{"id":"W3118692280","doi":"10.1007/s42452-020-03980-9","title":"Neuro-fuzzy based predictive model for cutting force in CNC turning process of Al–Si–Cu cast alloy using modifier elements","year":2021,"lang":"en","type":"article","venue":"SN Applied Sciences","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Adaptive neuro fuzzy inference system; Machining; Alloy; Materials science; Mechanical engineering; Process (computing); Fuzzy logic; Metallurgy; Control theory (sociology); Computer science; Fuzzy control system; Engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000274009,0.0001383664,0.0001776569,0.0001169748,0.0001556372,0.00004200176,0.0001701,0.00004261722,0.000003269964],"category_scores_gemma":[0.00008755174,0.0001412657,0.00002829724,0.000597202,0.00006530863,0.0002511416,0.00003188549,0.0001082748,1.672606e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003801927,"about_ca_system_score_gemma":0.0001309736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003573354,"about_ca_topic_score_gemma":0.00001174546,"domain_scores_codex":[0.9988137,0.000007102188,0.0002990126,0.0003200181,0.0002503282,0.0003097979],"domain_scores_gemma":[0.999588,0.00008863619,0.0001042489,0.00009119688,0.0000876387,0.00004030251],"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":[0.00001199577,0.00001444249,0.0002981219,0.0001376639,0.000004145871,5.980656e-7,0.0007218252,0.9687951,0.02892419,0.0003701979,0.000003214761,0.0007185544],"study_design_scores_gemma":[0.0003645726,0.0000187278,0.00001841044,0.00007018834,0.000009995417,8.787189e-7,0.0006490078,0.9769724,0.01997305,0.001777991,0.000006601039,0.0001381679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2137277,0.00006018801,0.7839811,0.00001937655,0.0000695241,0.0002498194,0.00001416689,0.00006211052,0.00181604],"genre_scores_gemma":[0.8853653,0.000005909926,0.1144335,0.00008782739,0.00001607002,0.00004904482,0.00001124944,0.00002040366,0.00001072687],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6716377,"threshold_uncertainty_score":0.5760648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02650561579428809,"score_gpt":0.2902894766229087,"score_spread":0.2637838608286206,"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."}}