0.2549351180968739,"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."}}