{"id":"W4386920594","doi":"10.1109/icaiss58487.2023.10250572","title":"Prediction of Flank Wear of Inconel by using the Levenberg-Marquardt ANN Model","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Inconel; Flank; Tool wear; Artificial neural network; Machining; Process (computing); Backpropagation; Computer science; Artificial intelligence; Engineering; Mechanical engineering; Machine learning; Materials science; Metallurgy","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":[0.00006961841,0.00005297051,0.00007807044,0.00003648517,0.0000261861,0.000003371725,0.00005600685,0.0000337971,0.000007869146],"category_scores_gemma":[0.00001316999,0.00004081664,0.00002008754,0.0002044588,0.00001599377,0.00009385061,0.00001692569,0.00004716248,0.000001081386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001091361,"about_ca_system_score_gemma":0.000009098705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000016211,"about_ca_topic_score_gemma":0.000002066253,"domain_scores_codex":[0.9996352,0.000004296041,0.0001448213,0.00005672377,0.0000820725,0.00007695097],"domain_scores_gemma":[0.9998133,0.00002122103,0.00003104799,0.00009185127,0.00003050217,0.00001213644],"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":[0.000002641862,0.000003344855,0.00007831085,0.00005450594,0.00001025732,4.096478e-8,0.0002371844,0.9849952,0.01329699,0.0001327292,0.0005407129,0.0006480846],"study_design_scores_gemma":[0.00009256394,0.000009435888,0.00003503945,0.00001692828,0.000009003788,3.483322e-7,0.0001270247,0.9900213,0.009198943,0.0003754749,0.00007862235,0.00003530456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09281389,0.0001009581,0.9053015,0.0000222545,0.00007718538,0.00006461827,0.00004327512,0.0001820856,0.001394218],"genre_scores_gemma":[0.9934582,0.0001420339,0.006109735,0.000006418198,0.00001379705,0.000002664688,0.00002194925,0.0000164725,0.0002287436],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9006443,"threshold_uncertainty_score":0.1664454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02963747242498566,"score_gpt":0.2404732150490689,"score_spread":0.2108357426240832,"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."}}